Intel on AI

Evolution, Technology, and the Brain – Intel on AI Season 3, Episode 13 Aug 17, 2022

In this episode of Intel on AI host Amir Khosrowshahi talks with Jeff Lichtman about the evolution of technology and mammalian brains.

Jeff Lichtman is the Jeremy R. Knowles Professor of Molecular and Cellular Biology at Harvard. He received an AB from Bowdoin and an M.D. and Ph.D. from Washington University, where he worked for thirty years before moving to Cambridge. He is now a member of Harvard’s Center for Brain Science and director of the Lichtman Lab, which focuses on connectomics— mapping neural connections and understanding their development.

In the podcast episode Jeff talks about why researching the physical structure of brain is so important to advancing science. He goes into detail about Brainbrow—a method he and Joshua Sanes developed to illuminate and trace the “wires” (axons and dendrites) connecting neurons to each other. Amir and Jeff discuss how the academic rivalry between Santiago Ramón y Cajal and Camillo Golgi pioneered neuroscience research. Jeff describes his remarkable research taking nanometer slices of brain tissue, creating high-resolution images, and then digitally reconstructing the cells and synapses to get a more complete picture of the brain. The episode closes with Jeff and Amir discussing theories about how the human brain learns and what technologists might discover from the grand challenge of mapping the entire nervous system.

Academic research discussed in the podcast episode:

Meta-Learning for Robots – Intel on AI Season 3, Episode 12 Jun 15, 2022

In this episode of Intel on AI host Amir Khosrowshahi and co-host Mariano Phielipp talk with Chelsea Finn about machine learning research focused on giving robots the capability to develop intelligent behavior.

Chelsea is Assistant Professor in Computer Science and Electrical Engineering at Stanford University, whose Stanford IRIS (Intelligence through Robotic Interaction at Scale) lab is closely associated with the Stanford Artificial Intelligence Laboratory (SAIL). She received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley, where she worked with Pieter Abbeel and Sergey Levine.

In the podcast episode Chelsea explains the difference between supervised learning and reinforcement learning. She goes into detail about the different kinds of new reinforcement algorithms that can aid robots to learn more autonomously. Chelsea talks extensively about meta-learning—the concept of helping robots learn to learn—and her efforts to advance model-agnostic meta-learning (MAML). The episode closes with Chelsea and Mariano discussing the intersection of natural language processing and reinforcement learning. The three also talk about the future of robotics and artificial intelligence, including the complexity of setting up robotic reward functions for seemingly simple tasks.

Academic research discussed in the podcast episode:

AI, Social Media, and Political Influence – Intel on AI Season 3, Episode 11 May 25, 2022

In this episode of Intel on AI host Amir Khosrowshahi talks with Joshua Tucker about using artificial intelligence to study the influence social media has on politics.

Joshua is professor of politics at New York University with affiliated appointments in the department of Russian and Slavic Studies and the Center for Data Science. He is also the director of the Jordan Center for the Advanced Study of Russia and co-director of the Center for Social Media and Politics. He was a co-author and editor of an award-winning policy blog at The Washington Post and has published several books, including his latest, where he is co-editor, titled Social Media and Democracy: The State of the Field, Prospects for Reform from Cambridge University Press.

In the podcast episode, Joshua discusses his background in researching mass political behavior, including Colored Revolutions in Eastern Europe. He talks about how his field of study changed after working with his then PhD student Pablo Barberá (now a professor at the University of Southern California), who proposed a method whereby researchers could estimate people's partisanship based on the social networks in which they had enmeshed themselves. Joshua describes the limitations researchers often have when trying to study data on various platforms, the challenges of big data, utilizing NYU’s Greene HPC Cluster, and the impact that the leak of the Facebook Papers had on the field. He also describes findings regarding people who are more prone to share material from fraudulent media organizations masquerading as news outlets and how researchers like Rebekah Tromble (Director of the Institute for Data, Democracy and Politics at George Washington University) are working with government entities like the European Union on balancing public research with data privacy. The episode closes with Amir and Joshua discussing disinformation campaigns in the context of the Russo-Ukrainian War.

Academic research discussed in the podcast episode:

Machine Learning and Molecular Simulation – Intel on AI Season 3, Episode 10 May 04, 2022

In this episode of Intel on AI host Amir Khosrowshahi talks with Ron Dror about breakthroughs in computational biology and molecular simulation.

Ron is an Associate Professor of Computer Science in the Stanford Artificial Intelligence Lab, leading a research group that uses machine learning and molecular simulation to elucidate biomolecular structure, dynamics, and function, and to guide the development of more effective medicines. Previously, Ron worked on the Anton supercomputer at D.E. Shaw Research after earning degrees in the fields of electrical engineering, computer science, biological sciences, and mathematics from MIT, Cambridge, and Rice. His groundbreaking research has been published in journals such as Science and Nature, presented at conferences like Neural Information Processing Systems (NeurIPS), and won awards from the Association of Computing Machinery (ACM) and other organizations.

In the podcast episode, Ron talks about his work with several important collaborators, his interdisciplinary approach to research, and how molecular modeling has improved over the years. He goes into detail about the gen-over-gen advancements made in the Anton supercomputer, including its software, and his recent work at Stanford with molecular dynamics simulations and machine learning. The podcast closes with Amir asking detailed questions about Ron and his team’s recent paper concerning RNA structure prediction that was featured on the cover of Science.

Academic research discussed in the podcast episode:

AI and Nanocomputing - Intel on AI Season 3, Episode 9 Mar 30, 2022

In this episode of Intel on AI host Amir Khosrowshahi, assisted by Dmitri Nikonov, talks with Jean Anne Incorvia about the use of new physics in nanocomputing, specifically with spintronic logic and 2D materials.

Jean is an Assistant Professor and holds the Fellow of Advanced Micro Devices Chair in Computer Engineering in the Department of Electrical and Computer Engineering at The University of Texas at Austin, where she directs the Integrated Nano Computing Lab.

Dimitri is a Principal Engineer in the Components Research at Intel. He holds a Master of Science in Aeromechanical Engineering from the Moscow Institute of Physics and Technology and a Ph.D. from Texas A&M. Dimitri works in the discovery and simulation of nanoscale logic devices and manages joint research projects with multiple universities. He has authored dozens of research papers in the areas of quantum nanoelectronics, spintronics, and non-Boolean architectures.

In the episode Jean talks about her background with condensed matter physics and solid-state electronics. She explains how magnetic properties and atomically thin materials, like graphene, can be leveraged at nanoscale for beyond-CMOS computing. Jean goes into detail about domain wall magnetic tunnel junctions and why such devices might have a lower energy cost than the modern process of encoding information in charge. She sees these new types of devices to be compatible with CMOS computing and part of a larger journey toward beyond-von Neumann architecture that will advance the evolution of artificial intelligence, neural networks, deep learning, machine learning, and neuromorphic computing.

The episode closes with Jean, Amir, and Dimitri talking about the broadening definition of quantum computing, existential philosophy, and AI ethics.

Academic research discussed in the podcast episode:

Designing Molecules with AI – Intel on AI Season 3, Episode 8 Feb 16, 2022

In this episode of Intel on AI hosts Amir Khosrowshahi and Santiago Miret talk with Alán Aspuru-Guzik about the chemistry of computing and the future of materials discovery.

Alán is a professor of chemistry and computer science at the University of Toronto, a Canada 150 Research Chair in theoretical chemistry, a CIFAR AI Chair at the Vector Institute, and a CIFAR Lebovic Fellow in the biology-inspired Solar Energy Program. Alán also holds a Google Industrial Research Chair in quantum computing and is the co-founder of two startups, Zapata Computing and Kebotix.

Santiago Miret is an AI researcher in Intel Labs, who has an active research collaboration Alán. Santiago studies at the intersection of AI and the sciences, as well as the algorithmic development of AI for real-world problems.

In the first half of the episode, the three discuss accelerating molecular design and building next generation functional materials. Alán talks about his academic background with high performance computing (HPC) that led him into the field of molecular design. He goes into detail about building a “self-driving lab” for scientific experimentation, which, coupled with advanced automation and robotics, he believes will help propel society to move beyond the era of plastics and into the era of materials by demand. Alán and Santiago talk about their research collaboration with Intel to build sophisticated model-based molecular design platforms that can scale to real-world challenges. Alán talks about the Acceleration Consortium and the need for standardization research to drive greater academic and industry collaborations for self-driving laboratories.

In the second half of the episode, the three talk about quantum computing, including developing algorithms for quantum dynamics, molecular electronic structure, molecular properties, and more. Alán talks about how a simple algorithm based on thinking of the quantum computer like a musical instrument is behind the concept of the variational quantum eigensolver, which could hold promising advancements alongside classical computers. Amir, and Santiago close the episode by talking about the future of research, including projects at DARPA, oscillatory computing, quantum machine learning, quantum autoencoders, and how young technologists entering the field can advance a more equitable society.

Academic research discussed in the podcast episode:

Learning with AI – Intel on AI Season 3, Episode 7 Jan 26, 2022

In this episode of Intel on AI host Amir Khosrowshahi and Milena Marinova talk about using artificial intelligence for professional learning.

Milena is currently the Vice President of Data and AI Solutions at Microsoft. At the time of recording this podcast (April 2021), Milena was the visionary and driving force behind the award-winning AI calculus tutoring application Aida and its capabilities platform in the AI Products & Solutions Group, which she founded and led at Pearson. Bringing over 15 years of experience and knowledge in machine learning, neural networks, computer vision, and the commercialization of new technologies, Milena’s background includes an MBA from IMD in Lausanne, Switzerland and a B.Sc. with Honors in Computer Science from Caltech. She is a passionate advocate for innovation and has been a Venture Partner with Atlantic Bridge Capital, helping with AI investments and portfolio companies. Milena is also a co-founder and advisor to several startups in Europe and the US and has previously held management positions at the startup incubator Idealab, as well as executive roles at Intel.

In the podcast episode Amir and Milena discuss some of the challenges of developing artificial intelligence products, going from academic research into commercial deployment, and the importance of data policy by design. Milena describes some of the lessons she’s learned over the years.

Academic research discussed in the podcast episode:

Computing with DNA – Intel on AI Season 3, Episode 6 Dec 22, 2021

In this episode of Intel on AI host Amir Khosrowshahi and Luis Ceze talk about building better computer architectures, molecular biology, and synthetic DNA.

