\u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n
\u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n
As AI advances, she points out; there needs to be in place social and political structures in place that allow society to participate in defining and determining the future of AI. This way, such powerful tools will be developed to benefit humanity and not just a few. Such a forum will also ensure that the limits of AI applications are set in preference of society. As such, AI applications will not be developed and deployed in an abrupt manner that would shatter society through job losses and autonomous AI.<\/p>\n\n\n\n
\u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n
\u201cSocial impact is always an issue when it comes to technology,\u201d says Dr. Ackerman. She explains that some of the issues that AI is raising have to do with social issues like job security, warfare and other sectors of society. \u201cAI must be approached from a humanistic perspective, with emphasis on what implications the applications have on society and humanity,\u201d she continues. She points out that while technology can have multiple applications, it should be focused on providing humans with greater freedom, empowerment, and capabilities, things that will not detract from humanity but enhance humanity\u2019s options.<\/p>\n\n\n\n
As AI advances, she points out; there needs to be in place social and political structures in place that allow society to participate in defining and determining the future of AI. This way, such powerful tools will be developed to benefit humanity and not just a few. Such a forum will also ensure that the limits of AI applications are set in preference of society. As such, AI applications will not be developed and deployed in an abrupt manner that would shatter society through job losses and autonomous AI.<\/p>\n\n\n\n
\u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n
\u201cSocial impact is always an issue when it comes to technology,\u201d says Dr. Ackerman. She explains that some of the issues that AI is raising have to do with social issues like job security, warfare and other sectors of society. \u201cAI must be approached from a humanistic perspective, with emphasis on what implications the applications have on society and humanity,\u201d she continues. She points out that while technology can have multiple applications, it should be focused on providing humans with greater freedom, empowerment, and capabilities, things that will not detract from humanity but enhance humanity\u2019s options.<\/p>\n\n\n\n
As AI advances, she points out; there needs to be in place social and political structures in place that allow society to participate in defining and determining the future of AI. This way, such powerful tools will be developed to benefit humanity and not just a few. Such a forum will also ensure that the limits of AI applications are set in preference of society. As such, AI applications will not be developed and deployed in an abrupt manner that would shatter society through job losses and autonomous AI.<\/p>\n\n\n\n
\u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n
\u201cMachines are extremely good at creating options,\u201d Dr. Ackerman says. However, she says that what machines are not very good at is choosing, something that humans are very good at doing. If you ask anyone to pick between two paintings, they will be able to do so, no matter whether they can create similar paintings or not. What she sees from this bifurcation of skills is an opportunity to collaborate in the creative process. With AI providing options and humans acting as a filter making choices, there can exist a symbiotic relationship between AI and humans, allowing humans to create ever faster, more accurately and more creatively.<\/p>\n\n\n\n
\u201cSocial impact is always an issue when it comes to technology,\u201d says Dr. Ackerman. She explains that some of the issues that AI is raising have to do with social issues like job security, warfare and other sectors of society. \u201cAI must be approached from a humanistic perspective, with emphasis on what implications the applications have on society and humanity,\u201d she continues. She points out that while technology can have multiple applications, it should be focused on providing humans with greater freedom, empowerment, and capabilities, things that will not detract from humanity but enhance humanity\u2019s options.<\/p>\n\n\n\n
As AI advances, she points out; there needs to be in place social and political structures in place that allow society to participate in defining and determining the future of AI. This way, such powerful tools will be developed to benefit humanity and not just a few. Such a forum will also ensure that the limits of AI applications are set in preference of society. As such, AI applications will not be developed and deployed in an abrupt manner that would shatter society through job losses and autonomous AI.<\/p>\n\n\n\n
\u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n
In Japan, there is a cultural trend that is catching on where musical concerts have hologram performers and machine-synthesized songs. While there are humans behind these events, the entire performance is conducted without any humans in sight. \u201cWhile computers can be taught to simulate emotion, they cannot spontaneously create genuine emotions,\u201d Dr. Ackerman says. \u201cThis is an important aspect about AI in that it cannot replace the connections that humans have with each other.\u201d While a time will come when such performances may pass the Turing Test, she says, the human factor will still be the main thing that differentiates humans from machines.<\/p>\n\n\n\n
