Today, on How AI Happens, we are joined by the Chief Digital Officer at Allied Digital, Utpal Chakraborty, to talk all things AI at Allied Digital. You’ll hear about Utpal’s AI background, how he defines Allied Digital’s mission, and what Smart Cities are and how the company captures data to achieve them, as well as why AI learning is the right approach for Smart Cities. We also discuss what success looks like to Utpal and the importance of designing something seamless for the end-user. To find out why customer success is Allied Digital’s success, tune in today! Key Points From This Episode:A brief overview of Utpal’s background and how he ended up in his current role at Allied. How Utpal would characterize Allied Digital’s mission. The definition of Smart Cities.How Allied Digital is able to capture the data needed to make a city a Smart City. What made it clear to Utpal that AI machine learning was the right approach for the Smart City services.Insight into what success and an end goal looks like for Utpal. Why it is everyone’s job to design something that is seamless for the end-user. A look at what Utpal thinks has been truly disruptive in the AI space. Tweetables:“I looked at how we can move this [Smart City] tool ahead and that’s where the AI machine learning came into the picture.” — @utpal_bob [0:11:11]“[Allied Digital] wants to bring that wow factor into each and every service product and solution that we provide to our customers and, in turn, that they provide to the industry.” — @utpal_bob [0:16:27]Links Mentioned in Today’s Episode:Utpal Chakraborty on LinkedInUtpal Chakraborty on TwitterAllied Digital ServicesSama

Today, on How AI Happens, we are joined by the Chief Digital Officer at Allied Digital, Utpal Chakraborty, to talk all things AI at Allied Digital. You’ll hear about Utpal’s AI background, how he defines Allied Digital’s mission, and what Smart Cities are and how the company captures data to achieve them, as well as why AI learning is the right approach for Smart Cities. We also discuss what success looks like to Utpal and the importance of designing something seamless for the end-user. To find out why customer success is Allied Digital’s success, tune in today! Key Points From This Episode:<ul><li>A brief overview of Utpal’s background and how he ended up in his current role at Allied. </li><li>How Utpal would characterize Allied Digital’s mission. </li><li>The definition of Smart Cities.</li><li>How Allied Digital is able to capture the data needed to make a city a Smart City. </li><li>What made it clear to Utpal that AI machine learning was the right approach for the Smart City services.</li><li>Insight into what success and an end goal looks like for Utpal. </li><li>Why it is everyone’s job to design something that is seamless for the end-user. </li><li>A look at what Utpal thinks has been truly disruptive in the AI space. </li></ul>Tweetables:“I looked at how we can move this [Smart City] tool ahead and that’s where the AI machine learning came into the picture.” — <a href="https://twitter.com/utpal_bob" rel="nofollow" target="_blank">@utpal_bob</a> [0:11:11]“[Allied Digital] wants to bring that wow factor into each and every service product and solution that we provide to our customers and, in turn, that they provide to the industry.” — <a href="https://twitter.com/utpal_bob" rel="nofollow" target="_blank">@utpal_bob</a> [0:16:27]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/utpal-chakraborty/" rel="nofollow" target="_blank">Utpal Chakraborty on LinkedIn</a><a href="https://twitter.com/utpal_bob" rel="nofollow" target="_blank">Utpal Chakraborty on Twitter</a><a href="http://www.allieddigital.net/" rel="nofollow" target="_blank">Allied Digital Services</a><a href="https://www.sama.com/" rel="nofollow" target="_blank">Sama</a>

Allied Digital CDO Utpal Chakraborty

Kevin Dunlap

Jason Schoettler

 In today’s conversation, we learn about Jason and Kevin’s career backgrounds, the potential that the deep technology sector has, what ideas excite them the most, the challenges when investing in AI-based companies, what kind of technology is easily understood by the consumer, what makes a technological innovation successful, and much more. Key Points From This Episode:Background and professional paths that led Jason and Kevin to their current roles.Reasons behind starting Calibrate Ventures and originally entering the sector.How the deep-technology sector can solve current problems.What kind of new technology and innovation they get most excited about.The most essential quality of innovative technology: what people want.Rundown of the diverse, experienced, and talented team they work with.Jason shares an example of a technological innovation that solved a real-world problem.How they differentiate the approach when investing in AI companies.What influences the longer sale cycles in the AI technology sector.An example of the challenges when integrating AI technology with the business side.The benefits that Jason and Kevin’s experience adds to the company.Some examples of the kind of technology that translates well to the consumer.We find out what their opinion is about automation and augmentation.We wrap up the show with some advice from Jason and Kevin for AI entrepreneurs. Tweetables:“I think for me personally, the cycle-time was very long. You work on projects for a very long time. As an investor, I get to see new ideas and new concepts every day. From an intellectual curiosity standpoint, there couldn’t be a better job.” — Kevin Dunlap [0:05:17]“So that lights me up. When I hear somebody talk about a problem that they are looking to solve and how their technology can do it uniquely with some type of competitive or differentiated advantage we think is sustainable.” — Jason Schoettler [0:08:14]“The things that really excite us are not, where can we do better than humans but first, where are there not humans work right now where we need humans doing work.” — Jason Schoettler [0:20:44]“Anytime that someone is doing a job that is dangerous, that is able to be solved with technology, I think we owe it to ourselves to do that.” — Kevin Dunlap [0:22:39]Links Mentioned in Today’s Episode:Jason Schoettler on LinkedInKevin Dunlap on LinkedInCalibrate VenturesCalibrate Ventures on LinkedInGrayMatter RoboticsGrayMatter Robotics on LinkedIn

In today’s conversation, we learn about Jason and Kevin’s career backgrounds, the potential that the deep technology sector has, what ideas excite them the most, the challenges when investing in AI-based companies, what kind of technology is easily understood by the consumer, what makes a technological innovation successful, and much more. Key Points From This Episode:<ul><li>Background and professional paths that led Jason and Kevin to their current roles.</li><li>Reasons behind starting Calibrate Ventures and originally entering the sector.</li><li>How the deep-technology sector can solve current problems.</li><li>What kind of new technology and innovation they get most excited about.</li><li>The most essential quality of innovative technology: what people want.</li><li>Rundown of the diverse, experienced, and talented team they work with.</li><li>Jason shares an example of a technological innovation that solved a real-world problem.</li><li>How they differentiate the approach when investing in AI companies.</li><li>What influences the longer sale cycles in the AI technology sector.</li><li>An example of the challenges when integrating AI technology with the business side.</li><li>The benefits that Jason and Kevin’s experience adds to the company.</li><li>Some examples of the kind of technology that translates well to the consumer.</li><li>We find out what their opinion is about automation and augmentation.</li><li>We wrap up the show with some advice from Jason and Kevin for AI entrepreneurs. </li></ul>Tweetables:“I think for me personally, the cycle-time was very long. You work on projects for a very long time. As an investor, I get to see new ideas and new concepts every day. From an intellectual curiosity standpoint, there couldn’t be a better job.” — Kevin Dunlap [0:05:17]“So that lights me up. When I hear somebody talk about a problem that they are looking to solve and how their technology can do it uniquely with some type of competitive or differentiated advantage we think is sustainable.” — Jason Schoettler [0:08:14]“The things that really excite us are not, where can we do better than humans but first, where are there not humans work right now where we need humans doing work.” — Jason Schoettler [0:20:44]“Anytime that someone is doing a job that is dangerous, that is able to be solved with technology, I think we owe it to ourselves to do that.” — Kevin Dunlap [0:22:39]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/jason-schoettler-5a81122/" rel="nofollow" target="_blank">Jason Schoettler on LinkedIn</a><a href="https://www.linkedin.com/in/kevindunlap/" rel="nofollow" target="_blank">Kevin Dunlap on LinkedIn</a><a href="https://www.calibratevc.com" rel="nofollow" target="_blank">Calibrate Ventures</a><a href="https://www.linkedin.com/company/calibrate-ventures/" rel="nofollow" target="_blank">Calibrate Ventures on LinkedIn</a><a href="https://graymatter-robotics.com" rel="nofollow" target="_blank">GrayMatter Robotics</a><a href="https://www.linkedin.com/company/graymatter-robotics/" rel="nofollow" target="_blank">GrayMatter Robotics on LinkedIn</a>

Calibrate Ventures Partners Jason Schoettler, Kevin Dunlap

Dennis Mortensen

Whether you’re building AI for self-driving cars or for scheduling meetings, it’s all about prediction! In this episode, we’re going to explore the complexity of teaching the human power of prediction to machines.Key Points From This Episode:Dennis shares an overview of what he has spent his career focusing on.How Dennis defines an intelligent agent.The role of prediction in the AI space.Dennis explains the mission that drove his most recent entrepreneurial venture, x.ai (acquired by Bizzabo).Challenges of transferring humans’ capacity for prediction and empathy to machines.Some of Dennis’s key learnings from his time working on the technology for x.ai.Unrealistic expectations that humans have of machines.How we can teach humans to have empathy for machines. Dennis’s hope for the next generation in terms of their approach to AI.A lesson Dennis learned from his daughter about AI and about human nature.What Dennis is most excited about in the AI space.Tweetables:“The whole umbrella of AI is really just one big prediction engine.” — @DennisMortensen [0:03:38]“Language is not a solved science.” — @DennisMortensen [0:06:32]“The expectation of a machine response is different to that of a human response to the same question.” — @DennisMortensen [0:11:36]Links Mentioned in Today’s Episode:Dennis Mortensen on LinkedInBizzabo [Formerly x.ai]

Whether you’re building AI for self-driving cars or for scheduling meetings, it’s all about prediction! In this episode, we’re going to explore the complexity of teaching the human power of prediction to machines.Key Points From This Episode:<ul><li>Dennis shares an overview of what he has spent his career focusing on.</li><li>How Dennis defines an intelligent agent.</li><li>The role of prediction in the AI space.</li><li>Dennis explains the mission that drove his most recent entrepreneurial venture, <a href="http://x.ai" rel="nofollow" target="_blank">x.ai</a> (acquired by Bizzabo).</li><li>Challenges of transferring humans’ capacity for prediction and empathy to machines.</li><li>Some of Dennis’s key learnings from his time working on the technology for <a href="http://x.ai" rel="nofollow" target="_blank">x.ai</a>.</li><li>Unrealistic expectations that humans have of machines.</li><li>How we can teach humans to have empathy for machines. </li><li>Dennis’s hope for the next generation in terms of their approach to AI.</li><li>A lesson Dennis learned from his daughter about AI and about human nature.</li><li>What Dennis is most excited about in the AI space.</li></ul>Tweetables:“The whole umbrella of AI is really just one big prediction engine.” — <a href="https://twitter.com/DennisMortensen?ref_src=twsrc%255Egoogle%257Ctwcamp%255Eserp%257Ctwgr%255Eauthor" rel="nofollow" target="_blank">@DennisMortensen</a> [0:03:38]“Language is not a solved science.” — <a href="https://twitter.com/DennisMortensen?ref_src=twsrc%255Egoogle%257Ctwcamp%255Eserp%257Ctwgr%255Eauthor" rel="nofollow" target="_blank">@DennisMortensen</a> [0:06:32]“The expectation of a machine response is different to that of a human response to the same question.” — <a href="https://twitter.com/DennisMortensen?ref_src=twsrc%255Egoogle%257Ctwcamp%255Eserp%257Ctwgr%255Eauthor" rel="nofollow" target="_blank">@DennisMortensen</a> [0:11:36]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/dennismortensen/" rel="nofollow" target="_blank">Dennis Mortensen on LinkedIn</a><a href="https://www.bizzabo.com/" rel="nofollow" target="_blank">Bizzabo [Formerly x.ai]</a>

x.AI Founder Dennis Mortensen

Danny Lange

Leading AI companies are adopting simulation, synthetic data and other aspects of the metaverse at an incredibly fast rate, and the opportunities for AI/machine learning practitioners are endless. Tune in today for a fascinating conversation about how the real world and the virtual world can be blended in what Danny refers to as “the real metaverse.”Key Points From This Episode:The career path that led Danny to his current role as Senior Vice President of AI at Unity.Data; the machine learning challenge that Danny has dealt with in many different forms throughout his career. An explanation of how Unity uses data to make game recommendations to players. How deep learning embedding works.What drew Danny to Unity.The benefits of using synthetic data.How Unity ensures that the synthetic data they create is as unbiased as possible. The importance of anchoring your synthetic data to a real-world counterpart.Danny’s thoughts on the potential of the Metaverse.Examples of the career opportunities that the Metaverse has opened up for AI/machine learning practitioners.Tweetables:“When you play a game, I don’t need to know your name, your age. I don’t need to know where you live, or how much you earn. All that really matters is that my system needs to learn the way you play and what you are interested in in your gameplay, to make excellent recommendations for other games. That’s what drives the gaming ecosystem.” — @danny_lange [0:03:16]“Deep learning embedding is something that is really driving a lot of progress right now in the machine learning AI space.” — @danny_lange [0:06:04]“The world is built on uncertainty and we are looking at simulation in an uncertain world, rather than in a Newtonian, deterministic world.” — @danny_lange [0:23:23]Links Mentioned in Today’s Episode:Danny Lange on LinkedInUnity