Luis Ceze is the Lazowska Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, Co-founder and CEO at OctoML, and Venture Partner at Madrona Venture Group. His research focuses on the intersection between computer architecture, programming languages, machine learning and biology. His current research focus is on approximate computing for efficient machine learning and DNA-based data storage. He co-directs the Molecular Information Systems Lab (misl.bio) and the Systems and Architectures for Machine Learning lab (sampl.ai). He has co-authored over 100 papers in these areas, and had several papers selected as IEEE Micro Top Picks and CACM Research Highlights. His research has been featured prominently in the media including New York Times, Popular Science, MIT Technology Review, Wall Street Journal, among others. He is a recipient of an NSF CAREER Award, a Sloan Research Fellowship, a Microsoft Research Faculty Fellowship, the 2013 IEEE TCCA Young Computer Architect Award, the 2020 ACM SIGARCH Maurice Wilkes Award and UIUC Distinguished Alumni Award.

In the episode, Amir and Luis talk about DNA storage, which has the potential to be a million times denser than solid state storage today. Luis goes into detail about the process he and fellow researchers at the University of Washington along with a team from Microsoft went through in order to store the high-definition music video “This Too Shall Pass” by the band OK Go onto DNA. Luis also discusses why enzymatic synthesis of DNA might potentially be environmentally sustainable, the advancements being made in similarity searches, and his role in creating the open source Apache TVM project that aims to use machine learning to find the most efficient hardware and software combination optimizations. Amir and Luis end the episode talking about why multi-technology systems with electronics, photonics, molecular systems, and even quantum components could be the future of compute.

Academic research discussed in the podcast episode:

Stephen Wolfram on the Current State of Artificial Intelligence – Intel on AI Season 3, Episode 5 Dec 15, 2021

In this episode of Intel on AI host Amir Khosrowshahi talks with Stephen Wolfram about the current state of artificial intelligence. Stephen is the founder and CEO of Wolfram Research, maker of the Wolfram Mathematica software system and WolframAlpha computational knowledge engine, author of A New Kind of Science, and most recently originator of the Wolfram Physics Project, which is a collaborative effort to find the fundamental theory of physics.

In the podcast episode, Stephen talks about the computational universe and the idea that even simple programs possibly have sophisticated abilities under the Principle of Computational Equivalence, but that these abilities are perceived to be useless to humans and therefore underexplored. He discusses the need for shared computational languages that will allow people and machines to mine the wealth of available historic data so that it can be translated into useable knowledge.

Amir and Stephen talk about a number of subjects during their two-hour conversation, including Emanuel Kant, Noam Chomsky, if aliens might view a completely different part of physical reality than humans, encoding values for AI content ranking, and why Stephen left academia to develop his own research institute. Stephen discusses his predictions about the limitations of quantum computing, the potential of computing at the molecular scale, and what comes after semiconductor processing. He also explains why Einstein’s theory of relatively and spacetime is misunderstood. Amir asks Stephen to explain how multiway systems and the biology of neuroscience can be viewed in harmony.

Academic research discussed in the podcast episode:

Moving Beyond CMOS – Intel on AI Season 3, Episode 4 Dec 08, 2021

In this episode of Intel on AI host Amir Khosrowshahi, assisted by Dmitri Nikonov, talks with Ian Young about Intel’s long-term research to develop more energy-efficient computing based on exploratory materials and devices as well as non-traditional architectures.

Ian is Senior Fellow at Intel and the Director of the Exploratory Integrated Circuits in the Components Research. Ian was one of the key players in the advancement of dynamic and static random-access memory (DRAM, SRAM), and the integration of the bipolar junction transistor and complementary metal-oxide-semiconductor (CMOS) gate into a single integrated circuit (BiCMOS). He developed the original Phase Locked Loop (PLL) based clocking circuit in a microprocessor while working at Intel, contributing to massive improvements in computing power.

Dimitri is a Principal Engineer in the Components Research at Intel. He works in the discovery and simulation of nanoscale logic devices and manages joint research projects with multiple universities. Both Ian and Dmitri have authored dozens of research papers, many together, in the areas of quantum nanoelectronics, spintronics, and non-Boolean architectures.

In the podcast episode, the three talk about moving beyond CMOS architecture, which is limited by current density and heat. By exploring new materials, the hope is to make significant improvements in energy efficiency that could greatly expand the performance of deep neural networks and other types of computing. The three discuss the possible applications of ferroelectric materials, quantum tunneling, spintronics, non-volatile memory and computing, and silicon photonics.

Ian talks about some of the current material challenges he and others are trying to solve, such as meeting operational performance targets and creating pristine interfaces, which mimic some of the same hurdles Intel executives Gordon Moore, Robert Noyce, and Andrew Grove faced in the past. He describes why he believes low-voltage, magneto-electric spin orbit (MESO) devices with quantum multiferroics (materials with coupled magnetic and ferroelectric order) have the most potential for improvement and wide-spread industry adoption.

Academic research discussed in the podcast episode:

The Need for New Deep Learning Architectures – Intel on AI Season 3, Episode 3 Dec 01, 2021

In this episode of Intel on AI host Amir Khosrowshahi and Yoshua Bengio talk about structuring future computers on the underlying physics and biology of human intelligence. Yoshua is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (Mila). In 2018 Yoshua received the ACM A.M. Turing Award with Geoffrey Hinton and Yann LeCun.

In the episode, Yoshua and Amir discuss causal representation learning and out-of-distribution generalization, the limitations of modern hardware, and why current models are exponentially increasing amounts of data and compute only to find slight improvements. Yoshua also goes into detail about equilibrium propagation—a learning algorithm that bridges machine learning and neuroscience by computing gradients closely matching those of backpropagation. Yoshua and Amir close the episode by talking about academic publishing, sharing information, and the responsibility to make sure artificial intelligence (AI) will not be misused in society, before touching briefly on some of the projects Intel and Mila are collaborating on, such as using parallel computing for the discovery of synthesizable molecules.

Academic research discussed in the podcast episode:

Biological Intelligence and the Limitations of Deep Neural Networks – Intel on AI Season 3, Episode 2 Nov 03, 2021

In this episode of Intel on AI host Amir Khosrowshahi and Melanie Mitchell talk about the paradox of studying human intelligence and the limitations of deep neural networks. Melanie is the Davis Professor of Complexity at the Santa Fe Institute, former professor of Computer Science at Portland State University, and the author/editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems, including Complexity: A Guided Tour and Artificial Intelligence: A Guide for Thinking Humans.

In the episode, Melanie and Amir discuss how intelligence emerges from the substrate of neurons and why being able to perceive abstract similarities between different situations via analogies is at the core of cognition. Melanie goes into detail about deep neural networks using spurious statistical correlations, the distinction between generative and discriminative systems and machine learning, and the theory that a fundamental part of the human brain is trying to predict what is going to happen next based on prior experience. She also talks about creating the Copycat software, the dangers of artificial intelligence (AI) being easy to manipulate even in very narrow areas, and the importance of getting inspiration from biological intelligence.

Academic research discussed in the podcast episode:

From Jumping Spiders to Silicon: Neuroscience and the Future of Computing - Intel on AI Season 3, Episode 1 Sep 29, 2021

In this episode of Intel on AI host Amir Khosrowshahi and Bruno Olshausen talk about neuroscience and the future of computing. Bruno is a professor at Berkeley with appointments in the Helen Wills Neuroscience Institute and School of Optometry. He is also the director of the Redwood center for Theoretical Neuroscience, which brings the fields of physics, mathematics, engineering, and neuroscience together to study how networks of neurons in the brain process information.

In the episode, Bruno and Amir discuss research about recording large populations of neurons, hyperdimensional computing, and discovering new types of engineering principles. Bruno talks about how in order to understand intelligence and its underpinnings, we have to understand the origins of intelligence and perceptual psychology outside of mammalian brains. He points to the sophisticated visual system of jumping spiders as inspiration for developing systems that use low energy in a small form factor. By better understanding the origins of perception and other biophysical structures, Bruno theorizes the artificial intelligence field may evolve beyond image recognition tasks of current neural networks. Bruno and Amir close the episode by talking about the elementary units of computation, the idea of “listening to silicon” as proposed by Carver Mead, neuromorphic computing, and what the future of research might hold.

Academic research discussed in the podcast episode:

The AI of Tomorrow – Intel on AI – Season 2, Episode 17 Mar 08, 2021

In this episode of Intel on AI host Abigail Hing Wen, Intel AI Tech Evangelist and New York Times Best Selling Author, passes the hosting mantle to Amir Khosrowshahi, Intel Vice President. The two talk about lessons learned from guests across Season 2 of the podcast and what the AI of tomorrow might be.

Abigail shares about some exciting next steps for her. Amir discusses his background studying neurobiology and theoretical physics, his research in computational neuroscience and mammalian visual systems at UC Berkeley, his work at Intel following the acquisition of Nervana, and his plans for hosting Season 3 of the podcast.

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AI and Government with US Congresswoman Robin Kelly – Intel on AI Season 2, Episode 16 Feb 18, 2021

In this episode of Intel on AI guest US Congresswoman Robin Kelly talks with host Abigail Hing Wen, Intel AI Tech Evangelist and New York Times best-selling author, about artificial intelligence (AI) and the United States government.

Congresswoman Kelly talks about how she became involved in AI policy, introducing a bipartisan resolution to create a national AI strategy with Will Hurd (R-Texas), and educating other Congress members about the field. The two also talk about the importance of training new talent in order for America to stay competitive in a global market and why ethics in AI is crucial when considering regulation.

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Genentech: Biomedicine Meets AI – Intel on AI Season 2, Episode 15 Feb 11, 2021

In this episode of Intel on AI guest Aviv Regev, Executive Vice President of Genentech Research and Early Development, talks with host Abigail Hing Wen, Intel AI Tech Evangelist and New York Times best-selling author, about biomedicine and artificial intelligence (AI).

The two discuss Aviv’s work on circuitry in cells, the future of experimental biology, why increasing the diversity of data is key to creating algorithms that can find patterns in genomic variants, and how strengthening global networks will help society better prepare for the next pandemic. Hear more from Aviv in a special episode of Genentech’s science podcast “Studying the Symphony of Cells.”

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Public Policy with Partnership on AI’s Terah Lyons – Intel on AI Season 2 – Episode 14 Jan 13, 2021

In this episode of Intel on AI guest Terah Lyons, Executive Director of Partnership on AI, talks with host Abigail Hing Wen, Intel AI Tech Evangelist and New York Times best-selling author, about her previous work as Policy Advisor to the United States Chief Technology Officer Megan Smith in President Barack Obama’s Office of Science and Technology Policy, her thoughts on the role policymakers should play in the field of artificial intelligence (AI), and the ongoing efforts of the Partnership on AI.