\u201cMachines are extremely good at creating options,\u201d Dr. Ackerman says. However, she says that what machines are not very good at is choosing, something that humans are very good at doing. If you ask anyone to pick between two paintings, they will be able to do so, no matter whether they can create similar paintings or not. What she sees from this bifurcation of skills is an opportunity to collaborate in the creative process. With AI providing options and humans acting as a filter making choices, there can exist a symbiotic relationship between AI and humans, allowing humans to create ever faster, more accurately and more creatively.<\/p>\n\n\n\n
\u201cSocial impact is always an issue when it comes to technology,\u201d says Dr. Ackerman. She explains that some of the issues that AI is raising have to do with social issues like job security, warfare and other sectors of society. \u201cAI must be approached from a humanistic perspective, with emphasis on what implications the applications have on society and humanity,\u201d she continues. She points out that while technology can have multiple applications, it should be focused on providing humans with greater freedom, empowerment, and capabilities, things that will not detract from humanity but enhance humanity\u2019s options.<\/p>\n\n\n\n
As AI advances, she points out; there needs to be in place social and political structures in place that allow society to participate in defining and determining the future of AI. This way, such powerful tools will be developed to benefit humanity and not just a few. Such a forum will also ensure that the limits of AI applications are set in preference of society. As such, AI applications will not be developed and deployed in an abrupt manner that would shatter society through job losses and autonomous AI.<\/p>\n\n\n\n
\u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n
In Japan, there is a cultural trend that is catching on where musical concerts have hologram performers and machine-synthesized songs. While there are humans behind these events, the entire performance is conducted without any humans in sight. \u201cWhile computers can be taught to simulate emotion, they cannot spontaneously create genuine emotions,\u201d Dr. Ackerman says. \u201cThis is an important aspect about AI in that it cannot replace the connections that humans have with each other.\u201d While a time will come when such performances may pass the Turing Test, she says, the human factor will still be the main thing that differentiates humans from machines.<\/p>\n\n\n\n
\u201cMachines are extremely good at creating options,\u201d Dr. Ackerman says. However, she says that what machines are not very good at is choosing, something that humans are very good at doing. If you ask anyone to pick between two paintings, they will be able to do so, no matter whether they can create similar paintings or not. What she sees from this bifurcation of skills is an opportunity to collaborate in the creative process. With AI providing options and humans acting as a filter making choices, there can exist a symbiotic relationship between AI and humans, allowing humans to create ever faster, more accurately and more creatively.<\/p>\n\n\n\n
\u201cSocial impact is always an issue when it comes to technology,\u201d says Dr. Ackerman. She explains that some of the issues that AI is raising have to do with social issues like job security, warfare and other sectors of society. \u201cAI must be approached from a humanistic perspective, with emphasis on what implications the applications have on society and humanity,\u201d she continues. She points out that while technology can have multiple applications, it should be focused on providing humans with greater freedom, empowerment, and capabilities, things that will not detract from humanity but enhance humanity\u2019s options.<\/p>\n\n\n\n
As AI advances, she points out; there needs to be in place social and political structures in place that allow society to participate in defining and determining the future of AI. This way, such powerful tools will be developed to benefit humanity and not just a few. Such a forum will also ensure that the limits of AI applications are set in preference of society. As such, AI applications will not be developed and deployed in an abrupt manner that would shatter society through job losses and autonomous AI.<\/p>\n\n\n\n
\u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n
\u201cThe definition of intelligence is closely tied to what makes us human, hence the changes over time,\u201d suggests Dr. Ackerman. She points out that when the definition of human intelligence begins to become conflated with that of machine intelligence, it affects the very essence of who we are as humans, a situation that creates a sense of anxiety around AI. However, referring to ALYSIA, Dr. Ackerman points out that AI\u2019s real utility is as a tool to make humans more intelligent, more powerful and able to achieve more. She clarifies that her AI platform is not built to replace human intelligence, but augment and enhance it.<\/p>\n\n\n\n
In Japan, there is a cultural trend that is catching on where musical concerts have hologram performers and machine-synthesized songs. While there are humans behind these events, the entire performance is conducted without any humans in sight. \u201cWhile computers can be taught to simulate emotion, they cannot spontaneously create genuine emotions,\u201d Dr. Ackerman says. \u201cThis is an important aspect about AI in that it cannot replace the connections that humans have with each other.\u201d While a time will come when such performances may pass the Turing Test, she says, the human factor will still be the main thing that differentiates humans from machines.<\/p>\n\n\n\n