Leading AI companies are adopting simulation, synthetic data and other aspects of the metaverse at an incredibly fast rate, and the opportunities for AI/machine learning practitioners are endless. Tune in today for a fascinating conversation about how the real world and the virtual world can be blended in what Danny refers to as “the real metaverse.”Key Points From This Episode:<ul><li>The career path that led Danny to his current role as Senior Vice President of AI at Unity.</li><li>Data; the machine learning challenge that Danny has dealt with in many different forms throughout his career. </li><li>An explanation of how Unity uses data to make game recommendations to players. </li><li>How deep learning embedding works.</li><li>What drew Danny to Unity.</li><li>The benefits of using synthetic data.</li><li>How Unity ensures that the synthetic data they create is as unbiased as possible. </li><li>The importance of anchoring your synthetic data to a real-world counterpart.</li><li>Danny’s thoughts on the potential of the Metaverse.</li><li>Examples of the career opportunities that the Metaverse has opened up for AI/machine learning practitioners.</li></ul>Tweetables:“When you play a game, I don’t need to know your name, your age. I don’t need to know where you live, or how much you earn. All that really matters is that my system needs to learn the way you play and what you are interested in in your gameplay, to make excellent recommendations for other games. That’s what drives the gaming ecosystem.” — <a href="https://twitter.com/danny_lange?lang=en" rel="nofollow" target="_blank">@danny_lange</a> [0:03:16]“Deep learning embedding is something that is really driving a lot of progress right now in the machine learning AI space.” — <a href="https://twitter.com/danny_lange?lang=en" rel="nofollow" target="_blank">@danny_lange</a> [0:06:04]“The world is built on uncertainty and we are looking at simulation in an uncertain world, rather than in a Newtonian, deterministic world.” — <a href="https://twitter.com/danny_lange?lang=en" rel="nofollow" target="_blank">@danny_lange</a> [0:23:23]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/dannylange/" rel="nofollow" target="_blank">Danny Lange on LinkedIn</a><a href="https://unity.com/" rel="nofollow" target="_blank">Unity</a>

Unity SVP of AI Danny Lange: The Industrial Metaverse

In today’s episode, Archy De Berker, Head of Data and Machine learning at CarbonChain, explains how he and his team calculate carbon footprints, some of the challenges that they face in this line of work, the most valuable use of machine learning in their business (and for climate change solutions as a whole), and some important lessons that he has learned throughout his diverse career so far! Key Points From This Episode:An overview of Archy’s career trajectory, from academic neuroscientist to Head of Data and Machine learning at CarbonChain.The foundational mission of CarbonChain.Archy explains how machine learning can be applied in the context of energy storage as a climate change solution. Industries that CarbonChain focuses on.How Archy and his team calculate carbon footprints.A key challenge for carbon footprinting.Where machine learning provides the most value for CarbonChain.The importance of the field of document understanding. A story from Archy’s time at Element AI that highlights the value of having technical people working as close to the design and data generation as possible. Why Archy chose to move into the product management realm.Additional ways that machine learning can help solve climate change issues. Tweetables:“We build automated carbon footprinting for the world’s most polluting industries. We’re really trying to help people who are buying things from carbon-intense industries figure out where they can get lower carbon versions of the same kind of products.” — @ArchydeB [0:02:14]“A key challenge for carbon footprinting is that you need to be able to understand somebody’s business in order to tell them what the carbon footprint of their activities is.” — @ArchydeB [0:13:01]“Probably the most valuable place for machine learning in our business is taking all this heterogeneous customer data from all these different systems and being able to map it onto a very rigid format that we can then retrieve information from our databases for.” — @ArchydeB [0:13:24]Links Mentioned in Today’s Episode:Archy de Berker on LinkedInCarbon Chain

In today’s episode, Archy De Berker, Head of Data and Machine learning at CarbonChain, explains how he and his team calculate carbon footprints, some of the challenges that they face in this line of work, the most valuable use of machine learning in their business (and for climate change solutions as a whole), and some important lessons that he has learned throughout his diverse career so far! Key Points From This Episode:<ul><li>An overview of Archy’s career trajectory, from academic neuroscientist to Head of Data and Machine learning at CarbonChain.</li><li>The foundational mission of CarbonChain.</li><li>Archy explains how machine learning can be applied in the context of energy storage as a climate change solution. </li><li>Industries that CarbonChain focuses on.</li><li>How Archy and his team calculate carbon footprints.</li><li>A key challenge for carbon footprinting.</li><li>Where machine learning provides the most value for CarbonChain.</li><li>The importance of the field of document understanding. </li><li>A story from Archy’s time at Element AI that highlights the value of having technical people working as close to the design and data generation as possible. </li><li>Why Archy chose to move into the product management realm.</li><li>Additional ways that machine learning can help solve climate change issues. </li></ul>Tweetables:“We build automated carbon footprinting for the world’s most polluting industries. We’re really trying to help people who are buying things from carbon-intense industries figure out where they can get lower carbon versions of the same kind of products.” — <a href="https://twitter.com/archydeb?lang=en" rel="nofollow" target="_blank">@ArchydeB</a> [0:02:14]“A key challenge for carbon footprinting is that you need to be able to understand somebody’s business in order to tell them what the carbon footprint of their activities is.” — <a href="https://twitter.com/archydeb?lang=en" rel="nofollow" target="_blank">@ArchydeB</a> [0:13:01]“Probably the most valuable place for machine learning in our business is taking all this heterogeneous customer data from all these different systems and being able to map it onto a very rigid format that we can then retrieve information from our databases for.” — <a href="https://twitter.com/archydeb?lang=en" rel="nofollow" target="_blank">@ArchydeB</a> [0:13:24]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/archy-de-berker/" rel="nofollow" target="_blank">Archy de Berker on LinkedIn</a><a href="https://www.carbonchain.com/" rel="nofollow" target="_blank">Carbon Chain</a>

CarbonChain Head of Data & Machine Learning Archy De Berker

Hessie Jones

MATR Ventures Partner, Hessie Jones, is dedicated to solving issues around AI ethics as well as diversity &amp; representation in the space. In our conversation with her, she breaks down how she came to beleive something was wrong with the way companies harvest &amp; use data, and the steps she has taken towards solving the privacy problem. We discuss the danger of intentionally convoluted terms and conditions and the problem with synthetic data. Tune in to hear about the future of biometrics and data privacy and the emerging technologies using data to increase accountability.Key Points From This Episode:Hessie Jones’ background: from marketing to her current role at MATR Ventures.Hessie’s focus on AI ethics and privacy, and diversity in the venture capital space.Her mission to provide equal access to programs and investment.What inspired her to tackle the problem of AI ethics and privacy.The consequences of Snowden and the responsibility of tech to enforce customer privacy.Hessie’s path of seeking the solution to the privacy problem.The danger of blanketed terms and conditions.The problem with synthetic data.Crass uses of facial recognition.Emerging technologies using data to increase accountability.The future of biometrics and data privacy.The mission of MATR Ventures and who they invest in.Examples of technologies MATR Ventures employs to ensure accountability.Tweetables:“Venture capital is not immune to the diversity problems that we see today.” — Hessie Jones [0:05:04]“We should separate who you are as an individual from who you are as a business customer.” — Hessie Jones [0:08:49]“The problem I see with synthetic data is the rise of deep fakes.” — Hessie Jones [0:21:24]“The future is really about data that’s not shared, or if it’s shared, it’s shared in a way that increases accountability.” — Hessie Jones [0:26:43]Links Mentioned in Today’s Episode:Hessie Jones on LinkedInMATR VenturesResponsible AI

MATR Ventures Partner, Hessie Jones, is dedicated to solving issues around AI ethics as well as diversity &amp; representation in the space. In our conversation with her, she breaks down how she came to beleive something was wrong with the way companies harvest &amp; use data, and the steps she has taken towards solving the privacy problem. We discuss the danger of intentionally convoluted terms and conditions and the problem with synthetic data. Tune in to hear about the future of biometrics and data privacy and the emerging technologies using data to increase accountability.Key Points From This Episode:<ul><li>Hessie Jones’ background: from marketing to her current role at MATR Ventures.</li><li>Hessie’s focus on AI ethics and privacy, and diversity in the venture capital space.</li><li>Her mission to provide equal access to programs and investment.</li><li>What inspired her to tackle the problem of AI ethics and privacy.</li><li>The consequences of Snowden and the responsibility of tech to enforce customer privacy.</li><li>Hessie’s path of seeking the solution to the privacy problem.</li><li>The danger of blanketed terms and conditions.</li><li>The problem with synthetic data.</li><li>Crass uses of facial recognition.</li><li>Emerging technologies using data to increase accountability.</li><li>The future of biometrics and data privacy.</li><li>The mission of MATR Ventures and who they invest in.</li><li>Examples of technologies MATR Ventures employs to ensure accountability.</li></ul>Tweetables:“Venture capital is not immune to the diversity problems that we see today.” — Hessie Jones [0:05:04]“We should separate who you are as an individual from who you are as a business customer.” — Hessie Jones [0:08:49]“The problem I see with synthetic data is the rise of deep fakes.” — Hessie Jones [0:21:24]“The future is really about data that’s not shared, or if it’s shared, it’s shared in a way that increases accountability.” — Hessie Jones [0:26:43]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/hessiejones1" rel="nofollow" target="_blank">Hessie Jones on LinkedIn</a><a href="https://www.matrventures.com/" rel="nofollow" target="_blank">MATR Ventures</a><a href="https://www.responsible.ai/" rel="nofollow" target="_blank">Responsible AI</a>