The two discuss how organizations can align their values and prioritize incentives around developing AI that helps workers, the importance of measuring such outcomes, and why practical frameworks for AI can help people outside the field better understand the benefits of AI.

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DeepMind: From the Lab to the World – Intel on AI – Season 2, Episode 13 Dec 23, 2020

In this episode of Intel on AI guest Colin Murdoch, Senior Business Director at DeepMind, talks with host Abigail Hing Wen, Intel AI Tech Evangelist and New York Times best-selling author, about text-to-speech system WaveNet, the recent breakthrough with AlphaFold, the potential for artificial intelligence to solve energy challenges, and how Google adopts cutting-edge research into a number of services.

The two also discuss examples like AlphaGo, GraphNet, advancements in Android products, and what the future of artificial general intelligence might look like.

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Algorithmic Fairness with Alice Xiang – Intel on AI – Season 2, Episode 12 Dec 16, 2020

In this episode of Intel on AI guest Alice Xiang, Head of Fairness, Transparency, and Accountability Research at the Partnership on AI, talks with host Abigail Hing Wen, Intel AI Tech Evangelist and New York Times best-selling author, about algorithmic fairness—the study of how algorithms might systemically perform better or worse for certain groups of people and the ways in which historical biases or other systemic inequities might be perpetuated by algorithmic systems.

The two discuss the lofty goals of the Partnership on AI, why being able to explain how a model arrived at a specific decision is important for the future of AI adoption, and the proliferation of criminal justice risk assessment tools.

Follow Alice on Twitter: twitter.com/alicexiang Follow Abigail on Twitter: twitter.com/abigailhingwen Learn more about Intel’s work in AI: intel.com/ai

Emotion and AI with Rana el Kaliouby – Intel on AI – Season 2, Episode 11 Dec 09, 2020

In this episode of Intel on AI guest Rana el Kaliouby, Ph.D., cofounder and CEO of Affectiva, and author of Girl Decoded: A Scientist’s Quest to Reclaim Our Humanity by Bringing Emotional Intelligence to Technology, talks with host Abigail Hing Wen, Intel AI Tech Evangelist and New York Times best-selling author, about emotional intelligence (EQ)—a person’s ability to sense emotional and cognitive states and behaviors, and be able to adapt in real-time based on that information.

The two talk about Rana’s journey to founding Affectiva with MIT professor Rosalind Picard, Sc.D, the future implementations of EQ in technology, such as customer service and autonomous driving, and why such systems need to have clearly defined data policies.

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Inside Intel Labs: Human and AI Collaboration – Intel on AI – Season 2, Episode 10 Dec 02, 2020

In this episode of Intel on AI guests Lama Nachman, Intel Fellow and Director of Anticipatory Computing Lab, and Hanlin Tang, Sr. Director of the Intel AI Lab, talk with host Abigail Hing Wen about the intersection of humans and AI.

The three discuss a wide range of topics, from keeping humans in the loop of AI systems to the ways that AI can augment human abilities. Lama talks about her work in building assistive computer systems for Prof. Stephen Hawking and British roboticist Dr. Peter Scott-Morgan. Hanlin reveals work on a DARPA program in collaboration with Brown University and Rhode Island Hospital that’s trying to restore the ability of patients with spinal cord injury to walk again.

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From the Creators of Thanos: The Making of a Virtual Human – Intel on AI Season 2, Episode 9 Nov 24, 2020

In this Intel on AI podcast episode guest Doug Roble, the senior director of software research and development at Digital Domain, joins hosts Abigail Hing Wen and Amir Khosrowshahi to talk about how Digital Domain creates virtual effects for blockbuster movies.

Doug, Abigail, and Amir discuss how Digital Domain developed virtual characters for Brad Pitt in The Curious Case of Benjamin Button and Josh Brolin in Avengers: Endgame, the technology and AI models that go into creating such complex visuals, and the virtual humans the company is working on today. To see the latest digital humans the company is developing, watch this YouTube video at: youtu.be/RKiGfGQxqaQs.

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A Modern History of AI with Turing Award Winner Yann LeCun – Intel on AI Season 2, Episode 8 Nov 11, 2020

In this episode of Intel on AI guest Yann LeCun, chief AI scientist at Facebook and professor at NYU, joins host Abigail Hing Wen to talk about the history and adoption of AI.

Considered one of the “godfathers of AI” and an ACM Turing Award Laureate, Yann has seen the ups and downs of AI for decades. Yann and Abigail talk about the origins of AI, how the ideas and advancements in technology proliferated, and what the future of AI holds.

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Data for Black Lives with Yeshi Milner – Intel on AI Season 2, Episode 7 Oct 28, 2020

In this episode of Intel on AI guest Yeshimabeit (Yeshi) Milner, founder and executive director of Data for Black Lives, joins host Abigail Hing Wen to talk about how AI technology is falling short for too many.

Yeshi and Abigail talk about how AI can exacerbate the historically negative impacts on the Black community, improving accountability and transparency in fintech, how to break down the silos between scientists and activists, and why it’s important to have a diverse set of voices in the room when monumental decisions in technology are being made.

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Inside Facebook AI with Jerome Pesenti – Intel on AI Season 2, Episode 6 Oct 21, 2020

In this episode of Intel on AI guest Jerome Pesenti, Head of AI at Facebook, joins host Abigail Hing Wen to talk about the different ways the company uses AI technology.

Jerome and Abigail discuss the three areas Facebook is focusing on for AI development, the challenges of creating systems that feel natural to users, and how social media platforms impact our lives. Also in this episode, Abigail talks with Sam Small, Chief Security Officer at ZeroFox, about using AI for risk protection across social media.

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AI & Ethics with Bernhardt Trout – Intel on AI – Season 2, Episode 5 Oct 19, 2020

In this Intel on AI podcast guest Bernhardt Trout, professor of chemical engineering and director of the Society, Engineering, and Ethics (SEE) at MIT, joins podcast host Abigail Hing Wen to talk about the ethical implications of AI.

Bernhardt and Abigail discuss the classic thought experiment “the trolley problem” and autonomous vehicles, cultural differences in technology, the Turing test, what constitutes true artificial intelligence, and why it’s important to think about happiness when discussing the future implications of machines in society.

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The Future of Work with Sandra Rivera – Intel on AI Season 2, Episode 4 Oct 07, 2020

In this episode of Intel on AI guest Sandra Rivera, Executive Vice President and Chief People Officer at Intel, joins host Abigail Hing Wen to talk about AI and the future of work.

Sandra and Abigail discuss the accelerating future of work, how AI is helping us identify and retain talent, and Sandra’s personal journey into leadership at Intel. Also in this episode, Ben Taylor, Chief AI Evangelist at DataRobot, talks with Abigail about how to fix AI models to avoid biases in the field of human resources.

Follow Sandra on Twitter at: twitter.com/sandralrivera Follow Abigail on Twitter at: twitter.com/abigailhingwen Learn more about the future of AI at: intel.com/ai

AI & the Developing World with Ed Hsu – Intel on AI – Season 2, Episode 3 Oct 06, 2020

In this episode of Intel on AI guest Edward (Ed) Hsu, senior adviser of disruptive technologies at World Bank, joins host Abigail Hing Wen to talk about how AI will continue to impact the developing world.

Ed sits at the intersection of one of the world’s oldest problems—global poverty—and newest solutions—artificial intelligence. His role includes managing special initiatives and developing partnerships with multinational technology companies. Ed and Abigail discuss how AI is being applied to the developing world, the challenges being faced, and how companies can help ensure technological gains aren’t only being made in certain sectors of the global economy.

Follow World Bank on Twitter at: twitter.com/worldbank Follow Abigail on Twitter at: twitter.com/abigailhingwen Learn more about the future of AI at: intel.com/ai

Smart Robots: From the Lab to the World with Pieter Abbeel – Intel on AI Season 2, Episode 2 Sep 30, 2020

In this Intel on AI podcast guest Pieter Abbeel, one of the world’s leading AI roboticists, joins host Abigail Hing Wen to talk about bringing AI robots into the world.

Professor Pieter Abbeel is Director of the Berkeley Robot Learning Lab and co-director of the Berkeley Artificial Intelligence (BAIR) Lab. Abbeel’s research strives to build ever more intelligent systems, pushing the frontiers of deep reinforcement learning, deep imitation learning, deep unsupervised learning, transfer learning, meta-learning, and learning to learn. His lab also investigates how AI could advance other science and engineering disciplines.

Pieter and Abigail discuss why twenty years from now almost all robots will be learning robots and how technology can help make that transition happen now.

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Learn more about the future of AI at: intel.com/ai

The Future of AI with Andrew Ng – Intel on AI Season 2, Episode 1 Sep 16, 2020

In this Intel on AI podcast episode guest Andrew Ng joins host Abigail Hing Wen to talk about the future of AI. Artificial intelligence has so much buzz around it, but only a handful of people understand it as deeply as Andrew Ng.

Andrew brings his perspective as an expert in the field and as global citizen, starting from his days as the founding leader at Google Brain, leading AI at Baidu, and serving as an adjunct professor in computer science at Stanford University. Among Andrew’s other pursuits: being the founder of deeplearning.ai, the founder and CEO of Landing AI, a general partner at AI Fund, and chairman and co-founder of Coursera.

Andrew and Abigail discuss why most of the important work yet to be done with AI is in industries outside of Silicon Valley, such as manufacturing, agriculture, and healthcare, highlighting specific examples of where AI will bring value and transform several sectors of society.

Follow Andrew on Twitter at: twitter.com/andrewyng

Follow Abigail on Twitter at: twitter.com/abigailhingwen

Learn more about the future of AI at: intel.com/ai

Relaunch with host Abigail Hing Wen – Intel on AI Season 2, Episode 0 Sep 10, 2020

The Intel on AI podcast is relaunching with New York Times best-selling author Abigail Hing Wen as the new host. Focusing on interviews with the world’s most interesting AI experts, the Intel on AI podcast covers a wide range of topics, including applications, strategy, ethics, policy, entertainment, scientific research, and society’s future. Previously, the podcast ran for over sixty episodes and featured Intel partners and AI business leaders.

Future episodes will include guests such as: • Andrew Ng, co-founder of Coursera, adjunct professor of computer science at Stanford University, and former head of Baidu AI Group and Google Brain • Pieter Abbeel, professor at Berkeley Artificial Intelligence Research (BAIR) Lab • Ed Hsu, senior adviser of disruptive technologies at ?World Bank Group • Sandra Rivera, executive vice president and chief people officer at Intel

Host Abigail Hing Wen is the author of the New York Times best-selling novel Loveboat, Taipei, a contributor to Forbes and Fortune, and has been seen on Bloomberg, NBC News, and more. She holds a BA from Harvard and JD from Columbia.