\u201cMachines are extremely good at creating options,\u201d Dr. Ackerman says. However, she says that what machines are not very good at is choosing, something that humans are very good at doing. If you ask anyone to pick between two paintings, they will be able to do so, no matter whether they can create similar paintings or not. What she sees from this bifurcation of skills is an opportunity to collaborate in the creative process. With AI providing options and humans acting as a filter making choices, there can exist a symbiotic relationship between AI and humans, allowing humans to create ever faster, more accurately and more creatively.<\/p>\n\n\n\n
\u201cSocial impact is always an issue when it comes to technology,\u201d says Dr. Ackerman. She explains that some of the issues that AI is raising have to do with social issues like job security, warfare and other sectors of society. \u201cAI must be approached from a humanistic perspective, with emphasis on what implications the applications have on society and humanity,\u201d she continues. She points out that while technology can have multiple applications, it should be focused on providing humans with greater freedom, empowerment, and capabilities, things that will not detract from humanity but enhance humanity\u2019s options.<\/p>\n\n\n\n
As AI advances, she points out; there needs to be in place social and political structures in place that allow society to participate in defining and determining the future of AI. This way, such powerful tools will be developed to benefit humanity and not just a few. Such a forum will also ensure that the limits of AI applications are set in preference of society. As such, AI applications will not be developed and deployed in an abrupt manner that would shatter society through job losses and autonomous AI.<\/p>\n\n\n\n
\u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n
\u201cThe definition of intelligence has evolved,\u201d Dr. Ackerman says. \u201cIn the past, intelligence was characterized by things like speed and accuracy, two things that machines excel at,\u201d she says, \u201cbut that definition has changed to refer to an ability to tackle higher-order problem solving and creativity.\u201d This definition is crucial when it comes to defining what AI can and cannot do. For instance, machines are extremely competent at doing repetitive tasks, something that may not be considered as a form of intelligence. However, at the same time, through such repetitive tasks, AI can be trained to create at a level well beyond that of human capabilities.<\/p>\n\n\n\n
\u201cThe definition of intelligence is closely tied to what makes us human, hence the changes over time,\u201d suggests Dr. Ackerman. She points out that when the definition of human intelligence begins to become conflated with that of machine intelligence, it affects the very essence of who we are as humans, a situation that creates a sense of anxiety around AI. However, referring to ALYSIA, Dr. Ackerman points out that AI\u2019s real utility is as a tool to make humans more intelligent, more powerful and able to achieve more. She clarifies that her AI platform is not built to replace human intelligence, but augment and enhance it.<\/p>\n\n\n\n
In Japan, there is a cultural trend that is catching on where musical concerts have hologram performers and machine-synthesized songs. While there are humans behind these events, the entire performance is conducted without any humans in sight. \u201cWhile computers can be taught to simulate emotion, they cannot spontaneously create genuine emotions,\u201d Dr. Ackerman says. \u201cThis is an important aspect about AI in that it cannot replace the connections that humans have with each other.\u201d While a time will come when such performances may pass the Turing Test, she says, the human factor will still be the main thing that differentiates humans from machines.<\/p>\n\n\n\n
\u201cMachines are extremely good at creating options,\u201d Dr. Ackerman says. However, she says that what machines are not very good at is choosing, something that humans are very good at doing. If you ask anyone to pick between two paintings, they will be able to do so, no matter whether they can create similar paintings or not. What she sees from this bifurcation of skills is an opportunity to collaborate in the creative process. With AI providing options and humans acting as a filter making choices, there can exist a symbiotic relationship between AI and humans, allowing humans to create ever faster, more accurately and more creatively.<\/p>\n\n\n\n
\u201cSocial impact is always an issue when it comes to technology,\u201d says Dr. Ackerman. She explains that some of the issues that AI is raising have to do with social issues like job security, warfare and other sectors of society. \u201cAI must be approached from a humanistic perspective, with emphasis on what implications the applications have on society and humanity,\u201d she continues. She points out that while technology can have multiple applications, it should be focused on providing humans with greater freedom, empowerment, and capabilities, things that will not detract from humanity but enhance humanity\u2019s options.<\/p>\n\n\n\n
As AI advances, she points out; there needs to be in place social and political structures in place that allow society to participate in defining and determining the future of AI. This way, such powerful tools will be developed to benefit humanity and not just a few. Such a forum will also ensure that the limits of AI applications are set in preference of society. As such, AI applications will not be developed and deployed in an abrupt manner that would shatter society through job losses and autonomous AI.<\/p>\n\n\n\n
\u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n