Privacy in AI with MATR Ventures Partner Hessie Jones

Vinesh Sukumar

During Vinesh Sukumar’s colorful career he has worked at NASA, Apple, Intel, and a variety of other companies, before finding his way to Qualcomm where he is currently the Head of AI/ML Product Management. In today’s conversation, Vinesh shares his experience of developing the camera for the very first iPhone and one of the biggest lessons he learned from working with Steve Jobs. We then discuss what his current role entails and the biggest challenge that he has with it, Qualcomm’s approach to sustainability from a hardware, systems and software standpoint, and his thoughts on why edge computing is so important.Key Points From This Episode:An overview of Vinesh’s career trajectory, including his experiences at NASA, Apple, and Intel.The focal area of Vinesh’s PhD.  Challenges that Vinesh faced while working on cutting edge technology for camera phones.Some of the early AI applications that were used in smartphone cameras. The most important factors to consider when developing cameras for phones.Valuable lessons that Vinesh learned from working with Steve Jobs.What Vinesh’s role as Head of AI/ML Product Management at Qualcomm consists of.Why optimization is one of the biggest technical challenges that Vinesh faces in his role at Qualcomm. The four buckets of MLOps. Vinesh explains why edge computing is so important. Benefits of building intelligence into devices rather than requiring a connection to the cloud.Qualcomm’s approach to scalability. Why Vinesh is excited about cognitive AI.Tweetables:“Camera became one of the most important features for a consumer to buy a phone. Then visual analytics, AI, deep learning, ML really started seeping into images, and then into videos, and now the most important consumer influencing factor to buy a phone is the camera.” — Vinesh Sukumar [0:07:01]“Reaction time is much better when you have intelligence on the device, rather than giving it to the cloud to make the decision for you.” — Vinesh Sukumar [0:20:48]Links Mentioned in Today’s Episode:Vinesh Sukumar on LinkedInQualcomm

During Vinesh Sukumar’s colorful career he has worked at NASA, Apple, Intel, and a variety of other companies, before finding his way to Qualcomm where he is currently the Head of AI/ML Product Management. In today’s conversation, Vinesh shares his experience of developing the camera for the very first iPhone and one of the biggest lessons he learned from working with Steve Jobs. We then discuss what his current role entails and the biggest challenge that he has with it, Qualcomm’s approach to sustainability from a hardware, systems and software standpoint, and his thoughts on why edge computing is so important.Key Points From This Episode:<ul><li>An overview of Vinesh’s career trajectory, including his experiences at NASA, Apple, and Intel.</li><li>The focal area of Vinesh’s PhD.  </li><li>Challenges that Vinesh faced while working on cutting edge technology for camera phones.</li><li>Some of the early AI applications that were used in smartphone cameras. </li><li>The most important factors to consider when developing cameras for phones.</li><li>Valuable lessons that Vinesh learned from working with Steve Jobs.</li><li>What Vinesh’s role as Head of AI/ML Product Management at Qualcomm consists of.</li><li>Why optimization is one of the biggest technical challenges that Vinesh faces in his role at Qualcomm. </li><li>The four buckets of MLOps. </li><li>Vinesh explains why edge computing is so important. </li><li>Benefits of building intelligence into devices rather than requiring a connection to the cloud.</li><li>Qualcomm’s approach to scalability. </li><li>Why Vinesh is excited about cognitive AI.</li></ul>Tweetables:“Camera became one of the most important features for a consumer to buy a phone. Then visual analytics, AI, deep learning, ML really started seeping into images, and then into videos, and now the most important consumer influencing factor to buy a phone is the camera.” — Vinesh Sukumar [0:07:01]“Reaction time is much better when you have intelligence on the device, rather than giving it to the cloud to make the decision for you.” — Vinesh Sukumar [0:20:48]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/vineshsukumar/" rel="nofollow" target="_blank">Vinesh Sukumar on LinkedIn</a><a href="https://www.qualcomm.com/" rel="nofollow" target="_blank">Qualcomm</a>

Qualcomm Head of AI & ML Product Management Dr. Vinesh Sukumar

Joining us on this episode of How AI Happens is four-time author, entrepreneur, future tech strategist, and The Digital Speaker himself, Dr. Mark van Rijmenam. Mark explainsthe extraordinary opportunities and challenges facing business leaders, consumers, regulators, policymakers, and other metaverse stakeholders trying to navigate the future of the internet; the important role that AI will play in the metaverse; why he believes we need to enable what he calls ‘anonymous accountability’; and how you can actively participate in building ethical AI. Key Points From This Episode:Meet Dr. Mark van Rijmenam and gain some insight into his trajectory thus far.The role that AI and the blockchain played in Mark’s book, The Organisation of Tomorrow.What we can learn about feedback loops from the failures of Microsoft’s Tay chatbot.At what point technology shifts from a tool employed by practitioners to autonomous agent.Distinguishing between artificial general intelligence (AGI) and Super AI.Mark responds to those who believe we will never reach Super AI; it’s inevitable!The advent of the metaverse and why Mark believes it will unlock a trillion-dollar social economy, as per his book, Step Into the Metaverse.How Web 3.0 will allow us to reclaim control of our data, digital assets, and identity; moving from value extraction to value creation.Understanding the difference between the metaverse and Web 3.0 without conflating the two.How Mark sees AI participating in the metaverse and the role it will play in this ‘new world’.The dangers that come with the uncanny ‘deep fakes’ of the future.Our responsibility to properly verify the digital information we consume and how AI can help.What Mark means when he says we need to enable ‘anonymous accountability’.How to take advantage of the career opportunities of Web 3.0 and the metaverse and how you can contribute to building ethical AI.Tweetables:“The social and the material [systems are] very good but, for the organizations of tomorrow, we need to add a third actor, which is the artificial.” — @VanRijmenam [0:03:05]“Once we reach AGI, that will be a fundamental shift because, once we have AGI—which is as intelligent as a human being, but at an exponential speed—everything will change.” — @VanRijmenam [0:08:34]“How can we create a metaverse that doesn’t continue on the path of the internet of today? We have this blank canvas where we can construct this immersive internet in ways where we do own our data, [digital assets, identity, and reputation] using a self-sovereign approach.” — @VanRijmenam [0:15:09]“Technology is neutral. My objective is to help people move to the positive side of technology.” — @VanRijmenam [0:29:24]Links Mentioned in Today’s Episode:Dr. Mark van Rijmenam on LinkedInDr. Mark van Rijmenam on TwitterThe Digital SpeakerDatafloqBetween Two Bots PodcastStep Into the MetaverseThe Organisation of Tomorrow‘The Matrix Awakens: An Unreal Engine 5 Experience’

Joining us on this episode of How AI Happens is four-time author, entrepreneur, future tech strategist, and The Digital Speaker himself, Dr. Mark van Rijmenam. Mark explainsthe extraordinary opportunities and challenges facing business leaders, consumers, regulators, policymakers, and other metaverse stakeholders trying to navigate the future of the internet; the important role that AI will play in the metaverse; why he believes we need to enable what he calls ‘anonymous accountability’; and how you can actively participate in building ethical AI. Key Points From This Episode:<ul><li>Meet Dr. Mark van Rijmenam and gain some insight into his trajectory thus far.</li><li>The role that AI and the blockchain played in Mark’s book, The Organisation of Tomorrow.</li><li>What we can learn about feedback loops from the failures of Microsoft’s Tay chatbot.</li><li>At what point technology shifts from a tool employed by practitioners to autonomous agent.</li><li>Distinguishing between artificial general intelligence (AGI) and Super AI.</li><li>Mark responds to those who believe we will never reach Super AI; it’s inevitable!</li><li>The advent of the metaverse and why Mark believes it will unlock a trillion-dollar social economy, as per his book, Step Into the Metaverse.</li><li>How Web 3.0 will allow us to reclaim control of our data, digital assets, and identity; moving from value extraction to value creation.</li><li>Understanding the difference between the metaverse and Web 3.0 without conflating the two.</li><li>How Mark sees AI participating in the metaverse and the role it will play in this ‘new world’.</li><li>The dangers that come with the uncanny ‘deep fakes’ of the future.</li><li>Our responsibility to properly verify the digital information we consume and how AI can help.</li><li>What Mark means when he says we need to enable ‘anonymous accountability’.</li><li>How to take advantage of the career opportunities of Web 3.0 and the metaverse and how you can contribute to building ethical AI.</li></ul>Tweetables:“The social and the material [systems are] very good but, for the organizations of tomorrow, we need to add a third actor, which is the artificial.” — <a href="https://twitter.com/VanRijmenam" rel="nofollow" target="_blank">@VanRijmenam</a> [0:03:05]“Once we reach AGI, that will be a fundamental shift because, once we have AGI—which is as intelligent as a human being, but at an exponential speed—everything will change.” — <a href="https://twitter.com/VanRijmenam" rel="nofollow" target="_blank">@VanRijmenam</a> [0:08:34]“How can we create a metaverse that doesn’t continue on the path of the internet of today? We have this blank canvas where we can construct this immersive internet in ways where we do own our data, [digital assets, identity, and reputation] using a self-sovereign approach.” — <a href="https://twitter.com/VanRijmenam" rel="nofollow" target="_blank">@VanRijmenam</a> [0:15:09]“Technology is neutral. My objective is to help people move to the positive side of technology.” — <a href="https://twitter.com/VanRijmenam" rel="nofollow" target="_blank">@VanRijmenam</a> [0:29:24]Links Mentioned in Today’s Episode:<a href="https://au.linkedin.com/in/markvanrijmenam" rel="nofollow" target="_blank">Dr. Mark van Rijmenam on LinkedIn</a><a href="https://twitter.com/VanRijmenam" rel="nofollow" target="_blank">Dr. Mark van Rijmenam on Twitter</a><a href="https://www.thedigitalspeaker.com/" rel="nofollow" target="_blank">The Digital Speaker</a><a href="https://datafloq.com/" rel="nofollow" target="_blank">Datafloq</a><a href="https://betweentwobots.com/" rel="nofollow" target="_blank">Between Two Bots Podcast</a><a href="https://amzn.to/2ZtC9S3" rel="nofollow" target="_blank">Step Into the Metaverse</a><a href="https://amzn.to/31mL436" rel="nofollow" target="_blank">The Organisation of Tomorrow</a><a href="https://youtu.be/WU0gvPcc3jQ" rel="nofollow" target="_blank">‘The Matrix Awakens: An Unreal Engine 5 Experience’</a>

AI in the Metaverse with Dr. Mark Rijmenam

Neil Sahota

Neil Sahota is an AI Advisor to the UN, co-founder of the UN’s AI for Good initiative, IBM Master Inventor, and author of Own the AI Revolution. In today’s episode, Neil shares some of the valuable lessons he learned during his first experience working in the AI world, which involved training the Watson computer system. We then dive into a number of different topics, ranging from Neil’s thoughts on synthetic data and to the language learning capacity of AI versus a human child, to an overview of the AI for Good initiative and what Neil believes our a “cyborg future” could entail! Key Points From This Episode:A few of the thousands of data points that humans use to make rapid judgments.Neil’s introduction into the world of AI.How data collection changed AI, using the Watson computer system as an example. Lessons that Neil learned through training Watson.The relative importance of confidence levels with regard to training AI in different fields.Why reaching a 99.9% confidence level is not realistic.Examples of cases where synthetic data is and isn’t helpful.A major difference between the language learning trajectory of AI versus a human child.Areas that Neil believes AI is best suited for.Focus of the United Nations’ AI for Good initiative.The UN’s approach to bringing AI technologies to remote parts of the world.Benefits of being exposed to technology at a young age.The cyborg future: what Neil believes this is going to look like.Why Neil is excited about AI augmentation for human creativity. Tweetables:“We, as human beings, have to make really rapid judgement calls, especially in sports, but there’s still thousands of data points in play and the best of us can only see seven to 12 in real time.” — @neil_sahota [0:01:21]“Synthetic data can be a good bridge if we’re in a very closed ecosystem.” — @neil_sahota [0:11:47]“For an AI system, if it gets exposed to about 100 billion words it becomes proficient and fluent in a language. If you think about a human child, it only needs about 30 billion words. So, it’s not the volume that matters, there’s certain words or phrases that trigger the cognitive learning for language. The problem is that we just don’t understand what that is.” — @neil_sahota [0:14:22]“Things that are more hard science, or things that have the least amount of variability, are the best things for AI systems.” — @neil_sahota [0:16:26]“Local problems have global solutions.” — @neil_sahota [0:20:06]Links Mentioned in Today’s Episode:Neil SahotaNeil Sahota on LinkedInOwn the A.I. RevolutionAI for Good