Follow Abigail on Twitter at: twitter.com/abigailhingwen

Learn more about the future of AI at: intel.com/ai

Empowering Enterprise AI with Cloudera Data Platform – Intel on AI – Episode 67 Aug 12, 2020

In this Intel on AI podcast episode: Enterprises today are investing in machine learning (ML) and artificial intelligence (AI) to transform their business and optimize existing workflows. Yet, knowing how to implement AI and ML in your business can be very challenging. Ali Bajwa, the Director of Partner Solutions Engineering at Cloudera, joins the Intel on AI podcast to discuss how Cloudera has recently launched the Cloudera Data Platform (CDP) which is an integrated analytics and data management platform offering broad data analytics and artificial intelligence functionality along with secure user access and data governance features. Ali describes how CDP can be deployed on cloud services or in private data centers and provides enterprises with powerful features to address almost any of your AI business needs.

Ali also describes how CDP is optimized for Intel architecture including 2nd Generation Intel Xeon Scalable Processors, Intel Optane DC Persistent Memory and Intel Ethernet providing their customers the incredible performance and flexibility that Intel technologies provides.

To learn more, visit: cloudera.com

Visit Intel AI Builders at: builders.intel.com/ai

Driving AI Adoption with DataRobot and Intel Optane DC PMM – Intel on AI – Episode 66 Aug 11, 2020

In this Intel on AI podcast episode: Organizations want to get insights from their data but face barriers to adopting machine learning (ML) and AI including lack of data science expertise in the global workforce, exorbitant costs, lack of guidance, and time commitments of traditional modeling methods. Ben Taylor, the Chief AI Evangelist at DataRobot, stops by the Intel on AI podcast to discuss how the DataRobot enterprise AI platform enables organizations build and deploy accurate ML models in a fraction of the time needed in comparison with traditional data science methods. He describes his work helping organizations overcome many of the obstacles they face when implementing AI in their business models including identifying the important business problem to solve for an organization rather than the most interesting problem. Ben also talks about a challenge DataRobot recently faced having a limitation on the size of data sets that they could train due to limited memory availability on their platform. DataRobot worked with the Intel AI Builders program to optimize their platform to utilize Intel DC Optane Persistent Memory which enabled DataRobot to provide customers with the ability to train incredibly large data sets up to 100+ gigabytes. This gives businesses the ability to truly unlock the potential of all of their data and not be hindered by smaller training data sets. Ben also talks about DataRobot is working hard to help organizations implement AI in an ethical way and protect against bias in AI algorithms.

To learn more, visit https://www.datarobot.com/ and join the conversation at: community.datarobot.com

Visit Intel AI Builders at: builders.intel.com/ai

AI Enhanced Wildlife Monitoring and Control with SAIMOS and ICU Server – Intel on AI – Episode 65 Aug 03, 2020

In this Intel on AI podcast episode: In Europe, like many locations around the world, there are important efforts around wildlife monitoring and invasive species population control to ensure that endemic species are still able to live in their natural habitats. Yet, it can be very challenging for conservationists and wildlife experts to track and monitor specific species manually. Some digital sensors and tracking systems can be put in place to assist, but often trail cameras and sensors can trigger false positives from unrelated wildlife species.

Jürgen Konetschnig, the Chief Technical Officer at SAIMOS, joins the Intel on AI podcast to talk about how the SAIMOS / ICU Server solution is using AI technology to help wildlife control efforts become more efficient. He illustrates how the solution uses AI to ensure that alarms are not triggered “blindly” and the authenticity of each alarm can be evaluated with a short video-recording or an image. Jürgen highlights how the solution is an integrative platform that links geo-data with data from any other source (eg video + sensor data) and can detect and track the intended wildlife by analyzing the image or video in real time. Jürgen also talks about how SAIMOS utilizes Intel Movidius technology to power their solution has worked with Intel to optimize their solution with Intel Distribution of OpenVINO toolkit enabling them to greatly improve performance. He also describes how SAIMOS is now working to enable all of their solutions to be OpenVINO compatible and will continue to work with Intel to utilize and optimize for Intel architecture.

To learn more, visit: saimos.de/en shop.icuserver.com

Visit Intel AI Builders at: builders.intel.com/ai

Wipro HOLMES: Foundational AI for Every Enterprise – Intel on AI – Episode 64 Aug 03, 2020

In this Intel on AI podcast episode: As customers realize the importance of adopting artificial intelligence (AI) throughout their business there can be many challenges to overcome. Hurdles like identifying the pertinent business problems, defining success criteria, evaluating technologies, implementation techniques, and adoption can all be gating factors to implementing AI.

Potti Ramakrishna, General Manager Head of HOLMES Platform Engineering at Wipro Technologies, joins the Intel on AI podcast to discuss how Wipro HOLMES can help alleviate may of the challenges for enterprise AI adoption and is a powerful suite of automation tools that can help an organization infuse AI into almost any facet of their business. He describes how Wipro and Intel have been collaborating for several years and through the Intel AI Builders program they have been able to optimize their HOLMES platform for Intel architecture and software like the Intel Optimization for Tensorflow and Intel Distribution of OpenVINO Toolkit.

Potti discusses how the Wipro platform can address specific challenges like data curation through their HOLMES Data Labeling Studio that helps reduce data intake and processing efforts. He also highlights how the name HOLMES is an acronym for Heuristics & Ontology-based Learning Machines & Experiential Systems which can help customers address an incredibly broad variety of AI challenges within their business. Potti also emphasizes several other solutions that Wipro has collaborated with Intel on including Wipro Pipesleuth for pipe crack analysis as well as medical imaging solutions that assist in diagnoses of cancer using lung CT scans.

To learn more, visit: wipro.com/holmes

Visit Intel AI Builders at: builders.intel.com/ai

Deevia AI Powered People Activity Monitoring System – Intel on AI – Episode 63 Jul 21, 2020

In this Intel on AI podcast episode: Safety and productivity issues are crucial for engineering, manufacturing, and industrial organizations. Ensuring that workers are in their safe and respective area not only helps prevent injuries but also increases productivity and compliance for a company. Apoorva Ankad, Head of Computer Vision and AI Group at Deevia Software, joins the Intel on AI podcast to discuss Deevia’s AI powered monitoring system which supports several different industries with AI powered vision solutions. He highlights how Deevia’s solution can leverage an organization’s existing CCTV (closed circuit television) camera infrastructure and existing Intel-based infrastructure to accomplish their customer’s training and inferencing needs. He talks about how Deevia’s solution has been used to help detect and analyze factory worker’s ergonomic positions to help analyze if workers are conducting their tasks in a safe manner. This can help maintain worker safety by sensing when they are not operating in a safe way and alerting the worker that they need to adjust their work to a more ergonomically correct action. Apoorva also discusses how Deevia has pivoted their technology to help create a social distancing monitoring system where camera sensors can detect if social distancing is correctly happening in a public area. Where a lapse is detected, an audio message can be delivered via speakers as a reminder to adhere to distancing protocols and help keep the general public safe by helping to remind everyone to stay socially distanced.

To learn more, visit: deevia.pw

Visit Intel AI Builders at: builders.intel.com/ai

Optimized Industrial Operations with Tvarit AI Solutions – Intel on AI – Episode 62 Jul 10, 2020

In this Intel on AI podcast episode: Industrial operations such as metal manufacturing need to keep track of how well their facilities, time, and materials are being utilized in order to be as productive and profitable as possible. Yet, there can be 100s of sensors in a manufacturing plant and capturing, analyzing, and making predictions based on all that data can be very difficult. Also, ensuring the proper working or equipment can also ensure the safety of industrial workers and minimize wasted materials. Hitesh Mittal, the Director Business Development from Tvarit GmbH, joins the Intel on AI podcast to discuss how Tvarit is working to optimize the business processes of their customers. He described a specific use case where Tvarit worked to optimize operations of a steel manufacturing plant using supervised machine learning algorithms to predict the health of mechanical components. Analyzing and predicting equipment utilization reduces waste & downtime, increases safety and profitability and Hitesh describes how the training and inferencing were done on Intel Xeon Scalable processors. Hitesh also emphasizes how Tvarit works closely with the Intel AI Builders program to optimize their solution for Intel technology and praised the program for the amount of help Tvarit received from the Intel team.

To learn more, visit: tvarit.com

Visit Intel AI Builders at: builders.intel.com/ai

Huiying Medical Helping Combat COVID-19 with AI – Intel on AI – Episode 61 Jul 06, 2020

In this Intel on AI podcast episode: The COVID-19 coronavirus, since its initial outbreak, has quickly become a global pandemic and the inadequate amount of lab tests available for people suspected of infection have posed serious risks to public health and efforts in containing the virus. Jingwen Jia (Wendy), the Assistant General Manager at Huiying Medical (HYHY), joins the Intel on AI podcast to discuss how the HYHY imaging diagnostic solution assists healthcare providers to detect and diagnose potential COVID-19 infections by analyzing computed tomography (CT) chest scans with AI-powered algorithms. She discusses how the HYHY solution is a complementary tool to help doctors make diagnosis quickly by analyzing the ground-glass opacity (GGO) and other indicators revealed in lung CT imagery. The HYHY solution has already been deployed in 30+ hospitals throughout China. Wendy emphasizes how, with the help of AI, the HYHY solution can help doctors detect the virus quickly, helping patients receive the care they need faster and assisting with the tracking and containment of the COVID-19 virus. She also discusses how HYHY has collaborated with Intel through the Intel Capital and Intel AI Builders programs to optimize their solutions to run on Intel architecture and the Intel Distribution of OpenVINO toolkit to help give healthcare providers a powerful tool to help combat the COVID-19 pandemic around the world.

To learn more, visit: en.huiyihuiying.com

Visit Intel AI Builders at: builders.intel.com/ai

Cloudpick AI-Powered Autonomous Retail Store Solution – Intel on AI – Episode 60 Jun 24, 2020

In this Intel on AI podcast episode: Retail shoppers around the world are always looking to have a more personalized, convenient, and overall better shopping experience when they visit stores. With the advent of advanced cameras, sensors, and AI technology, smart stores are eliminating the need for customers to wait in line, scan their purchases, or even complete a payment transaction with a cashier. Also, during the worldwide pandemic it can be difficult for retail staff to limit their exposure to potentially infected people throughout the course of their workday. For consumers as well, maintaining safe distances can be nearly impossible when at a busy store or when interacting directly with a store cashier. Mark Perry, the Global Business Director at Cloudpick, joins the Intel on AI podcast to talk about how Cloudpick’s AI-powered smart store solution is providing customers with enhanced shopping experiences while also working to help keep them safe. He describes how Cloudpick’s system can automatically recognize a customer when they enter the store as well as the products that the customer gathers and automatically charge costs to the customer’s account without having to interact with a cashier or scan their items. Mark also talks about how during the Covid-19 pandemic, retail stores being able to limit staff interactions with customers and allowing customers to avoid touching checkout machines helps customers staff avoid potential exposure. He also discusses how optimizing the Cloudpick solution for Intel architecture and using the Intel distribution of OpenVINO has helped enable Cloudpick’s solution to operate fater and provide consumers with better, safer shopping experiences.