\u201cThe definition of intelligence has evolved,\u201d Dr. Ackerman says. \u201cIn the past, intelligence was characterized by things like speed and accuracy, two things that machines excel at,\u201d she says, \u201cbut that definition has changed to refer to an ability to tackle higher-order problem solving and creativity.\u201d This definition is crucial when it comes to defining what AI can and cannot do. For instance, machines are extremely competent at doing repetitive tasks, something that may not be considered as a form of intelligence. However, at the same time, through such repetitive tasks, AI can be trained to create at a level well beyond that of human capabilities.<\/p>\n\n\n\n
\u201cThe definition of intelligence is closely tied to what makes us human, hence the changes over time,\u201d suggests Dr. Ackerman. She points out that when the definition of human intelligence begins to become conflated with that of machine intelligence, it affects the very essence of who we are as humans, a situation that creates a sense of anxiety around AI. However, referring to ALYSIA, Dr. Ackerman points out that AI\u2019s real utility is as a tool to make humans more intelligent, more powerful and able to achieve more. She clarifies that her AI platform is not built to replace human intelligence, but augment and enhance it.<\/p>\n\n\n\n
In Japan, there is a cultural trend that is catching on where musical concerts have hologram performers and machine-synthesized songs. While there are humans behind these events, the entire performance is conducted without any humans in sight. \u201cWhile computers can be taught to simulate emotion, they cannot spontaneously create genuine emotions,\u201d Dr. Ackerman says. \u201cThis is an important aspect about AI in that it cannot replace the connections that humans have with each other.\u201d While a time will come when such performances may pass the Turing Test, she says, the human factor will still be the main thing that differentiates humans from machines.<\/p>\n\n\n\n
\u201cMachines are extremely good at creating options,\u201d Dr. Ackerman says. However, she says that what machines are not very good at is choosing, something that humans are very good at doing. If you ask anyone to pick between two paintings, they will be able to do so, no matter whether they can create similar paintings or not. What she sees from this bifurcation of skills is an opportunity to collaborate in the creative process. With AI providing options and humans acting as a filter making choices, there can exist a symbiotic relationship between AI and humans, allowing humans to create ever faster, more accurately and more creatively.<\/p>\n\n\n\n
\u201cSocial impact is always an issue when it comes to technology,\u201d says Dr. Ackerman. She explains that some of the issues that AI is raising have to do with social issues like job security, warfare and other sectors of society. \u201cAI must be approached from a humanistic perspective, with emphasis on what implications the applications have on society and humanity,\u201d she continues. She points out that while technology can have multiple applications, it should be focused on providing humans with greater freedom, empowerment, and capabilities, things that will not detract from humanity but enhance humanity\u2019s options.<\/p>\n\n\n\n
As AI advances, she points out; there needs to be in place social and political structures in place that allow society to participate in defining and determining the future of AI. This way, such powerful tools will be developed to benefit humanity and not just a few. Such a forum will also ensure that the limits of AI applications are set in preference of society. As such, AI applications will not be developed and deployed in an abrupt manner that would shatter society through job losses and autonomous AI.<\/p>\n\n\n\n
\u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n
Artificial Intelligence or AI is a powerful technology that potentially has the power to create machines that can replace humans. This perception is the basis of the dread with which some consider AI and the fabled coming rise of the machines. However, AI, like any other technology, is a tool, says Dr. Maya Ackerman, founder, and CEO of AI startup WaveAI. She and her team are behind the breakthrough songwriting AI platform ALYSIA<\/a>, a platform that, in her words, can cut down songwriting from hours to just minutes. We recently caught up with Dr. Ackerman to discuss the future of AI in a world that values unique human characteristics.<\/p>\n\n\n\n \u201cThe definition of intelligence has evolved,\u201d Dr. Ackerman says. \u201cIn the past, intelligence was characterized by things like speed and accuracy, two things that machines excel at,\u201d she says, \u201cbut that definition has changed to refer to an ability to tackle higher-order problem solving and creativity.\u201d This definition is crucial when it comes to defining what AI can and cannot do. For instance, machines are extremely competent at doing repetitive tasks, something that may not be considered as a form of intelligence. However, at the same time, through such repetitive tasks, AI can be trained to create at a level well beyond that of human capabilities.<\/p>\n\n\n\n \u201cThe definition of intelligence is closely tied to what makes us human, hence the changes over time,\u201d suggests Dr. Ackerman. She points out that when the definition of human intelligence begins to become conflated with that of machine intelligence, it affects the very essence of who we are as humans, a situation that creates a sense of anxiety around AI. However, referring to ALYSIA, Dr. Ackerman points out that AI\u2019s real utility is as a tool to make humans more intelligent, more powerful and able to achieve more. She clarifies that her AI platform is not built to replace human intelligence, but augment and enhance it.