Neil Sahota is an AI Advisor to the UN, co-founder of the UN’s AI for Good initiative, IBM Master Inventor, and author of Own the AI Revolution. In today’s episode, Neil shares some of the valuable lessons he learned during his first experience working in the AI world, which involved training the Watson computer system. We then dive into a number of different topics, ranging from Neil’s thoughts on synthetic data and to the language learning capacity of AI versus a human child, to an overview of the AI for Good initiative and what Neil believes our a “cyborg future” could entail! Key Points From This Episode:<ul><li>A few of the thousands of data points that humans use to make rapid judgments.</li><li>Neil’s introduction into the world of AI.</li><li>How data collection changed AI, using the Watson computer system as an example. </li><li>Lessons that Neil learned through training Watson.</li><li>The relative importance of confidence levels with regard to training AI in different fields.</li><li>Why reaching a 99.9% confidence level is not realistic.</li><li>Examples of cases where synthetic data is and isn’t helpful.</li><li>A major difference between the language learning trajectory of AI versus a human child.</li><li>Areas that Neil believes AI is best suited for.</li><li>Focus of the United Nations’ AI for Good initiative.</li><li>The UN’s approach to bringing AI technologies to remote parts of the world.</li><li>Benefits of being exposed to technology at a young age.</li><li>The cyborg future: what Neil believes this is going to look like.</li><li>Why Neil is excited about AI augmentation for human creativity. </li></ul>Tweetables:“We, as human beings, have to make really rapid judgement calls, especially in sports, but there’s still thousands of data points in play and the best of us can only see seven to 12 in real time.” — <a href="https://twitter.com/neil_sahota?ref_src=twsrc%255Egoogle%257Ctwcamp%255Eserp%257Ctwgr%255Eauthor" rel="nofollow" target="_blank">@neil_sahota</a> [0:01:21]“Synthetic data can be a good bridge if we’re in a very closed ecosystem.” — <a href="https://twitter.com/neil_sahota?ref_src=twsrc%255Egoogle%257Ctwcamp%255Eserp%257Ctwgr%255Eauthor" rel="nofollow" target="_blank">@neil_sahota</a> [0:11:47]“For an AI system, if it gets exposed to about 100 billion words it becomes proficient and fluent in a language. If you think about a human child, it only needs about 30 billion words. So, it’s not the volume that matters, there’s certain words or phrases that trigger the cognitive learning for language. The problem is that we just don’t understand what that is.” — <a href="https://twitter.com/neil_sahota?ref_src=twsrc%255Egoogle%257Ctwcamp%255Eserp%257Ctwgr%255Eauthor" rel="nofollow" target="_blank">@neil_sahota</a> [0:14:22]“Things that are more hard science, or things that have the least amount of variability, are the best things for AI systems.” — <a href="https://twitter.com/neil_sahota?ref_src=twsrc%255Egoogle%257Ctwcamp%255Eserp%257Ctwgr%255Eauthor" rel="nofollow" target="_blank">@neil_sahota</a> [0:16:26]“Local problems have global solutions.” — <a href="https://twitter.com/neil_sahota?ref_src=twsrc%255Egoogle%257Ctwcamp%255Eserp%257Ctwgr%255Eauthor" rel="nofollow" target="_blank">@neil_sahota</a> [0:20:06]Links Mentioned in Today’s Episode:<a href="https://www.neilsahota.com/" rel="nofollow" target="_blank">Neil Sahota</a><a href="https://www.linkedin.com/in/neilsahota/" rel="nofollow" target="_blank">Neil Sahota on LinkedIn</a><a href="https://www.amazon.com/Own-I-Revolution-Intelligence-Competition/dp/1260458377" rel="nofollow" target="_blank">Own the A.I. Revolution</a><a href="https://aiforgood.itu.int/%23:~:text=More%2520than%2520a%2520Summit,%2520more,United%2520Nations%2520Sustainable%2520Development%2520Goals." rel="nofollow" target="_blank">AI for Good</a>

IBM Master Inventor & AI Advisor to the UN Neil Sahota

Marcell Vollmer

Dr. Marcell Vollmer is the CEO of Prospitalia Group, formerly Chief Innovation Officer at Celonis and Chief Digital Officer at SAP. He joins to discuss Machine Learning advances in MedTech and how practitioners can be thoughtful about when it is appropriate to deploy ML.

Prospitalia Group CEO Dr. Marcell Vollmer

How AI Happens is a podcast featuring experts and practitioners explaining their work at the cutting edge of Artificial Intelligence. Tune in to hear AI Researchers, Data Scientists, ML Engineers, and the leaders of today’s most exciting AI companies explain the newest and most challenging facets of their field. Powered by Sama.

Roman Yampolskiy

 Today’s guest has committed many years of his life to trying to understand Artificial Superintelligence and the security concerns associated with it.  Dr. Roman Yampolskiy is a computer scientist (with a Ph.D. in behavioral biometrics), and an Associate Professor at the University of Louisville. He is also the author of the book Artificial Superintelligence: A Futuristic Approach. Today he joins us to discuss AI safety engineering. You’ll hear about some of the safety problems  he has discovered in his 10 years of research, his thoughts on accountability and ownership when AI fails, and whether he believes it’s possible to enact any real safety measures in light of the decentralization and commoditization of processing power. You’ll discover some of the near-term risks of not prioritizing safety engineering in AI, how to make sure you’re developing it in a safe capacity, and what organizations are deploying it in a way that Dr. Yampolskiy believes to be above board. Key Points From This Episode:An introduction to Dr. Roman Yampolskiy, his education, and how he ended up in his current role. Insight into Dr. Yampolskiy’s Ph.D. dissertation in behavioral biometrics and what he learned from it. A definition of AI safety engineering.The two subcomponents of AI safety: systems we already have and future AI.Thoughts on whether or not there is a greater need for guardrails in AI than other forms of technology.Some of the safety problems that Dr. Yampolskiy has discovered in his 10 years of research.Dr. Yampolskiy’s thoughts on the need for some type of AI security governing body or oversight board.Whether it’s possible to enact any sort of safety in light of the decentralization and commoditization of processing power.Solvable problem areas. Trying to negotiate the tradeoff between enabling AI to have creative freedom and being able to control it.Thoughts on whether or not there will be a time where we will have to decide whether or not to go past the point of no return in terms of AI superintelligence.Some of the near-term risks of not prioritizing safety engineering in AI.What led Dr. Yampolskiy to focus on this area of AI expertise.How to make sure you’re developing AI safely.Thoughts on accountability and ownership when AI fails, and the legal implications of this.Other problems Dr. Yampolskiy has uncovered. Thoughts on the need for a greater understanding of the implications of AI work and whether or not this is a conceivable solution.Use cases or organizations that are deploying AI in a way that Dr. Yampolskiy believes to be above board.Questions that Dr. Yampolskiy would be asking if he was on an AI development safety team.How you can measure progress in safety work. Tweetables:“Long term, we want to make sure that we don’t create something which is more capable than us and completely out of control.” — @romanyam [0:04:27]“This is the tradeoff we’re facing: Either [AI] is going to be very capable, independent, and creative, or we can control it.” — @romanyam [0:12:11]“Maybe there are problems that we really need Superintelligence [to solve]. In that case, we have to give it more creative freedom but with that comes the danger of it making decisions that we will not like.” — @romanyam [0:12:31]“The more capable the system is, the more it is deployed, the more damage it can cause.” — @romanyam [0:14:55]“It seems like it’s the most important problem, it’s the meta-solution to all the other problems. If you can make friendly well-controlled superintelligence, everything else is trivial. It will solve it for you.” — @romanyam [0:15:26]Links Mentioned in Today’s Episode:Dr. Roman YampolskiyArtificial Superintelligence: A Futuristic ApproachDr. Roman Yampolskiy on Twitter

Today’s guest has committed many years of his life to trying to understand Artificial Superintelligence and the security concerns associated with it.  Dr. Roman Yampolskiy is a computer scientist (with a Ph.D. in behavioral biometrics), and an Associate Professor at the University of Louisville. He is also the author of the book Artificial Superintelligence: A Futuristic Approach. Today he joins us to discuss AI safety engineering. You’ll hear about some of the safety problems  he has discovered in his 10 years of research, his thoughts on accountability and ownership when AI fails, and whether he believes it’s possible to enact any real safety measures in light of the decentralization and commoditization of processing power. You’ll discover some of the near-term risks of not prioritizing safety engineering in AI, how to make sure you’re developing it in a safe capacity, and what organizations are deploying it in a way that Dr. Yampolskiy believes to be above board. Key Points From This Episode:<ul><li>An introduction to Dr. Roman Yampolskiy, his education, and how he ended up in his current role. </li><li>Insight into Dr. Yampolskiy’s Ph.D. dissertation in behavioral biometrics and what he learned from it. </li><li>A definition of AI safety engineering.</li><li>The two subcomponents of AI safety: systems we already have and future AI.</li><li>Thoughts on whether or not there is a greater need for guardrails in AI than other forms of technology.</li><li>Some of the safety problems that Dr. Yampolskiy has discovered in his 10 years of research.</li><li>Dr. Yampolskiy’s thoughts on the need for some type of AI security governing body or oversight board.</li><li>Whether it’s possible to enact any sort of safety in light of the decentralization and commoditization of processing power.</li><li>Solvable problem areas. </li><li>Trying to negotiate the tradeoff between enabling AI to have creative freedom and being able to control it.</li><li>Thoughts on whether or not there will be a time where we will have to decide whether or not to go past the point of no return in terms of AI superintelligence.</li><li>Some of the near-term risks of not prioritizing safety engineering in AI.</li><li>What led Dr. Yampolskiy to focus on this area of AI expertise.</li><li>How to make sure you’re developing AI safely.</li><li>Thoughts on accountability and ownership when AI fails, and the legal implications of this.</li><li>Other problems Dr. Yampolskiy has uncovered. </li><li>Thoughts on the need for a greater understanding of the implications of AI work and whether or not this is a conceivable solution.</li><li>Use cases or organizations that are deploying AI in a way that Dr. Yampolskiy believes to be above board.</li><li>Questions that Dr. Yampolskiy would be asking if he was on an AI development safety team.</li><li>How you can measure progress in safety work. </li></ul>Tweetables:“Long term, we want to make sure that we don’t create something which is more capable than us and completely out of control.” — <a href="https://twitter.com/romanyam" rel="nofollow" target="_blank">@romanyam</a> [0:04:27]“This is the tradeoff we’re facing: Either [AI] is going to be very capable, independent, and creative, or we can control it.” — <a href="https://twitter.com/romanyam" rel="nofollow" target="_blank">@romanyam</a> [0:12:11]“Maybe there are problems that we really need Superintelligence [to solve]. In that case, we have to give it more creative freedom but with that comes the danger of it making decisions that we will not like.” — <a href="https://twitter.com/romanyam" rel="nofollow" target="_blank">@romanyam</a> [0:12:31]“The more capable the system is, the more it is deployed, the more damage it can cause.” — <a href="https://twitter.com/romanyam" rel="nofollow" target="_blank">@romanyam</a> [0:14:55]“It seems like it’s the most important problem, it’s the meta-solution to all the other problems. If you can make friendly well-controlled superintelligence, everything else is trivial. It will solve it for you.” — <a href="https://twitter.com/romanyam" rel="nofollow" target="_blank">@romanyam</a> [0:15:26]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/romanyam/" rel="nofollow" target="_blank">Dr. Roman Yampolskiy</a><a href="https://www.amazon.com/Artificial-Superintelligence-Futuristic-Roman-Yampolskiy/dp/1482234432" rel="nofollow" target="_blank">Artificial Superintelligence: A Futuristic Approach</a><a href="https://twitter.com/romanyam" rel="nofollow" target="_blank">Dr. Roman Yampolskiy on Twitter</a>