To learn more, visit: cloudpick.com

Visit Intel AI Builders at: builders.intel.com/ai

Driving Sustainable Energy with HCL Wind Turbine Defect Detection Solution – Intel on AI – Episode 59 Jun 17, 2020

In this Intel on AI podcast episode: As wind turbines proliferate and grow in size and complexity, the biggest challenge to Wind Energy is the high cost of Operations and Maintenance. Manual inspection and maintenance is dangerous and expensive. With the advent of drones, gathering maintenance footage has become much easier, but without the use of AI technology, inspecting tons of footage and data is time consuming, expensive, ineffective. One defect can potentially incapacitate an entire turbine, however automation of maintenance can significantly improve the value and cost of wind energy. Alberto Gutierrez Ph.D., Chief Data Scientist at HCL America, joins the Intel on AI podcast to talk about HCL’s deep learning (DL) based wind turbine defect detection solution and how it is helping to drive sustainable energy today.

He illustrates how HCL’s solution enables wind energy operators to utilize drone technology to capture images of turbines and use deep neural network (DNN) computer vision algorithm to find potential defects in those turbines. Some of the defects that are often detected include visible defects on blade surfaces like missing teeth in VG (Vortex Generator) or panel and blade edge corrosion. Alberto describes how using AI and drones to address this workload is ultimately a safer and less expensive option that helps make wind energy cheaper and more attractive as an alternative, clean energy source. He also discusses how HCL has collaborated closely with the Intel AI Builders program to optimize their solution’s DL model using the Intel Distribution of OpenVINO toolkit to process video stream, image segmentation and object detection.

To learn more, visit: builders.intel.com/ai/membership/hcl

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AI Powered Self-Healing 4G LTE Networks with Altran – Intel on AI – Episode 58 Jun 17, 2020

In this Intel on AI podcast episode: 4G/ LTE network is a preferred method of information transfer today and is becoming more and more crucial in our extremely connected lives. To keep up with the ever-increasing volume of traffic, the network is constantly changing and becoming more and more complex. Legacy rules-based network automation techniques are not working, and communication service providers need to use predictive health analytics to monitor, predict and optimizing the behavior of 4G/LTE network continuously. Networks need to become ‘intelligent’ and can take care of themselves?. Subhankar Pal, the Assistant Vice President of Research and Innovation at Altran, joins the Intel on AI podcast to discuss how Altran’s NetAnticipate framework is driving state-of-the-art self-learning networks through continuous self-feedback.

Subhankar illustrates how Altran’s NetAnticipate uses advanced deep learning (DL) models for channel quality prediction and health analytics of 4G/LTE radio networks. He talks about how the solution involves network behavior prediction using key performance indicators in multi-cell mobility scenarios along with several regression and classification models chained together to achieve its network prediction. Subhankar also describes how the Intel AI Builders team helped with optimization testing of Intel optimized Python and Tensorflow to enable Altran to reduce training time and improve model performance so their customers can use existing Intel based hardware to achieve their network automation. Finally, Subhankar discusses the future of 5G technology and how Altran is enabling the future of LTE networks.

To learn more, visit: altran.com

Visit Intel AI Builders at: builders.intel.com/ai

Manufacturing Visual Inspection with SPECTRO From HACARUS – Intel on AI – Episode 57 Jun 02, 2020

In this Intel on AI podcast episode: Sparse modeling methods can improve the interpretability of predictive AI models and is widely used in academia today. Yet despite advances in the field, issues remain when sparse modeling meets real-life applications. Adrian Sossna, the Chief Marketing Officer at HACARUS, joins the Intel on AI podcast to talk about how the SPECTRO visual software inspection module can make sparse modeling more available. He highlights how SPECTRO enables factory automation by vastly reducing the amount of reclassification needed by human inspectors. Adrian talks about how this enables AI models to be trained faster with less data while achieving accurate results specifically targeted for production of precision parts, metals, plastics and other materials. SPECTRO contains explainability features that allow detection of defects within manufacturing and provides businesses to make improvements in their processes because they have visibility into the detection made by the software. Adrian also talks about how working with the Intel AI Builders program has allowed HACARUS to run SPECTRO on Intel Optimized Python and achieve impressive performance improvements and has been very powerful for HACARUS to deliver a better experience to their customers.

To learn more, visit: hacarus.com

Visit Intel AI Builders at: builders.intel.com/ai

Safe Industrial Workspaces with Video Analytics and Flutura AI – Intel on AI – Episode 56 May 21, 2020

In this Intel on AI podcast episode: In industrial and manufacturing settings plant safety issues like oil spills, chemical spills, and PPE (personal protective equipment) violations can happen often. These issues put lives at risk, the environment in danger, and bring down productivity and profitability of the organization. Yet, addressing these issues automatically with AI is a challenging potential. The effort to capture, create and analyze a data set for image and video annotation is immense. Sajin Payandath, the Lead Data Scientist at Flutura, joins the Intel on AI podcast to illustrate how Flutura’s CerebraVision solution addresses these issues of safety and security specific to oil & gas, manufacturing, and heavy engineering sectors. He describes how CerebraVision is a central safety monitoring system that uses AI analysis of CCTV video feed to detect safety violations occurring within an industrial plant environment. This allows organizations to detect and respond to safety issues in real-time. The solution can also potentially be used to improve the productivity by combining visual intelligence with sensor data to analyze and improve industrial processes. Sajin talks about how Flutura has been working to adapt their solution to help organizations during the Covid-19 pandemic to detect and alert social distancing violations within the workplace. He also Highlights how Flutura has worked with Intel to optimize Flutura’s training and inference workloads to ensure they run efficiently and with increased accuracy and performance on Intel architecture.

To learn more, visit: flutura.com flutura.com

Visit Intel AI Builders at: builders.intel.com/ai

End-to-end Enterprise Machine Learning Pipeline in Minutes with PaperSpace – Intel on AI Episode 55 May 18, 2020

In this Intel on AI podcast episode: Enterprises are in a race to become more agile, nimble, and responsive to remain competitive in today’s fast-changing marketplace. Turning to machine learning (ML) and data science is essential. Today companies can spend millions building their own internal ML pipelines that need ongoing support and maintenance. There are numerous tools that exist for developing traditional web services, but not many tools that enable teams to adopt ML and artificial intelligence (AI). Dillon Erb, CEO at PaperSpace, joins the Intel on AI podcast to talk about how their Gradient solution brings simplicity and flexibility of a traditional platform as a service (PaaS) for building ML models in the cloud. Grandient enables ML teams to deploy more models from research to production because of dramatically shorter development cycles when using the solution. Dillon describes how enterprises can now deploy a mature and robust PaaS within their data center to train and deploy models in a fraction of the time and costs that it previously required. He also discusses how PaperSpace has worked closely with Intel to make it easy for enterprises to use their existing CPU hardware infrastructures to build performant machine learning models with Gradient.

To learn more, visit: paperspace.com

Visit Intel AI Builders at: builders.intel.com/ai

vPhrase Making Data Easier to Understand in the Enterprise – Intel on AI – Episode 54 May 13, 2020

In this Intel on AI podcast episode: To remain competitive businesses need to utilize their data to the fullest and make data-driven decisions at all levels. Yet, collecting and analyzing data can be expensive when hiring external expertise or time consuming when training internal teams. Vivek Mishra, Head of Technology at vPhrase Analytics, joins the Intel on AI podcast to talk about their AI-based business intelligence tool automate data analysis and reporting to help any company take advantage of their data. He describes how their solution, Phrazor, transforms complex data into easy-to-understand reports with language-based insights and supports multiple languages. Vivek also highlights how Phrazor gathers data in a structured format and applies language to present the reader with humanized, targeted analysis of their data so that anyone in an organization can analyze and understand it. Phrazor gives any enterprise the ability to both analyze and visualize their data in a single, easy to use platform. Vivek discusses how the vPhrase team worked closely with Intel engineers through the Intel AI Builders program to optimize their solution for Intel Xeon processors and Intel optimized TensorFlow and Python to substantially reduce their training time.

To learn more, visit: vphrase.com phrazor.ai

Visit Intel AI Builders at: builders.intel.com/ai

Gnani AI Enabled Voice Bots Empowering Enterprises at Scale – Intel on AI – Episode 53 May 07, 2020

In this Intel on AI podcast episode: Every customer call is an opportunity to gain information about customer preferences and provide a positive experience for callers. Yet, call centers can be expensive to run and maintain, especially in the current environment where many workers are unable to travel to their job due to local and state govt restrictions. Filling the call center agent role with an AI assistant is no simple task, but by utilizing Gnani’s solution, companies can ensure that their customers have a good call center experience while saving costs on the back end. Ganesh Gopalan, Co-founder and CEO at Gnani.ai, joins the Intel on AI podcast to talk about how Gnani’s AI enabled virtual voice assistants integrate real-time analytics with a voice-bot agent to interact with callers. He illustrates how this technology delivers an intelligent, fully automated option for call centers enabling businesses to quickly respond to customer concerns and questions in a scalable way. Ganesh also illustrates how Intel Engineers worked to optimize Gnani’s decoding speed to help address more customer service calls for the same hardware configuration, making the whole solution more viable and efficient from the customer standpoint.

To learn more about Gnani.ai and AI enabled voice bot technology, visit: gnani.ai gnani.ai

Visit Intel AI Builders at: builders.intel.com/ai

Transforming Enterprise with AI and IoT, Combined – Intel on AI – Episode 52 May 06, 2020

In this Intel on AI podcast episode: The Internet of Things (IoT) is producing a tremendous amount of data. But companies need to make sense of the data and AI is a clear answer to analyze and act on that data to deliver the full potential of IoT. Previously, combining AI and IoT was relatively unthinkable. Now it is an incredibly fast growing trend, often referred to as AIoT. Bill Roberts, Senior Director of Global Process, Sensors and Smart Practice at SAS, joins the Intel on AI podcast to discuss how Intel and SAS participated in a survey to discover how organizations are using AIoT today, who within the company realizes and utilizes the value, and where AIoT will grow in the future. He illustrates how an organization’s ability to deliver value from IoT is facilitated by the use of AI. Bill discusses how all of the data being derived by the many IoT sensors and cameras available today need AI to analyze and produce insights from that data. The survey highlights how this convergence of AI and IoT is really beginning to show tremendous value to organizations that are implementing AIoT within their systems. Bill also talks about how SAS themselves have even put their own AIoT system in place measuring the health of bee hives on their North Carolina campus and use the huge amounts of data they derive from their IoT systems and AI analysis to help track, analyze, and predict the health of bees across their campus and even their state.