<\/p>\n\n\n\n In Japan, there is a cultural trend that is catching on where musical concerts have hologram performers and machine-synthesized songs. While there are humans behind these events, the entire performance is conducted without any humans in sight. \u201cWhile computers can be taught to simulate emotion, they cannot spontaneously create genuine emotions,\u201d Dr. Ackerman says. \u201cThis is an important aspect about AI in that it cannot replace the connections that humans have with each other.\u201d While a time will come when such performances may pass the Turing Test, she says, the human factor will still be the main thing that differentiates humans from machines.<\/p>\n\n\n\n \u201cMachines are extremely good at creating options,\u201d Dr. Ackerman says. However, she says that what machines are not very good at is choosing, something that humans are very good at doing. If you ask anyone to pick between two paintings, they will be able to do so, no matter whether they can create similar paintings or not. What she sees from this bifurcation of skills is an opportunity to collaborate in the creative process. With AI providing options and humans acting as a filter making choices, there can exist a symbiotic relationship between AI and humans, allowing humans to create ever faster, more accurately and more creatively.<\/p>\n\n\n\n \u201cSocial impact is always an issue when it comes to technology,\u201d says Dr. Ackerman. She explains that some of the issues that AI is raising have to do with social issues like job security, warfare and other sectors of society. \u201cAI must be approached from a humanistic perspective, with emphasis on what implications the applications have on society and humanity,\u201d she continues. She points out that while technology can have multiple applications, it should be focused on providing humans with greater freedom, empowerment, and capabilities, things that will not detract from humanity but enhance humanity\u2019s options.<\/p>\n\n\n\n As AI advances, she points out; there needs to be in place social and political structures in place that allow society to participate in defining and determining the future of AI. This way, such powerful tools will be developed to benefit humanity and not just a few. Such a forum will also ensure that the limits of AI applications are set in preference of society. As such, AI applications will not be developed and deployed in an abrupt manner that would shatter society through job losses and autonomous AI.<\/p>\n\n\n\n \u201cALYSIA does not replace songwriters, it just makes them better at what they do,\u201d says Dr. Ackerman. This statement points to what she feels is the real power of AI; the ability to empower people to do more. She compares the future of AI-assisted songwriting to the rise in consumer photography via smartphones. In the same way smartphone cameras made everyone an amateur photographer, so will ALYSIA make everyone an amateur songwriter. This alludes to a future where people will be skilled at operating AI that performs tasks that the operators may not be good at. \u201cRoles may change as technology progresses,\u201d says Dr. Ackerman, \u201cbut computers will not overtake us as humans. Instead, they will only get more useful to us.\u201d<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\n Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":628,"post_author":"1","post_date":"2018-10-17 14:40:00","post_date_gmt":"2018-10-17 21:40:00","post_content":"\n In 2017, Facebook triumphantly announced that over 100,000 chatbots were available on the Facebook Messenger platform. Focusing on rudimentary, structured queries, these chatbots failed to deliver on the promise of intelligent Star Trek-type intelligent conversational bots. It seems the journey to true conversational AI had undergone a false start. Today, the quest for truly conversational AI is one that tackles deeper challenges than just understanding what the input is and predicting a possible answer. This associative approach to conversational AI is but the tip of the iceberg.<\/p>\n\n\n\n Kunal Contractor is global director at Avaamo, a company that is building the next generation of conversational AI applications for the enterprise. The company, working with some the of the largest companies in the world, is attempting to crack the conversational AI conundrum, one that will unlock the power of conversational AI to drive down costs and increase customer and employee engagement. We recently sat down with Kunal to discuss what the vision of conversational AI is for the enterprise and what challenges enterprises will need to surmount to achieve revolutionary digital transformation.<\/p>\n\n\n\n Gartner predicts<\/a> that by 2020 customers will manage 85% of their relationship with the enterprise without interacting with a human. This prediction is predicated on the rapid rise in conversational AI driven by advances in deep learning, big data and predictive analytics. However, Kunal explains, to truly deliver what can be called the promise of conversational AI, fundamentally new technology must be built to perform multi-turn conversations and execute judgment-intensive tasks, just like humans. What Kunal is referring to as multi-turn conversations are interactions injected with slang, insinuations, references to past conversations, colloquialisms and other language factors that current chatbots cannot handle.<\/p>\n\n\n\n However, when implemented correctly, conversational AI can quickly supplant human interactions. Consider how difficult it is to ask a fellow human a certain question but then ask Google to search the same question with no reservations. The belief that robots are not judgmental will be a huge factor that drives the successful adoption of conversational AI in the enterprise. Other factors that will potentially drive the uptake of conversational AI will be the instantaneous access to data AIs have resulting in instant answers to complex questions, the belief that AIs do not lie, as well as access to the customer\u2019s entire history, not just of purchases but conversations as well. The potential for deep context and unprecedented customer engagement serves as an incentive for enterprises to pay greater attention to conversational AI.