AI Safety Engineering - Dr. Roman Yampolskiy

Sebastian Raschka

LEAD

Joining us today on How AI Happens is Sebastian Raschka, Lead AI educator at GRID.ai and Assistant Professor of Statistics at the University of Wisconsin-Madison.  Sebastian fills us in on the coursework he’s creating in his role at GRID.ai, and we find out what can be attributed to the crossover of machine learning in academia and the private sector. We speculate on the pros and cons of the commodification of deep learning models and which machine learning framework is better: PyTorch or TensorFlow. Key Points From This Episode:Sebastian Raschka’s journey from the computation of biology to AI and machine learning.The focus of his current role as Lead AI educator at GRID.ai.The ideal applications and outcomes of the coursework Sebastian is developing.The crossover of machine learning in academia and the private sector; the theory versus the application.Deep learning versus machine learning and what constitutes a deep learning problem.The importance of sufficient data for deep learning to be effective.The applications of the BERT text model.The pros and cons of developing more accessible models.Why Sebastian set out to write Machine Learning with PyTorch and Scikit-Learn.The structure of the book, including theory and application.Why Sebastian prefers PyTorch over TensorFlow.What he finds most exciting in the current deep learning space.The emerging opportunities to use deep learning!Tweetables:“In academia, the focus is more on understanding how deep learning works… On the other hand, in the industry, there are [many] use cases of machine learning.” — @rasbt [0:10:10]“Often it is hard to formulate answers as a human to complex questions.” — @rasbt [0:12:53]“In my experience, deep learning can be very powerful but you need a lot of data to make it work well.” — @rasbt [0:14:06]“In [Machine Learning with PyTorch and Scikit-Learn], I tried to provide a resource that is a hybrid between more theoretical books and more applied books.” — @rasbt [0:23:21]“Why I like PyTorch is that it gives me the readability [and] flexibility to customize things.” — @rasbt [0:25:55]Links Mentioned in Today’s Episode:Sebastian RaschkaSebastian Raschka on TwitterGRID.aiMachine Learning with PyTorch and Scikit-Learn

Joining us today on How AI Happens is Sebastian Raschka, Lead AI educator at <a href="https://www.grid.ai/" rel="nofollow" target="_blank">GRID.ai</a> and Assistant Professor of Statistics at the University of Wisconsin-Madison.  Sebastian fills us in on the coursework he’s creating in his role at <a href="http://GRID.ai" rel="nofollow" target="_blank">GRID.ai</a>, and we find out what can be attributed to the crossover of machine learning in academia and the private sector. We speculate on the pros and cons of the commodification of deep learning models and which machine learning framework is better: PyTorch or TensorFlow. Key Points From This Episode:<ul><li>Sebastian Raschka’s journey from the computation of biology to AI and machine learning.</li><li>The focus of his current role as Lead AI educator at <a href="http://GRID.ai" rel="nofollow" target="_blank">GRID.ai</a>.</li><li>The ideal applications and outcomes of the coursework Sebastian is developing.</li><li>The crossover of machine learning in academia and the private sector; the theory versus the application.</li><li>Deep learning versus machine learning and what constitutes a deep learning problem.</li><li>The importance of sufficient data for deep learning to be effective.</li><li>The applications of the BERT text model.</li><li>The pros and cons of developing more accessible models.</li><li>Why Sebastian set out to write Machine Learning with PyTorch and Scikit-Learn.</li><li>The structure of the book, including theory and application.</li><li>Why Sebastian prefers PyTorch over TensorFlow.</li><li>What he finds most exciting in the current deep learning space.</li><li>The emerging opportunities to use deep learning!</li></ul>Tweetables:“In academia, the focus is more on understanding how deep learning works… On the other hand, in the industry, there are [many] use cases of machine learning.” — <a href="https://twitter.com/rasbt" rel="nofollow" target="_blank">@rasbt</a> [0:10:10]“Often it is hard to formulate answers as a human to complex questions.” — <a href="https://twitter.com/rasbt" rel="nofollow" target="_blank">@rasbt</a> [0:12:53]“In my experience, deep learning can be very powerful but you need a lot of data to make it work well.” — <a href="https://twitter.com/rasbt" rel="nofollow" target="_blank">@rasbt</a> [0:14:06]“In [Machine Learning with PyTorch and Scikit-Learn], I tried to provide a resource that is a hybrid between more theoretical books and more applied books.” — <a href="https://twitter.com/rasbt" rel="nofollow" target="_blank">@rasbt</a> [0:23:21]“Why I like PyTorch is that it gives me the readability [and] flexibility to customize things.” — <a href="https://twitter.com/rasbt" rel="nofollow" target="_blank">@rasbt</a> [0:25:55]Links Mentioned in Today’s Episode:<a href="https://sebastianraschka.com/" rel="nofollow" target="_blank">Sebastian Raschka</a><a href="https://twitter.com/rasbt" rel="nofollow" target="_blank">Sebastian Raschka on Twitter</a><a href="https://www.grid.ai/" rel="nofollow" target="_blank">GRID.ai</a><a href="https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-scikit-learn-ebook-dp-B09NW48MR1/dp/B09NW48MR1/" rel="nofollow" target="_blank">Machine Learning with PyTorch and Scikit-Learn</a>

GRID.ai's Lead AI Educator Sebastian Raschka

Antonio Grasso

Antonio Grasso joins us to explain how he empowers some of the biggest companies in the world to use AI in a meaningful way and explains the two ways his company goes about this. You’ll hear about what Antonio believes is coming down the pipeline in terms of the Internet of Things, especially when it comes to edge computing, and why network traffic has become a huge concern. We discuss where edge computing begins and ends with regards to the difference between the device and its computational resources. In light of the fact that one can infer at the edge but not train at the edge, Antonio shares his views on why he disagrees that the ultimate goal should be to train at the edge. He also provides a helpful resource for AI practitioners to calculate an AI readiness index.Key Points From This Episode:How Antonio Grasso grew from a software engineer in the 80s to someone so influential in the world of AI today. How Antonio empowers some of the biggest companies to use AI in a meaningful way. The two faces of Antonio’s company Digital Business Innovation Srl. One of Antonio’s biggest achievements at IBM.How, as a technical expert, he helps people to understand what is possible with this technology and build trust. The challenges of maintaining the pace of innovation.What Antonio believes is coming down the pipeline in terms of Internet of Things especially when it comes to edge computing.Why network traffic has become a huge concern and the role of edge computing in this. What defines edge computing and the difference between the device and its computational resources.In light of the fact that one can infer at the edge but not train at the edge, Antonio’s views on whether or not the goal should be to train at the edge. Antonio’s advice to the AI practitioner out there on how you can calculate an AI readiness index. Tweetables:“‘Wow, this is really unbelievable! We can also create not [only] code software with direct explicit instruction, we can also [create] code software that learns from experience!’ That really [caught] me and I fell in love with this kind of technology.” — @antgrasso [0:03:13]“I started on Social Media to share my knowledge, my experience, because I think you must share what you see because everyone can benefit of it too.” — @antgrasso [0:03:39]“We need to shift to better understand what is the meaning of edge computing but we must divide the device itself from the computational resources that we put [there] to harness the power of computational power in proximity.” — @antgrasso [0:16:15]“I can not imagine training at the edge. — We can do it, yes, but my question is why?” — @antgrasso [0:20:50]Links Mentioned in Today’s Episode:Antonio GrassoDigital Business Innovation SrlAI Singapore (AIRI Assessment)Antonio Grasso on TwitterHow AI Happens

Antonio Grasso joins us to explain how he empowers some of the biggest companies in the world to use AI in a meaningful way and explains the two ways his company goes about this. You’ll hear about what Antonio believes is coming down the pipeline in terms of the Internet of Things, especially when it comes to edge computing, and why network traffic has become a huge concern. We discuss where edge computing begins and ends with regards to the difference between the device and its computational resources. In light of the fact that one can infer at the edge but not train at the edge, Antonio shares his views on why he disagrees that the ultimate goal should be to train at the edge. He also provides a helpful resource for AI practitioners to calculate an AI readiness index.Key Points From This Episode:<ul><li>How Antonio Grasso grew from a software engineer in the 80s to someone so influential in the world of AI today. </li><li>How Antonio empowers some of the biggest companies to use AI in a meaningful way. </li><li>The two faces of Antonio’s company Digital Business Innovation Srl. </li><li>One of Antonio’s biggest achievements at IBM.</li><li>How, as a technical expert, he helps people to understand what is possible with this technology and build trust. </li><li>The challenges of maintaining the pace of innovation.</li><li>What Antonio believes is coming down the pipeline in terms of Internet of Things especially when it comes to edge computing.</li><li>Why network traffic has become a huge concern and the role of edge computing in this. </li><li>What defines edge computing and the difference between the device and its computational resources.</li><li>In light of the fact that one can infer at the edge but not train at the edge, Antonio’s views on whether or not the goal should be to train at the edge. </li><li>Antonio’s advice to the AI practitioner out there on how you can calculate an AI readiness index. </li></ul>Tweetables:“‘Wow, this is really unbelievable! We can also create not [only] code software with direct explicit instruction, we can also [create] code software that learns from experience!’ That really [caught] me and I fell in love with this kind of technology.” — <a href="https://twitter.com/antgrasso" rel="nofollow" target="_blank">@antgrasso</a> [0:03:13]“I started on Social Media to share my knowledge, my experience, because I think you must share what you see because everyone can benefit of it too.” — <a href="https://twitter.com/antgrasso" rel="nofollow" target="_blank">@antgrasso</a> [0:03:39]“We need to shift to better understand what is the meaning of edge computing but we must divide the device itself from the computational resources that we put [there] to harness the power of computational power in proximity.” — <a href="https://twitter.com/antgrasso" rel="nofollow" target="_blank">@antgrasso</a> [0:16:15]“I can not imagine training at the edge. — We can do it, yes, but my question is why?” — <a href="https://twitter.com/antgrasso" rel="nofollow" target="_blank">@antgrasso</a> [0:20:50]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/antonio-grasso-0544007/" rel="nofollow" target="_blank">Antonio Grasso</a><a href="https://www.dbi.srl/" rel="nofollow" target="_blank">Digital Business Innovation Srl</a><a href="https://aisingapore.org/airi/" rel="nofollow" target="_blank">AI Singapore (AIRI Assessment)</a><a href="https://twitter.com/antgrasso" rel="nofollow" target="_blank">Antonio Grasso on Twitter</a><a href="https://www.howaihappens.com/" rel="nofollow" target="_blank">How AI Happens</a>