To learn more, visit: sas.com/aiotsolutions

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Driving AI Model Training in Healthcare with Intel Xeon and Dell EMC – Intel on AI – Episode 51 Apr 20, 2020

In this Intel on AI podcast episode: Healthcare workloads, particularly in medical imaging, require more memory usage than other AI workloads because they often use higher resolution 3D images. Deep learning (DL) models developed from these data sets require both high accuracy and high confidence levels to be useful in clinical practice, but this is incredibly data and compute intensive. David Ojika, Research Scientist at the University of Florida, joins the Intel on AI podcast to talk about his research focused on the use of accelerators for machine learning (ML) as well as heterogeneous computing using Intel FPGAs, CPUs, and GPUs for inferencing. He describes a project that he led between Intel and Dell EMC which illustrated how 2nd Generation Intel Xeon Scalable processors with Intel-optimized TensorFlow on a DellEMC PowerEdge server was a very suitable configuration to address 3D models being deployed for medical imaging analytics. David talks about how, with more than 1 TB of system memory available, 2nd Gen Intel Xeon Scalable enable researchers to develop large DL models that can be several orders of magnitude larger than those available on existing DL accelerators. He expresses how this work between the University of Florida, Dell EMC and Intel better enable the use of AI-based medical imaging to help detect and diagnose cancer using MRI and other medical imaging systems and can ultimately help save lives.

To learn more, visit: intel.ly/memorybottleneck

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Making Machine Learning Application Development Easy with Ray and Anyscale – Intel on AI – Episode 50 Apr 09, 2020

In this Intel on AI podcast episode: Today, the deluge of data has made demand for machine learning engineers explode. Also because distributed computing is a challenging and elite subfield of computer programming, finding engineers to address these skill sets can be even more challenging and limit many business from being able to take advantage of advanced technologies like machine learning (ML). Dean Wampler, the Head of Developer Relations at Anyscale, joins the Intel on AI podcast to talk about how the Ray framework, which is heavily developed and supported by Anyscale, enables any developer to easily write distributed applications which are performant, debuggable, and maintainable. He illustrates how Ray helps developers, enterprises and organizations solve their problems without having to worry about scalable infrastructure and without needing to be experts in distributed computing. Dean discusses some of the biggest users of Ray utilize it to support their infrastructure especially during incredibly high traffic volume events to do general processes, payment processing, and fraud detection. He also describes how other companies are using Ray to do reinforcement learning and business process automation. Lastly, Dean talks about how many teams within Intel are leveraging the Ray framework for model training and reinforcement learning and at the same time working together with Anyscale to contribute to Ray and optimize it for Intel architecture. Lastly, Dean mentioned that in light of growing concerns about COVID-19, they have decided to postpone Ray Summit to late Summer or early Fall of 2020.

To learn more, visit: anyscale.io

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Teaching Machines to Recognize Human Emotions with Entropik Tech and Intel – Intel on AI – Episode 49 Apr 08, 2020

In this Intel on AI podcast episode: Knowing how a product or service makes a customer feel enables companies to make successful products that their customers enjoy. Yet measuring this traditionally takes a lot of time and effort through impact studies and advertising testing. Millions are spent on creating promotional materials that have little to no analytics behind them. The ability to analyze and measure a customer’s emotional reaction in real-time would be an incredibly valuable tool for many companies. Sumit Chauhan, a Data Scientist from Entropik Tech, joins the Intel on AI podcast to talk about how Entropik focuses on emotion AI to create technologies to detect human emotions through the monitoring of brainwaves, facial expressions, and eye tracking. He illustrates how Entropik’s Affect Lab, the Emotion AI platform is an emotionally intelligent consumer research platform that offers brands a chance to preview the performance of their creatives before launch and integrate the results to produce consumer-centric offerings that generate better ROIs. Sumit discusses how Entropik was able to work with Intel to better optimize their workloads to take advantage of the efficient multi-core processing of Intel Xeon Scalable processors, along with Dlib source build and Intel Distribution of Python to achieve significant improvement in Inference performance for their solution.

To learn more, visit: entropiktech.com

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Unlocking the Potential of Your Data with Nuveo OCR and Xeon Scalable – Intel on AI – Episode 48 Apr 07, 2020

In this Intel on AI podcast episode: Manually gathering, processing, and analyzing unstructured data is extremely effort and time intensive. For industries such as insurance or finance this is a big issue and can cost an organization much time and money to address. Antonio Filho, Head of Machine Learning at Nuveo, joins the Intel on AI podcast to discuss how the Nuveo Ultra OCR (Optical Character Recognition) solution eliminates the bureaucracy enabling companies to process documents and payments through an automated system saving time and money. He illustrates how their solution enables computer systems to rapidly classify image files and extract useful metadata for export to a spreadsheet or database effectively unlocking the information trapped in a PDF or TIF image. This alleviates manual data entry by letting the computer read all the characters in a document. Antonio also emphasizes how Nuveo’s solution saw a performance beyond their expectations upon optimizing the inference with Intel optimized tools running on systems powered by Intel Xeon Scalable processors.

To learn more, visit: nuveo.ai

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Predicting the Future of Fashion with IFDAQ and Intel – Intel on AI – Episode 47 Feb 26, 2020

In this Intel on AI podcast episode: Gauging prospects and predicting the direction of the fashion industry is incredibly difficult. Businesses and investors hypothesizing the success of rising stars in the industry have to make real-time decisions to stay ahead of the curve in such a fast-paced industry. Previously, trying to analyze market data to predict a model’s success could take weeks. Frédéric Godart, Co-CEO and Head of Industry at IFDAQ (International Fashion Digital Automated Quantification), joins the Intel on AI podcast to discuss how IFDAQ is redefining the intelligent insights and real-time predictive analytics for the fashion and luxury industry enabling organizations to identify fashion trends, highlight opportunities, and guide investors by measuring the effective market value based on the relative performance in the industry. IFDAQ is an artificial intelligence (AI) system that provides quantitative market values for anyone and everything in the fashion and luxury industry drawing data from numerous sources including; industry publications, social media, corporate financial data, casting value of models appearing in fashion shows and many more. Frédéric describes how this solution can predict the real market value and influence of everything that counts in fashion and enables retailers to make smart decisions on their portfolio, helps brands hire fashion models that will have the best impact on their brand image, or enables fashion models determine where they will have the most success. He also describes how working with the Intel AI Builders program has provided great value to IFDAQ and their customers through significant performance increases.

To learn more, visit: ifdaq.com research.ifdaq.com/cities

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AI and Sound Analytics Driving Value in Manufacturing Operations – Intel on AI – Episode 46 Feb 13, 2020

In this Intel on AI podcast episode: One of the biggest challenges manufacturing operations face when adopting digitalization and intelligence is the cost and complexity to instrument existing machines, connect them to a network, and deploy relevant software. This is especially costly with legacy equipment that is not enabled with the necessary sensors, intelligence, or ability to communicate with plant infrastructure. Anand Deshpande, the Founder and CEO of Asquared IoT (A2IoT), joins the Intel on AI podcast to talk about how the Equilips 4.0 solution from A2IoT enables businesses to measure overall equipment effectiveness and provide insight into manufacturing operations right from the site of measurement. He explains how Equilips 4.0 is a completely non-invasive and non-touch device that analyzes sounds from industrial machines, welders, and other operations to provide real time feedback on the health and functionality of these operations. Equilips runs on Intel architecture and performs all of the computing at the edge, eliminating the need for a network or cloud and enabling usage in remote and rugged environments. Anand talks about how Equilips is able to transform legacy machines into AI enabled smart operations and highlights how A2IoT worked with Intel to greatly increase their performance by utilizing Intel Distribution of Python, Intel Optimizations for TensorFlow and Intel MKL-DNN.

To learn more, visit: a2iot.com

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Altoros PDF Mining and Car Damage Assessment Optimized for Xeon Processors – Intel on AI – Episode 45 Feb 06, 2020

In this Intel on AI podcast episode: When making car insurance claims it can take a lot of time to have a claims adjuster inspect the damage to your car and then get the estimate from a body shop reviewed and approved by your insurance company. This process is costly and complicated for both the insurance company and consumers and can be a pain point for all parties involved. Vladimir Starostenkov, a Machine Learning Architect at Altoros, joins the Intel on AI podcast to discuss how the Altoros Car Parts Identification Solution allows users to upload photos of damaged vehicle on location and uses a machine learning (ML) algorithm to assess the vehicle body to provide a real-time estimate on the damage. He points out that this solution can not only help consumers have a better experience when assessing car damage, but that it can save insurance companies, body shops, and consumers an incredible amount of time and money. Vladimir also describes another solution that Altoros provides that automates discovery and derivation of information from PDF documents using techniques like PDF parsing and natural language processing. He highlights how Altoros has worked with Intel to help optimize their solutions using the Intel distribution of OpenVINO toolkit to provide greater value and performance to their customers.

To learn more, visit: altoros.com cardamage.altoros.com

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AI Powered Digital Risk Protection with ZeroFOX – Intel on AI – Episode 44 Feb 06, 2020

In this Intel on AI podcast episode: Today, social media is among the primary business and communication platforms for modern organizations, yet, social media networks are incredibly large platforms with some of the most complex security challenges. Increasingly attackers hide attacks with embedded images and video manipulation which evade traditional detection methods and are very difficult for untrained systems to detect, let alone to be detectable by human beings. Matt Price, Principal Research Engineer at ZeroFOX, joins the Intel on AI podcast to discuss how ZeroFox is using machine learning and artificial intelligence to detect deepfake technology being used on social media platforms today. He talks about the challenges that occur when ingesting differently structured data from various social media platforms and how the ZeroFox platform is able to parse and categorize relevant content or types of media to be used in their data models. Matt highlights how utilizing the Intel Distribution of OpenVINO toolkit has allowed ZeroFOX to greatly increase their object detection model performance by taking advantage of the CPU optimizations within the toolkit. He also discusses how ZeroFOX works on threat intelligence, impersonation remediation, financial fraud detection and many other services with their technology.