<\/p>\n\n\n\n To demonstrate the potential conversational AI has to impact enterprise employees, Kunal offers two illustrations. In the first illustration, an investment bank employee with 20 years of experience needs to confirm a detail to a customer. He can either dig into the system to surface that information, which may take long or ask a junior associate for the information. To avoid embarrassment, the employee will opt to put the customer on hold and search for the information. In the second illustration, Kunal talks of a large enterprise organization that receives over 15,000 password reset requests from employees each month. It takes each employee around 20 minutes to get their password reset resulting in the loss of a staggering 5,000 work hours each month.<\/p>\n\n\n\n In both instances, it is possible to see the cost implications of the actions taken by employees to remedy the situation at hand. Functional conversational AI can remedy these situations. In the first instance, the employee seeking information can ask the conversational AI assistant for the information, both avoiding embarrassment and cutting the time it takes to respond to the customer. In the second instance, Kunal says that with conversational AI, it is possible to cut down the password reset time per employee to under 27 seconds, saving the organization close to 98% of the time employees previously spent resetting their passwords. It\u2019s clear from this illustration why Juniper Research forecasts<\/a> that chatbots and other conversational AI will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million in 2017.<\/p>\n\n\n\n Conversational computing is a rapidly evolving technology, and it does promise to change the way that we work and how a lot of business would interact with their customers, with their employees, stakeholders, and with a massive capability of impacting both the customer experience and reducing cost at the same time, says Kunal. He calls this application last-mile automation. This last mile, however, represents a frontier that is both pregnant with potential yet fraught with challenges. As it stands, Natural Language Processing (NLP), what current conversational AIs use, is the easier part. What is more difficult to achieve is Natural Language Understanding (NLU), a state that will make artificial AIs able to respond to more complex multi-turn inputs.<\/p>\n\n\n\n Current AI technologies, while adept at probabilistic computing, falter when it comes to causal computing<\/a>. For instance, a banking AI can understand a bank transfer command based on similar inputs but may not understand a question that requires causative reasoning. That is, if a customer asks, \u201cHow can I improve my credit rating?\u201d Such a question must reach far beyond simply regurgitating standard credit rating answers to delve into the customer\u2019s financial history, purchasing trends, earning trends, and other data that require more than just an X=Y, therefore, Y=X approach to answer in a meaningful manner.<\/p>\n\n\n\n Organizations on the quest for digital transformation cannot afford to ignore conversational AI. Gartner predicts that by 2019, 20 percent of brands will abandon their mobile apps<\/a> in favor of building services on top of other existing platforms like Facebook Messenger, WeChat and WhatsApp. Gartner also predicts that AI will be the foundational technology<\/a> that drives the next wave of these enterprise applications. While in the past the enterprise technology conversation was framed around having a website and mobile apps, says Kunal, today, we are in the world of an AI-first strategy. He is quick to point out that from history, any and every early adopter to get technology always gets to the top. Enterprises will, therefore, do well to start the journey towards integrating conversational AI into both their customer facing and employee facing interaction infrastructure if they are to survive the 4th industrial age, or as Kunal puts it, Industry 4.0.<\/p>\n\n\n\nIntelligence<\/h2>\n\n\n\n
The Human Factor<\/h2>\n\n\n\n
Social Structures<\/h2>\n\n\n\n
Conclusion<\/h2>\n\n\n\n
VIDEO: Full Dr. Maya Ackerman Interview<\/h2>\n\n\n\n
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Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
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Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
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WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
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Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
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WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
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Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
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WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
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Waymo - Autonomous Cars<\/h3>\n\n\n\n
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WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
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Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
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Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n
Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Customer-facing Conversational AI<\/h2>\n\n\n\n
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