Edge Computing & AI Readiness with Antonio Grasso

Aleksandra Przegalinska

Today's guest is Aleksandra Przegalinska PhD, Vice-Rector at Kozminski University, research associate, and Polish futurist. From studying pure philosophy, Aleksandra moved into AI when she started researching natural language processing in the virtual space. We kickstart our discussion with her account of how she ended up where she is now, and how she transferred her skills from philosophy to AI. We hear how Second Life was common in Asia centuries ago, why we are seeing a return to anonymization online, and why Aleksandra feels NLP should be called ‘natural language understanding’. We also discover what the real-world applications of NLP are, and why text processing is under-utilized. Moving onto more philosophical questions around AI and labor, Aleksandra explains how AI should be used to help people and why what is sometimes simple for a human can be immensely complex for AI. We wrap up with Aleksandra’s thoughts on transformers and why their applications are more important than their capabilities, as well as why she is so excited about the idea of xenobots. Key Points From This Episode:An introduction to today’s guest, Aleksandra Przegalinska, PhD.What Aleksandra is researching at the moment and how she ended up in academia. Insight into the link between her PhD topic and the Metaverse and the transfer of skills from a philosophy degree to AI. How a properly built digital ecosystem allows people freedom of expression and other takeaways from Aleksandra’s PhD experience. The return to online anonymization that we are currently seeing. Aleksandra’s experience of NLP in Second Life and how AI has altered the field. The role of NLP in Aleksandra’s work today and why she feels it should be called ‘natural language understanding’. The real-world applications of NLP: why text processing is under-utilized. Why people don't have to believe that programs are close to human.Why Aleksandra feels removing the need for manually annotated data should be a key focus in the field of AI.Tradeoffs between automation and human labor; why we should use AI to help humans first.How the challenges of automating tasks differ between fields from creative and marketing to calendar management. What Aleksandra thinks of the transformer arms race: why applications are more important than parameters. Why Aleksandra feels xenobots will change the world.Tweetables:“My major discovery [during my PhD] was that people are capable of building robust identities online and can live two lives. They can have their first life and then they can have their second life online, which can be very different from the one they pursue on-site, in the real world.” — @Przegaa [0:06:42]“We can all observe that there is a great boom in NLP. I’m not even sure we should call it NLP anymore. Maybe NLP is an improper phrase. Maybe it’s NLU: natural language understanding.” — @Przegaa [0:14:51]“Transformers seem to be a really big game-changer in the AI space.” — @Przegaa [0:16:40]“I think that using text as a resource for data analytics for businesses in the future is something that we will see happen in the coming two or three years.” — @Przegaa [0:19:46]“AI should not replace you, AI should help you at your work and make your work more effective but also more satisfying for you.” — @Przegaa [0:25:31]Links Mentioned in Today’s Episode:Aleksandra Przegalinska on LinkedInAlexandra Przegalinska on Twitter

Today's guest is Aleksandra Przegalinska PhD, Vice-Rector at Kozminski University, research associate, and Polish futurist. From studying pure philosophy, Aleksandra moved into AI when she started researching natural language processing in the virtual space. We kickstart our discussion with her account of how she ended up where she is now, and how she transferred her skills from philosophy to AI. We hear how Second Life was common in Asia centuries ago, why we are seeing a return to anonymization online, and why Aleksandra feels NLP should be called ‘natural language understanding’. We also discover what the real-world applications of NLP are, and why text processing is under-utilized. Moving onto more philosophical questions around AI and labor, Aleksandra explains how AI should be used to help people and why what is sometimes simple for a human can be immensely complex for AI. We wrap up with Aleksandra’s thoughts on transformers and why their applications are more important than their capabilities, as well as why she is so excited about the idea of xenobots. Key Points From This Episode:<ul><li>An introduction to today’s guest, Aleksandra Przegalinska, PhD.</li><li>What Aleksandra is researching at the moment and how she ended up in academia. </li><li>Insight into the link between her PhD topic and the Metaverse and the transfer of skills from a philosophy degree to AI. </li><li>How a properly built digital ecosystem allows people freedom of expression and other takeaways from Aleksandra’s PhD experience. </li><li>The return to online anonymization that we are currently seeing. </li><li>Aleksandra’s experience of NLP in Second Life and how AI has altered the field. </li><li>The role of NLP in Aleksandra’s work today and why she feels it should be called ‘natural language understanding’. </li><li>The real-world applications of NLP: why text processing is under-utilized. </li><li>Why people don't have to believe that programs are close to human.</li><li>Why Aleksandra feels removing the need for manually annotated data should be a key focus in the field of AI.</li><li>Tradeoffs between automation and human labor; why we should use AI to help humans first.</li><li>How the challenges of automating tasks differ between fields from creative and marketing to calendar management. </li><li>What Aleksandra thinks of the transformer arms race: why applications are more important than parameters. </li><li>Why Aleksandra feels xenobots will change the world.</li></ul>Tweetables:“My major discovery [during my PhD] was that people are capable of building robust identities online and can live two lives. They can have their first life and then they can have their second life online, which can be very different from the one they pursue on-site, in the real world.” — <a href="https://twitter.com/Przegaa" rel="nofollow" target="_blank">@Przegaa</a> [0:06:42]“We can all observe that there is a great boom in NLP. I’m not even sure we should call it NLP anymore. Maybe NLP is an improper phrase. Maybe it’s NLU: natural language understanding.” — <a href="https://twitter.com/Przegaa" rel="nofollow" target="_blank">@Przegaa</a> [0:14:51]“Transformers seem to be a really big game-changer in the AI space.” — <a href="https://twitter.com/Przegaa" rel="nofollow" target="_blank">@Przegaa</a> [0:16:40]“I think that using text as a resource for data analytics for businesses in the future is something that we will see happen in the coming two or three years.” — <a href="https://twitter.com/Przegaa" rel="nofollow" target="_blank">@Przegaa</a> [0:19:46]“AI should not replace you, AI should help you at your work and make your work more effective but also more satisfying for you.” — <a href="https://twitter.com/Przegaa" rel="nofollow" target="_blank">@Przegaa</a> [0:25:31]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/aleksandra-przegalinska-5b17125/" rel="nofollow" target="_blank">Aleksandra Przegalinska on LinkedIn</a><a href="https://twitter.com/Przegaa" rel="nofollow" target="_blank">Alexandra Przegalinska on Twitter</a>

The Future of NLP with AI Professor, Vice Rector, & Researcher Aleksandra Przegalinska

CyberLink's facial recognition technology routinely registers best-in-class accuracy. But how do developers deal with masks, glasses, headphones, or changes in faces over time? How can they prevent spoofing in order to protect identities? And where does computer vision &amp; object detection stop and FRT truly begin? CyberLink Senior Vice President of Global Marketing and US General Manager Richard Carriere and Head of Sales Engineering Craig Campbell join to discuss the endless use cases for facial recognition technology, how CyberLink is improving the tech's accuracy &amp; security, and the ethical considerations of deploying FRT at scale.CyberLink's Ultimate Guide to Facial RecognitionFaceMe Security SDK DemoGet in touch with Cyberlink: FaceMe_US@cyberlink.com

CyberLink's facial recognition technology routinely registers best-in-class accuracy. But how do developers deal with masks, glasses, headphones, or changes in faces over time? How can they prevent spoofing in order to protect identities? And where does computer vision &amp; object detection stop and FRT truly begin? CyberLink Senior Vice President of Global Marketing and US General Manager Richard Carriere and Head of Sales Engineering Craig Campbell join to discuss the endless use cases for facial recognition technology, how CyberLink is improving the tech's accuracy &amp; security, and the ethical considerations of deploying FRT at scale.<a href="https://www.cyberlink.com/faceme/insights/articles/204/Facial-Recognition-at-the-Edge-The-Ultimate-Guide" rel="nofollow" target="_blank">CyberLink's Ultimate Guide to Facial Recognition</a><a href="https://www.dropbox.com/s/3ka50ns1gkpp080/Demo%20of%20FaceMe%20SDK.mp4?dl=0" rel="nofollow" target="_blank">FaceMe Security SDK Demo</a> Get in touch with Cyberlink: FaceMe_US@cyberlink.com

Unpacking Facial Recognition Technology at CyberLink

Oxbotica is a vehicle software company at the forefront of autonomous technology, and today we have a fascinating chat with Ben Upcroft, the Vice President of Technology. Ben explains Oxbotica's mission of enabling industries to make the most of autonomy, and how their technological progress affects real-world situations. We also get into some of the challenges that Oxbotica and the autonomy space, in general, are currently facing, before drilling down on the important concepts of user trust, future implementations, and creating an adaptable core functionality. The last part of today's episode is spent exploring the exciting possibilities of simulated environments for data collection, and the broadening of vehicle experience. Ben talks about the importance of seeking out edge cases to improve their data, and we get into how Oxbotica applies this data across locations. Key Points From This Episode:The constant joy and excitement that Ben feels about his work!An introduction to Oxbotica and its main mission as an organization. How the advances in autonomy translate into real-world progress in safety and efficiency. Handbrakes on the widespread implementation of more autonomy; Ben looks at current limitations. Facilitating trust in the public sphere for something new and the markers of progress. Oxbotica's array of vehicles and goals beyond basic transportation. Constant evolution and the question of staying on course with the rising tide of technology.  How generic features allow for an adaptable core functionality in Oxbotica's vehicles.  Applying data from different environments to boost performance across location types.How Oxbotica focuses on simulated edge cases as a means to broaden the capabilities of their technologies.The amount of real-world data that is necessary for accurate synthesis. Assessing the idea of quality over quantity when it comes to data for AI applications. The areas of the AI field that have Ben most excited right now; emulation of the human brain for the creation of new platforms! Tweetables:“Oxbotica is about deploying and enabling industries to use and leverage autonomy for performance, for efficiency, and safety gains.” — @ben_upcroft “The autonomy that we bring revolutionizes how we move around the globe, through logistics transport, on wheeled vehicles.” — @ben_upcroft “The idea behind the system is that it is modular, enables a core functionality, and I am able to add little extras that customize for a particular domain.” — @ben_upcroft Links Mentioned in Today’s Episode:Ben Upcroft on LinkedInOxboticaBen Upcroft on Twitter

Oxbotica is a vehicle software company at the forefront of autonomous technology, and today we have a fascinating chat with Ben Upcroft, the Vice President of Technology. Ben explains Oxbotica's mission of enabling industries to make the most of autonomy, and how their technological progress affects real-world situations. We also get into some of the challenges that Oxbotica and the autonomy space, in general, are currently facing, before drilling down on the important concepts of user trust, future implementations, and creating an adaptable core functionality. The last part of today's episode is spent exploring the exciting possibilities of simulated environments for data collection, and the broadening of vehicle experience. Ben talks about the importance of seeking out edge cases to improve their data, and we get into how Oxbotica applies this data across locations. Key Points From This Episode:<ul><li>The constant joy and excitement that Ben feels about his work!</li><li>An introduction to Oxbotica and its main mission as an organization. </li><li>How the advances in autonomy translate into real-world progress in safety and efficiency. </li><li>Handbrakes on the widespread implementation of more autonomy; Ben looks at current limitations. </li><li>Facilitating trust in the public sphere for something new and the markers of progress. </li><li>Oxbotica's array of vehicles and goals beyond basic transportation. </li><li>Constant evolution and the question of staying on course with the rising tide of technology.  </li><li>How generic features allow for an adaptable core functionality in Oxbotica's vehicles.  </li><li>Applying data from different environments to boost performance across location types.</li><li>How Oxbotica focuses on simulated edge cases as a means to broaden the capabilities of their technologies.</li><li>The amount of real-world data that is necessary for accurate synthesis. </li><li>Assessing the idea of quality over quantity when it comes to data for AI applications. </li><li>The areas of the AI field that have Ben most excited right now; emulation of the human brain for the creation of new platforms! </li></ul>Tweetables:“Oxbotica is about deploying and enabling industries to use and leverage autonomy for performance, for efficiency, and safety gains.” — <a href="https://twitter.com/ben_upcroft" rel="nofollow" target="_blank">@ben_upcroft</a> “The autonomy that we bring revolutionizes how we move around the globe, through logistics transport, on wheeled vehicles.” — <a href="https://twitter.com/ben_upcroft" rel="nofollow" target="_blank">@ben_upcroft</a> “The idea behind the system is that it is modular, enables a core functionality, and I am able to add little extras that customize for a particular domain.” — <a href="https://twitter.com/ben_upcroft" rel="nofollow" target="_blank">@ben_upcroft</a> Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/ben-upcroft?originalSubdomain=uk" rel="nofollow" target="_blank">Ben Upcroft on LinkedIn</a><a href="https://www.oxbotica.com/" rel="nofollow" target="_blank">Oxbotica</a><a href="https://twitter.com/ben_upcroft" rel="nofollow" target="_blank">Ben Upcroft on Twitter</a>