To learn more, visit: zerofox.com

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HCL Optimized Edge Analytics using Intel Distribution of OpenVINO toolkit – Intel on AI – Episode 43 Jan 10, 2020

In this Intel on AI podcast episode: The process of diagnosing a patient with chest abnormality is done by radiologists and doctors with a lot of experience and expertise. This involves looking for the presence of foreign bodies, infiltrates, and other information to determine the type of infection so that proper medication can be suggested for a cure. This process can be challenging for providers with heavy workloads and sometimes expertise may not be available in remote areas. Alberto Gutierrez Ph.D. Chief Data Scientist, Analytics COE for HCL America, joins the Intel on AI podcast to talk about how HCL’s Diagnostic Decision Support for Medical Imaging (DDSM) solution utilizes the power of deep learning to detect the presence of thoracic disease in patients Chest X-ray. He highlights how using the Intel Distribution of OpenVINO toolkit enables HCL to deliver optimized image processing to their customers driving clear ROI in processing and accurate image detection for patients. Alberto describes how this heightened performance assists radiologist to classify the type of infection present in the patient’s chest X-ray, both saving waiting time and improving accuracy in patient diagnoses. He also talks about how HCL has worked closely with the Intel AI Builders program to utilize Intel support and software to achieve incredible performance improvements and provide greater value to their customers.

To learn more, visit: hcltech.com builders.intel.com/ai/solutions

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Detecting Deepfakes Using Intel Xeon Scalable Processors – Intel on AI – Episode 42 Jan 07, 2020

In this Intel on AI podcast episode: Recording live at the Intel AI Summit event in San Francisco California, Ben Taylor the Chief Data Officer of Zeff, joins the Intel on AI Podcast to discuss deepfake technology and risks that deepfakes can present to elections, banking, security, and many other sectors. A deepfake is the use of AI or machine learning techniques to take an existing image or video and superimpose or imitate someone’s likeness in that media. Ben talks about how a previous indicator that was used to detect deepfakes was analyzing the pattern of an individual’s eye blink rate in a video but because deepfake technology has increasingly become more complex, Zeff now uses techniques like blood flow analysis to identify them. He highlights that while previously Zeff used GPUs to run their workloads, because of batch size and memory constraints Zeff is using Intel Xeon Scalable processors to overcome these limitations and drive better performance in their workloads. Ben also discusses how, in addition to detecting deepfakes, Zeff has been working to transform businesses in a wide array of ways by using AI including smart home technology, gameshow prediction, and many others.

To learn more, visit: zeff.ai

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Saving Resources and Driving AI Innovation with Supermicro – Intel on AI – Episode 41 Dec 17, 2019

In this Intel on AI podcast episode: Recording live at the Intel AI Summit event in San Francisco California, Ray Pang Head of Technology Enablement at Supermicro, joins the Intel on AI Podcast to talk about the long term collaboration between Intel and Supermicro. He explains how, in addition to hardware, Supermicro is providing many solutions to their customers including their Resource Savings architecture which allows customers to reuse components in their server systems. This architecture enables customers to upgrade their compute and memory in server systems as advances become readily available while keeping the still viable sub-systems like power, cooling and cabling intact in the server. This reduces TCO (total cost of ownership), lowers acquisition costs, and overall reduces IT waste to help the environment. Ray also describes how Supermicro is working with Intel to support their customers to better take advantage of the AI technology that Intel has been innovating by creating efficient power and cooling systems as well as their AI and Machine Learning Ready Platform to allow their customers to take advantage of state of the art processors from Intel for AI. Lastly, he also highlights how AI and 5G are coming together at the edge and that Supermicro is helping to enable this trend by providing a very broad product portfolio that addresses the different density, power and cooling needs for edge deployments.

To learn more, visit: supermicro.com

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Efficient MRI Scans and Better Patient Outcomes with GE Healthcare AIRx – Intel on AI – Episode 40 Dec 06, 2019

In this Intel on AI podcast episode: Before an MRI technologist can scan a patient, they manually specify the slices they want the MRI to acquire. This can take several minutes of tweaking, leaving a patient waiting anxiously in the MRI scanner and adding unnecessary steps to set up each scan. It can also introduce inconsistencies into images taken over time if parameters or positioning are slightly different each time a patient gets scanned, making it challenging to accurately monitor disease progression or treatment. Recording live at the Intel AI Summit event in San Francisco California, Matthew DiDonato Director of Product and AI at GE Healthcare, joins the Intel on AI Podcast to talk about GE Healthcare’s AIRx solution. He highlights how AIRx uses deep learning to automatically identify anatomical structures to prescribe slice locations, and angle of those slices for neurological exams, delivering consistent and quantifiable results. Matthew explains how AIRx enables consistent, repeatable scan alignment to help physicians better monitor a patient across longitudinal studies and also reduces the amount of time a patient has to wait and spend during their MRI treatment. He also talks about how working with the Intel Distribution of OpenVino enabled GE to achieve a significant reduction in processing time to enable more efficient healthcare and better patient outcomes when using AIRx. Matt also talks about how GE Healthcare and Intel are working together on a number of other projects based on the GE Edison AI platform and achieving amazing result with Intel AI technology.

To learn more, visit: gehealthcare.com

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Wipro AI Solutions From Edge to Data Center Powered by Intel Technologies – Intel on AI – Episode 39 Dec 02, 2019

In this Intel on AI podcast episode: Recording live at the Intel AI Summit in San Francisco California, Deepak Dinkar Senior Practice Manager at Wipro Technologies, joins the Intel on AI Podcast to discuss how Wipro is working with Intel AI technologies to drive a wide array of innovative solutions. Deepak discusses how the Wipro Pipe Sleuth solution uses artificial intelligence (AI) to automatically process video scans of municipality sewer and water pipes to identify and mitigate pipe leakage, breakage, and blockage, which could result in property damage or safety hazards. He mentions how DC Water in Washington DC is already seeing benefits in the reduction of time it takes to inspect and maintain their piping infrastructure by using Pipe Sleuth. Deepak also highlights other innovations from Wipro including their surface crack detection solution which uses AI to identify potential defects in concrete structures enabling inspectors to more rapidly address safety concerns. He also outlines the Wipro medical imaging solution which utilizes the Intel Distribution of OpenVINO toolkit to speed up analysis of medical images helping to diagnose diseases from CT and x-ray scans. Lastly, Deepak discusses how being a member of the Intel AI Builders program has helped Wipro address different customers and verticals to create new and innovative solutions.

To learn more, visit: wipro.com

Visit Intel AI Builders at: builders.intel.com/ai

Eliminating IT Downtime with Ignio Cognitive Automation from Digitate – Intel on AI – Episode 38 Nov 18, 2019

In this Intel on AI podcast episode: Enterprises today have exploding volumes of data which is growing the scale and complexity of data centers and often result in unplanned IT downtime. This disrupts mission-critical operations, causes loss of data, and impairs application services. Atul Gupta, the Head of Alliances at Digitate, joins the Intel on AI podcast to talk about how the ignio platform helps IT rapidly identify and remediate outages in minutes. He talks about how ignio blends AI, ML and advanced Deep learning to quickly resolve issues and preempt incidents when possible. He also emphasizes how ignio binds together disparate but interconnected business applications, processes, and the underlying infrastructure to drive smart decisions and perform actions autonomously. Atul explains how the Digitate team worked with Intel AI to use the Intel Distribution of OpenVINO toolkit to optimize some of their workloads and achieved incredible performance improvements. He also expresses how being a part of the Intel AI Builders program has been an excellent and productive experience for the Digitate team.

To learn more, visit: digitate.com

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SparkCognition Automated AI Accelerating Data Science at Scale – Intel on AI – Episode 36 Oct 31, 2019

In this Intel on AI podcast episode: Creating, maintaining and deploying data models that allow businesses to gain actionable insights can require a lot of time and effort due to the vast amount of data to process. Also, machine learning optimization is a resource-intensive process and can be a challenge to accomplish in an industry where achieving fantastic results at speed is the final goal. Carlos Pazos, Product Marketing Manager at SparkCognition, joins the Intel on AI Podcast to show how the SparkCognition Darwin platform is transforming the way that data models are built in the enterprise world. He explains that automated machine learning (AutoML) enables even non-technical users to make use of science and drives organizations to scale the operationalization of ML models. The SparkCognition Darwin platform uses neuroevolution to custom build model architectures creating models in less time than traditional methods and enabling the rapid prototyping of scenarios and insights. Carlos also discusses how using Intel tools like the Intel Message Parsing Interface (Intel MPI) has allowed SparkCognition to provide greater performance to their end users. Also, how the Intel AI Builders program has provided an immense amount of development and marketing guidance to SparkCognition.

To learn more, visit: sparkcognition.com

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Gramener Image Recognition and Intel AI Saving Antarctic Penguins – Intel on AI – Episode 35 Oct 27, 2019

In this Intel on AI podcast episode: Counting and identifying characteristics of crowds can provide organizations with a lot of valuable insights. Yet challenges like image distortion, density, and different camera angles can make analyzing images accurately very challenging. Ganes Kesari, Co-founder and Head of Analytics at Gramener, joins the Intel on AI podcast to discuss how Gramener has created a crowd counting solution that can overcome those challenges and produce a very rapid and accurate analysis of images. He talks about how Gramener has utilized this solution for several AI for good projects including a joint effort with Microsoft to count Antarctic penguin colonies. Ganes explains how their solution used convolutional neural networks (CNNs) using density-based estimations to deliver a more accurate penguin count than traditional manual counting methods. He also emphasized how benchmarking the solution on Intel AI technology and the Intel Optimization for PyTorch helped Gramener achieve comparable performance at a potentially lower computational cost. In addition to AI for good projects, Ganes also outlines how this same solution can also be utilized for other enterprise opportunities like drug discovery and how Gramener will continue to collaborate with Intel to provide better optimizations and performance for its customers.

To learn more, visit: gramener.com

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John Snow Labs Spark NLP Driving AI Applications in – Intel on AI – Episode 34 Oct 20, 2019

In this Intel on AI podcast episode: Accurately answering clinical and billing questions by reading patient records, which can be hundreds of pages long, is a challenge even for human experts. While traditional rule-based or expression-matching techniques work for simple fields in templated documents, it’s harder to infer facts based on implied statements, the absence of certain statements, or a combination of other facts. David Talby, Acting CTO at John Snow Labs, joins us to talk about how the John Snow Labs Healthcare AI Platform and Spark NLP project are helping revolutionize the healthcare industry by addressing such workloads at a very high level of accuracy using state-of-the-art deep learning techniques applied to natural language processing (NLP). He discusses how NLP in healthcare is particularly challenging because clinical vocabulary and context can be very unique in comparison with other industries. David also explains how NLP is incredibly important for the healthcare industry because the data for many practical AI applications is trapped in text and NLP is needed to extract actionable insights. He illustrates how their solutions can run on-prem or in the cloud and highlights that John Snow Labs was recently recognized by CIO Applications magazine as its 2019 ‘AI Platform of the Year’ winner.