Universal Autonomy with Oxbotica VP of Technology Ben Upcroft

Key Points From This Episode:Jerome’s background, interest in AI, and how he landed his role at Sama.Social initiatives, training data, and what attracted Jerome to Sama.The shift from focusing on AI models to the importance of data quality.Why academia requires the use of a foundational dataset to compare models.The reason for the early focus on building new AI models.Whether datasets will become open source in the future as models have.The role of annotation in making data meaningful and useful.Challenges of annotating data and different approaches to doing so.The three components of data annotation: models, filtering, and the annotation pipeline.How to hone in on goals for filtering data into valuable subsets that align with your desired outcomes.How to measure a model’s accuracy by focusing on user experience and more.What data drift is and how to prevent it by keeping track of it and retraining models where necessary.How to know that your training data is close enough to your production data.What excites Jerome most about the world of data and annotation.Tweetables:“Most of the successful model architectures are now open source. You can get them anywhere on the web easily, but the one thing that a company is guarding with its life is its data.” — Jerome Pasquero [0:05:36]“If you consider that we now know that a model can be highly sensitive to the quality of the data that are used to train it, there is this natural shift to try to feed models with the best data possible and data quality becomes of paramount importance.” — Jerome Pasquero [0:05:47]“The point of this whole system is that, once you have these three components in place, you can drive your filtering strategy.” — Jerome Pasquero [0:14:06]“You can always get more data later. What you want to avoid is getting yourself into a situation where the data that you are annotating is useless.” — Jerome Pasquero [0:17:30]“A model is like a living thing. You need to take care of it otherwise it is going to degrade, not because it’s degrading internally, but because the data that it is used to seeing has changed.” — Jerome Pasquero [0:25:49]Links Mentioned in Today’s Episode:Jerome Pasquero on LinkedInJerome Pasquero Blog: Top 10 Data Labeling FAQsSama

Key Points From This Episode:<ul><li>Jerome’s background, interest in AI, and how he landed his role at Sama.</li><li>Social initiatives, training data, and what attracted Jerome to Sama.</li><li>The shift from focusing on AI models to the importance of data quality.</li><li>Why academia requires the use of a foundational dataset to compare models.</li><li>The reason for the early focus on building new AI models.</li><li>Whether datasets will become open source in the future as models have.</li><li>The role of annotation in making data meaningful and useful.</li><li>Challenges of annotating data and different approaches to doing so.</li><li>The three components of data annotation: models, filtering, and the annotation pipeline.</li><li>How to hone in on goals for filtering data into valuable subsets that align with your desired outcomes.</li><li>How to measure a model’s accuracy by focusing on user experience and more.</li><li>What data drift is and how to prevent it by keeping track of it and retraining models where necessary.</li><li>How to know that your training data is close enough to your production data.</li><li>What excites Jerome most about the world of data and annotation.</li></ul>Tweetables:“Most of the successful model architectures are now open source. You can get them anywhere on the web easily, but the one thing that a company is guarding with its life is its data.” — Jerome Pasquero [0:05:36]“If you consider that we now know that a model can be highly sensitive to the quality of the data that are used to train it, there is this natural shift to try to feed models with the best data possible and data quality becomes of paramount importance.” — Jerome Pasquero [0:05:47]“The point of this whole system is that, once you have these three components in place, you can drive your filtering strategy.” — Jerome Pasquero [0:14:06]“You can always get more data later. What you want to avoid is getting yourself into a situation where the data that you are annotating is useless.” — Jerome Pasquero [0:17:30]“A model is like a living thing. You need to take care of it otherwise it is going to degrade, not because it’s degrading internally, but because the data that it is used to seeing has changed.” — Jerome Pasquero [0:25:49]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/jeromepasquero/" rel="nofollow" target="_blank">Jerome Pasquero on LinkedIn</a><a href="https://www.sama.com/blog/top-10-data-labeling-frequently-asked-questions" rel="nofollow" target="_blank">Jerome Pasquero Blog: Top 10 Data Labeling FAQs</a><a href="https://www.sama.com/" rel="nofollow" target="_blank">Sama</a>

Upleveling Data Labeling with Sama's Jerome Pasquero

Eric Daimler

Dr. Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. He is also the founder of data integration firm Conexus, and we kick our conversation off with the work he is doing to integrate large heterogeneous data infrastructures. This leads us into an exploration of the concept of compositionality, a structural feature that enables systems to scale, which Dr. Daimler argues is the future of IT infrastructure. We discuss how the way we apply AI to data is constantly changing, with data sources growing quadratically, and how this necessitates an understanding of newer forms of math such as category theory by AI specialists. Towards the end of our discussion, we move on to the subject of the adoption of AI in technologies that lives depend on, and Dr. Daimler gives his recommendation for how to engender trust amongst the larger population. Key Points From This Episode:Experience Dr. Daimler has in AI in an academic, commercial, and governmental capacity.An issue in the choices being made around how to create data that is useful in large organizations.Dr. Daimler’s work bringing heterogeneous data together to influence better business decisions.How much money is wasted on ETL processes and how bad the jobs in that field are.The difference between modularity and compositionality and why the latter is the future of IT infrastructure.How compositionality enables scalability and the need of certain branches of math to justify it.The work Dr. Daimler is doing in the field of compositionality at Connexus.Whether it is crucial to grasp these newer forms of math to achieve AI mastery.How AI systems can integrate into contexts involving human labor and empathy.The need to bring together probabilistic and deterministic AI in life and death contexts.How to get the public to trust and believe in AI-powered tech with the capacity to save lives.What AI practitioners can do to ensure they use their skillset to create a better future.Tweetables:“You can create data that doesn’t add more fidelity to the knowledge you’re looking to gain for better business decisions and that is one of the limitations that I saw expressed in the government and other large organizations.” — @ead [0:01:32]“That’s the world, is compositionality. That is where we are going and the math that supports that, type theory, categorical theory, categorical logic, that’s going to sweep away everything underneath IT infrastructure.” — @ead [0:10:23]“At the trillions of data, a trillion data sources, each growing quadratically, what we need is category theory.” — @ead [0:13:51]“People die and the way to solve that problem when you are talking about these life and death contexts for commercial airplane manufacturers or in energy exploration where the consequences of failure can be disastrous is to bring together the sensibilities of probabilistic AI and deterministic AI.” — @ead [0:24:07]“Circuit breakers, oversight, and data lineage, those are three ways that I would institute a regulatory regime around AI and algorithms that will engender trust amongst the larger population.” — @ead [0:35:12]Links Mentioned in Today’s Episode:Dr. Eric Daimler on LinkedInDr. Eric Daimler on TwitterConexus

Dr. Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. He is also the founder of data integration firm Conexus, and we kick our conversation off with the work he is doing to integrate large heterogeneous data infrastructures. This leads us into an exploration of the concept of compositionality, a structural feature that enables systems to scale, which Dr. Daimler argues is the future of IT infrastructure. We discuss how the way we apply AI to data is constantly changing, with data sources growing quadratically, and how this necessitates an understanding of newer forms of math such as category theory by AI specialists. Towards the end of our discussion, we move on to the subject of the adoption of AI in technologies that lives depend on, and Dr. Daimler gives his recommendation for how to engender trust amongst the larger population. Key Points From This Episode:<ul><li>Experience Dr. Daimler has in AI in an academic, commercial, and governmental capacity.</li><li>An issue in the choices being made around how to create data that is useful in large organizations.</li><li>Dr. Daimler’s work bringing heterogeneous data together to influence better business decisions.</li><li>How much money is wasted on ETL processes and how bad the jobs in that field are.</li><li>The difference between modularity and compositionality and why the latter is the future of IT infrastructure.</li><li>How compositionality enables scalability and the need of certain branches of math to justify it.</li><li>The work Dr. Daimler is doing in the field of compositionality at Connexus.</li><li>Whether it is crucial to grasp these newer forms of math to achieve AI mastery.</li><li>How AI systems can integrate into contexts involving human labor and empathy.</li><li>The need to bring together probabilistic and deterministic AI in life and death contexts.</li><li>How to get the public to trust and believe in AI-powered tech with the capacity to save lives.</li><li>What AI practitioners can do to ensure they use their skillset to create a better future.</li></ul>Tweetables:“You can create data that doesn’t add more fidelity to the knowledge you’re looking to gain for better business decisions and that is one of the limitations that I saw expressed in the government and other large organizations.” — <a href="https://twitter.com/ead" rel="nofollow" target="_blank">@ead</a> [0:01:32]“That’s the world, is compositionality. That is where we are going and the math that supports that, type theory, categorical theory, categorical logic, that’s going to sweep away everything underneath IT infrastructure.” — <a href="https://twitter.com/ead" rel="nofollow" target="_blank">@ead</a> [0:10:23]“At the trillions of data, a trillion data sources, each growing quadratically, what we need is category theory.” — <a href="https://twitter.com/ead" rel="nofollow" target="_blank">@ead</a> [0:13:51]“People die and the way to solve that problem when you are talking about these life and death contexts for commercial airplane manufacturers or in energy exploration where the consequences of failure can be disastrous is to bring together the sensibilities of probabilistic AI and deterministic AI.” — <a href="https://twitter.com/ead" rel="nofollow" target="_blank">@ead</a> [0:24:07]“Circuit breakers, oversight, and data lineage, those are three ways that I would institute a regulatory regime around AI and algorithms that will engender trust amongst the larger population.” — <a href="https://twitter.com/ead" rel="nofollow" target="_blank">@ead</a> [0:35:12]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/ericdaimler/" rel="nofollow" target="_blank">Dr. Eric Daimler on LinkedIn</a><a href="https://twitter.com/ead" rel="nofollow" target="_blank">Dr. Eric Daimler on Twitter</a><a href="https://conexus.com/" rel="nofollow" target="_blank">Conexus</a>

A Highly Compositional Future with Dr. Eric Daimler

Slater Victoroff

Irrespective of the application or the technology, a common problem among AI professionals always seems to be data. Is there enough of it? What do we prioritize? Is it clean? How do we annotate it? Today’s guest, however, believes that AI is not data-limited but compute-limited. Joining us to share some very interesting insights on the subject matter is Slater Victoroff, Founder and Chief Technology Officer at Indico, an unstructured data platform that enables users to build innovative, mission-critical enterprise workflows that maximize opportunity, reduce risk, and accelerate revenue. Slater explains how he came to co-found Indico Data despite a previous admission that he believed that deep learning was dead. He explains what happened that unlocked deep learning, how he was influenced by the AlexNet paper, and how Indico goes about solving the problem of unstructured data.  Key Points From This Episode:How Slater Victoroff came to found Indico and how he came to understand the value of deep learning.How Indico’s approach has evolved over time. What happened that unlocked deep learning and what inspired Slater to incorrectly believe it was over before it began.The event of the AlexNet paper published in 2012 and its influence on deep learning. Insight into the application of deep learning at Indico and their focus on human-machine interaction.What is meant by “solving the problem of unstructured data”.How Indico is reducing the price of building unstructured use cases.Thoughts on whether or not the downsizing of investment and hardware requirements of AI technology is a necessary outcome. The surprisingly low percentage of projects that succeed. Why Slater believes that AI today is not data-limited but compute-limited. Why resolving compute won’t remove the need for clean annotated data.Whether or not determining which data to prioritize is still a computer vision problem. How the refocus into transfer learning has affected Indico’s approach. What Slater is really excited about in the short and medium-term future of AI.Tweetables:“Deep learning is particularly useful for these sorts of unstructured use-cases, image, text, audio. And it’s an incredibly powerful tool that allows us to attack these use cases in a way that we fundamentally weren’t able to otherwise.” — @sl8rv [0:02:44]“By and large, AI today is not data-limited, it is compute limited. It is the only field in software that you can say that.” — @sl8rv [0:19:27]“That’s really this next frontier though: This is where transfer learning is going next, this idea ‘Can I take visual information and language information? Can I understand that together in a comprehensive way, and then give you one interface to learn on top of that consolidated understanding of the world?’” — @sl8rv [0:26:05]“We have gone from asking the question ‘Is transfer learning possible?’ to asking the question ‘What does it take to be the best in the world at transfer learning?’”. — @sl8rv [0:27:03]Links Mentioned in Today’s Episode:"Visualizing and Understanding Convolutional Networks"Slater VictoroffSlater Victoroff on TwitterIndico Data

Irrespective of the application or the technology, a common problem among AI professionals always seems to be data. Is there enough of it? What do we prioritize? Is it clean? How do we annotate it? Today’s guest, however, believes that AI is not data-limited but compute-limited. Joining us to share some very interesting insights on the subject matter is Slater Victoroff, Founder and Chief Technology Officer at Indico, an unstructured data platform that enables users to build innovative, mission-critical enterprise workflows that maximize opportunity, reduce risk, and accelerate revenue. Slater explains how he came to co-found Indico Data despite a previous admission that he believed that deep learning was dead. He explains what happened that unlocked deep learning, how he was influenced by the AlexNet paper, and how Indico goes about solving the problem of unstructured data.  Key Points From This Episode:<ul><li>How Slater Victoroff came to found Indico and how he came to understand the value of deep learning.</li><li>How Indico’s approach has evolved over time. </li><li>What happened that unlocked deep learning and what inspired Slater to incorrectly believe it was over before it began.</li><li>The event of the AlexNet paper published in 2012 and its influence on deep learning. </li><li>Insight into the application of deep learning at Indico and their focus on human-machine interaction.</li><li>What is meant by “solving the problem of unstructured data”.</li><li>How Indico is reducing the price of building unstructured use cases.</li><li>Thoughts on whether or not the downsizing of investment and hardware requirements of AI technology is a necessary outcome. </li><li>The surprisingly low percentage of projects that succeed. </li><li>Why Slater believes that AI today is not data-limited but compute-limited. </li><li>Why resolving compute won’t remove the need for clean annotated data.</li><li>Whether or not determining which data to prioritize is still a computer vision problem. </li><li>How the refocus into transfer learning has affected Indico’s approach. </li><li>What Slater is really excited about in the short and medium-term future of AI.</li></ul>Tweetables:“Deep learning is particularly useful for these sorts of unstructured use-cases, image, text, audio. And it’s an incredibly powerful tool that allows us to attack these use cases in a way that we fundamentally weren’t able to otherwise.” — <a href="https://twitter.com/sl8rv" rel="nofollow" target="_blank">@sl8rv</a> [0:02:44]“By and large, AI today is not data-limited, it is compute limited. It is the only field in software that you can say that.” — <a href="https://twitter.com/sl8rv" rel="nofollow" target="_blank">@sl8rv</a> [0:19:27]“That’s really this next frontier though: This is where transfer learning is going next, this idea ‘Can I take visual information and language information? Can I understand that together in a comprehensive way, and then give you one interface to learn on top of that consolidated understanding of the world?’” — <a href="https://twitter.com/sl8rv" rel="nofollow" target="_blank">@sl8rv</a> [0:26:05]“We have gone from asking the question ‘Is transfer learning possible?’ to asking the question ‘What does it take to be the best in the world at transfer learning?’”. — <a href="https://twitter.com/sl8rv" rel="nofollow" target="_blank">@sl8rv</a> [0:27:03]Links Mentioned in Today’s Episode:<a href="https://arxiv.org/abs/1311.2901" rel="nofollow" target="_blank">"Visualizing and Understanding Convolutional Networks"</a><a href="https://www.linkedin.com/in/slatervictoroff/" rel="nofollow" target="_blank">Slater Victoroff</a><a href="https://twitter.com/sl8rv" rel="nofollow" target="_blank">Slater Victoroff on Twitter</a><a href="https://indicodata.ai/" rel="nofollow" target="_blank">Indico Data</a>

Transfer Learning & Solving Unstructured Data with Indico Data CTO Slater Victoroff

The innovations that drive space exploration not only aid us in discovering other worlds, but they also benefit us right here on earth. Today’s guest is Shelli Brunswick, who joins us to talk about the role of AI in space exploration and how the ‘space ecosystem’ can create jobs and career opportunities on Earth. Shelli is the COO at the Space Foundation and was selected as the 2020 Diversity and Inclusion Officer and Role Model of the Year by WomenTech Network and a Woman of Influence by the Colorado Springs Business Journal. We kick our discussion off by hearing how Shelli got to her current role and what it entails. She talks about how connected the space industry has become to many others, and how this amounts to a ‘space ecosystem’, a rich field for opportunity, innovation, and commerce. We talk about the many innovations that have stemmed from space exploration, the role they play on this planet, and the possibilities this holds as the space ecosystem continues to grow. She gets into the programs at the Space Foundation to encourage entrepreneurship and the ways that innovators can direct their efforts to participate in the space ecosystem. We also explore the many ways that AI plays a role in the space ecosystem and how the AI being utilized across industries on earth will find later applications in space. Tune in today to learn more!Key Points From This Episode:Shelli’s background and career journey to her role as COO at the Space Foundation.What made Shelli decide that she wanted to occupy her current role.Using space innovation to benefit us on earth; the definition of ‘space ecosystem’.The many industries and countries that participate in the space ecosystem.Using the Center for Innovation and Education to create diversity and opportunities across industries.The role of AI in the growing space ecosystem; satellites, space exploration, GPS, and more.How AI regulates hydroponic agriculture on earth and can in space too.The many challenges of living off-world and the role AI will play.How entrepreneurship in the context of the space ecosystem is taught at the Space Commerce Institute.The spinoff commercialization and innovations that come from the space industry.How AI practitioners can point their expertise and technology into the space ecosystem.What AI will change in the world of the future and the effects of this on jobs.Tweetables:“What we really need to do is wrap it back to how that space technology, that space innovation, that investing in space, benefits us right here on planet earth and creates jobs and career opportunities.” — @shellibrunswick [0:05:52]“The sky is not the limit [for the role that] AI can play in this.” — @shellibrunswick [0:12:12]“It is the Wild West. It is exciting and, if you want to be an entrepreneur, buckle in because there is an opportunity for you!” — @shellibrunswick [0:20:36]“You can sit in the Space Symposium sessions and hear what are those governments investing in, what are those companies investing in, and how can you as an entrepreneur create a product or service that’s related to AI that helps them fill that capability gap?” — @shellibrunswick [0:22:00]Links Mentioned in Today’s Episode:Shelli Brunswick on LinkedInShelli Brunswick on TwitterThe Space FoundationThe Center for Innovation and EducationSpace Symposium

The innovations that drive space exploration not only aid us in discovering other worlds, but they also benefit us right here on earth. Today’s guest is Shelli Brunswick, who joins us to talk about the role of AI in space exploration and how the ‘space ecosystem’ can create jobs and career opportunities on Earth. Shelli is the COO at the Space Foundation and was selected as the 2020 Diversity and Inclusion Officer and Role Model of the Year by WomenTech Network and a Woman of Influence by the Colorado Springs Business Journal. We kick our discussion off by hearing how Shelli got to her current role and what it entails. She talks about how connected the space industry has become to many others, and how this amounts to a ‘space ecosystem’, a rich field for opportunity, innovation, and commerce. We talk about the many innovations that have stemmed from space exploration, the role they play on this planet, and the possibilities this holds as the space ecosystem continues to grow. She gets into the programs at the Space Foundation to encourage entrepreneurship and the ways that innovators can direct their efforts to participate in the space ecosystem. We also explore the many ways that AI plays a role in the space ecosystem and how the AI being utilized across industries on earth will find later applications in space. Tune in today to learn more!Key Points From This Episode:<ul><li>Shelli’s background and career journey to her role as COO at the Space Foundation.</li><li>What made Shelli decide that she wanted to occupy her current role.</li><li>Using space innovation to benefit us on earth; the definition of ‘space ecosystem’.</li><li>The many industries and countries that participate in the space ecosystem.</li><li>Using the Center for Innovation and Education to create diversity and opportunities across industries.</li><li>The role of AI in the growing space ecosystem; satellites, space exploration, GPS, and more.</li><li>How AI regulates hydroponic agriculture on earth and can in space too.</li><li>The many challenges of living off-world and the role AI will play.</li><li>How entrepreneurship in the context of the space ecosystem is taught at the Space Commerce Institute.</li><li>The spinoff commercialization and innovations that come from the space industry.</li><li>How AI practitioners can point their expertise and technology into the space ecosystem.</li><li>What AI will change in the world of the future and the effects of this on jobs.</li></ul>Tweetables:“What we really need to do is wrap it back to how that space technology, that space innovation, that investing in space, benefits us right here on planet earth and creates jobs and career opportunities.” — <a href="https://twitter.com/shellibrunswick" rel="nofollow" target="_blank">@shellibrunswick</a> [0:05:52]“The sky is not the limit [for the role that] AI can play in this.” — <a href="https://twitter.com/shellibrunswick" rel="nofollow" target="_blank">@shellibrunswick</a> [0:12:12]“It is the Wild West. It is exciting and, if you want to be an entrepreneur, buckle in because there is an opportunity for you!” — <a href="https://twitter.com/shellibrunswick" rel="nofollow" target="_blank">@shellibrunswick</a> [0:20:36]“You can sit in the Space Symposium sessions and hear what are those governments investing in, what are those companies investing in, and how can you as an entrepreneur create a product or service that’s related to AI that helps them fill that capability gap?” — <a href="https://twitter.com/shellibrunswick" rel="nofollow" target="_blank">@shellibrunswick</a> [0:22:00]Links Mentioned in Today’s Episode:<a href="https://www.linkedin.com/in/shellibrunswick/" rel="nofollow" target="_blank">Shelli Brunswick on LinkedIn</a><a href="https://twitter.com/shellibrunswick" rel="nofollow" target="_blank">Shelli Brunswick on Twitter</a><a href="https://www.spacefoundation.org/" rel="nofollow" target="_blank">The Space Foundation</a><a href="https://www.spacefoundation.org/cie/" rel="nofollow" target="_blank">The Center for Innovation and Education</a><a href="https://www.spacesymposium.org/" rel="nofollow" target="_blank">Space Symposium</a>

AI Opportunity in the Space Ecosystem with Space Foundation COO Shelli Brunswick

How AI Happens

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