To learn more, visit: johnsnowlabs.com

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InstaDeep Reinforcement Learning Accelerating an AI-First World – Intel on AI – Episode 33 Oct 11, 2019

In this Intel on AI podcast episode: Enterprises today are attempting to use Artificial Intelligence (AI) to tackle more and more complex challenges. Yet, many of the AI applications today are unable to cope with optimization and automation challenges in dynamic and complex environments like mobility, logistics, manufacturing and energy. Karim Beguir, Co-Founder & CEO at InstaDeep, joins the Intel on AI podcast to discuss how InstaDeep is helping their customers solve complex decision-making problems that would traditionally have been solved with existing algorithms but that can much better be served with AI and Machine Learning (ML). Some of the use cases that InstaDeep has tackled include working with large car companies to solve issues around ride-sharing and vehicle routing. Another example is how the company has helped customers in supply chain to optimize operations like container loading and bin packing. Karim explains how InstaDeep utilizes reinforcement learning which allows the algorithm to learn from itself and can model, simulate, and solve a problem without needing to have data from a customer in the first place. He talks about how collaborating with the Intel AI Builders program has enabled InstaDeep to develop solutions that provide better efficiency and savings to their customers. Karim also shares his vision for the future of an AI-first world and how InstaDeep is helping startups and companies around the world develop and utilize AI to improve their organizations and communities.

To learn more, visit: instadeep.com

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Amsterdam UMC Uses SAS AI and Intel Architecture to Fight Cancer – Intel on AI – Episode 32 Oct 03, 2019

In this Intel on AI podcast episode: Colorectal cancer is the one of the most common cancers worldwide, and in about half of patients it can spread to the liver and lead to greater complications. Traditional manual examinations of tumors are time-consuming and subjective for radiologists. Yet, using AI for this workload can be complicated because biomarkers, DNA, and genomic data are enormous and require lots of compute, storage and software to analyze. Saurabh Gupta, Director Advanced Analytics & Artificial Intelligence Product Management at SAS, joins us to talk about how SAS is working with the Amsterdam University Medical Center (UMC) on medical imaging analysis to identify if patients are candidates for surgery based upon the size and growth of lesions. He talks about how Amsterdam UMC using SAS AI-trained models running on Intel processors was able to analyze MRI images and with much greater accuracy identify patients who respond well to chemotherapy and become candidates for surgery. Saurabh highlights how SAS is not just simply a software vendor but also provides many tools that gives its customers a platform to do powerful AI analysis with the SAS software that they know and trust. He also talks about how important it is for SAS customers to be able to develop their solutions on Intel architecture because they already have the skills and architecture needed to address the workloads they have.

To learn more, visit: SAS.com/ai

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Powerful Visual Analytics for Retail and Fighting Bias in AI with Valtech – Intel on AI Episode 31 Sep 27, 2019

In this Intel on AI podcast episode: There is a massive amount of insight to be gained by studying brick and mortar customer behavior. Yet capturing, labeling, and analyzing that data can be a very complicated and compute intensive activity. Dan Klein, Chief Data Officer at Valtech, joins the Intel on AI podcast to discuss how Valtech’s project VOID (Visual Object Identification) uses Intel® NUCs and the Intel developer stack to capture retail customer data like customer locations, face metadata, and apparel metadata to provide incredible real time insights for retail organizations. He talks about how powerful this technology can be to help retailers better analyze and cater to their customers in real-time but also how sensitive capturing individual information can be from a privacy and bias standpoint. Dan highlights how Valtech is proactively working to build their training data sets to include a diverse set of individuals of different genders and ethnic backgrounds to fight discrimination and bias in their analysis. He addresses the importance of ensuring individual privacy and fighting gender and racial bias in AI analytics and highlights that building a strong and diverse team at Valtech provides the foundation for this effort.

To learn more, visit: valtech.com

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Lifting AI Makers Into the Professional Arena with AAEON and Intel – Intel on AI – Episode 30 Sep 19, 2019

In this Intel on AI podcast episode: There are many individuals (makers) creating artificial intelligence (AI) enabled solutions and many of which may help drive AI to the next level. Yet, there is a challenge for makers when moving from an inexpensive board used for development to large scale production that requires much more specialization and infrastructure. Aling Wu, the AI and IoT Alliance Director at AAEON, joins the Intel on AI Podcast to discuss how the AAEON UP Board Computer Board family for Professional Makers helps bridge the gap from maker market to large scale production. She highlights how AAEON works directly with makers to take their design and actually produce and ship in volume production in 6-12 month period of time. Aling also discusses how AAEON provides makers with easy shopping solutions for development boards with UP shop and a forum where makers can discuss and refine their solution called UP Community. She talks about how important AI on the edge is for the development of the AI industry and how AAEON uses Intel’s broad portfolio of technologies including FPGAs (Field Programmable Gate Array) and Intel Core process to achieve performance on the edge.

To learn more, visit: aaeon.com

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Driving Results with OneClick.AI and Intel Optimizations for Deep Learning – Intel on AI – Episode 29 Sep 13, 2019

In this Intel on AI podcast episode: As artificial intelligence (AI) technologies rapidly advance many companies would like to take advantage of the potential of AI but can’t because they do not have the resources or skills for modeling and engineering. Yuan Shen, Founder and CEO of OneClick.AI, joins Intel on AI to talk about how OneClick enables businesses with no knowledge of AI and coding to solve complex business problems and drive real ROI with only a few clicks. He discusses how the OneClick.ai Automated Deep Learning (AutoDL) platform automates every step of AI model development supporting many different use cases including numeric prediction, classification, forecasting, recommendation, and image object detection. Yuan talks about how OneClick.AI has worked with the Intel AI Builders program to optimize deep learning on Intel Xeon Scalable processors using Intel optimization libraries and how they have been able to see great performance increases due to using those optimizations.

To learn more, visit: oneclick.ai

Or: builders.intel.com/ai/membership/oneclickai

Real-Time Motion Capture Technology with wrnch AI and Intel OpenVINO – Intel on AI – Episode 28 Sep 09, 2019

In this Intel on AI podcast episode: Today athletes are always looking for new ways to analyze and improve their performance through video analysis and sports enthusiasts love to experience sports in new and innovative ways, but 3D capture technology can be difficult to program and expensive to access. Paul Kruszewski, the CEO and Founder of wrnch AI, joins the Intel on AI podcast to talk about the markerless motion capture real-time 3D tracking that wrnch is enabling which is helping to democratize that motion capture technology. He discusses how the wrnch solution can enable any 2D camera to become a motion capture device by using a deep learning trained model that tracks 23 skeletal points to do real-time analysis on human movement. Dr. Paul highlights how this technology can be used not just for sports training and in-home exercise guidance, but can even span many healthcare and even autonomous driving applications. He expresses how wrnch heavily benefits from breadth of hardware options that Intel delivers and how utilizing the Intel OpenVINO toolkit has enabled wrnch solutions to be available to developers around the world to do real-time AI on Intel Architecture.

To learn more, visit: wrnch.ai

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Wipro Pipe Sleuth and Optimized Inference with Intel OpenVINO Toolkit – Intel on AI – Episode 27 Sep 08, 2019

In this Intel on AI podcast episode: Regular inspection of underground water and sewage pipelines is essential in the water utility industry to prioritize maintenance tasks and avoid pipe leakage, breakage, and blockage, which might result in property damage or safety hazards. Traditional inspection with videos being captured by a remotely operated rover and then manually analyzing footage is cumbersome, time-consuming, and error prone.

Sundar Varadarajan, Consulting partner for AI/ML at Wipro Technologies, joins the Intel on AI podcast to discuss how Wipro is using artificial intelligence (AI) to automatically process video scans and identify, grade, and score overall pipe quality. Utilizing the Intel Optimization for TensorFlow and Intel OpenVINO Toolkit, Sundar explains how Wipro enables their customers to perform image inferencing in real time allowing pipeline inspections to be incredibly more efficient, consistent, and accurate. Sundar also talks about how Wipro is working with the Intel AI Builders program to optimize their solutions for the latest Intel technologies and lists several other solutions focused on concrete surface crack detection and medical imaging.

To learn more, visit: wipro.com

Visit Intel AI Builders at: builders.intel.com/ai

Enabling Data-driven Culture with J!Quant and Intel Xeon Scalable Processors – Intel on AI – Episode 26 Aug 29, 2019

In this Intel on AI podcast episode: Many large enterprises need more accuracy and atomization for planning and purchasing. Yet, when generating and analyzing such a massive volume of data, the algorithms use an enormous amount of memory and processing can be slow, problematic, and often not calculate correctly at all? Dionisio Agourakis, the CEO at J!Quant, joins the Intel on AI podcast to talk about how J!Quant has a diverse portfolio of products involving deep learning (DL) and time-series prediction for stock optimization, demand forecast, and profit forecast to help their customers. He talks about how J!Quant helps enable a data-driven culture within their customers’ decision-making processes in order to stay relevant, profitable and open to new opportunities. Dionisio also discusses a specific use case utilizes the 2nd Generation Intel Xeon Scalable processors to tackle a memory bounded algorithm that a customer had and were able to successfully process the inference using Intel processors and Intel Optimizations for Tensorflow.

To learn more, visit: jquant.com.br builders.intel.com/ai/membership/jquant software.intel.com/en-us/frameworks

Visit Intel AI Builders at: builders.intel.com/ai

Using AI to Build Explainable AI with Intel Optimizations and DarwinAI – Intel on AI – Episode 25 Aug 21, 2019

In this Intel on AI podcast episode: Deep neural networks (DNNs), which are arguably the most powerful form of AI today, are difficult to build, run, and explain. Such challenges constitute significant roadblocks for their adoption in their enterprise. Ironically, AI itself can be used to assist data scientists and developers to build and evaluate DNNs. Sheldon Fernandez, CEO of DarwinAI, joins us to talk about how DarwinAI is using this ‘AI building AI’ method in their Generative Synthesis platform.

Sheldon explains how their technology reduces the complexity in designing high-performance deep learning solutions and also facilitates explainable deep learning, which allows a user to understand why a network makes the decisions it does. Finally, he describes a recent analysis conducted by the Intel AI Builders team, where Darwin-generated networks coupled with Intel Optimizations for TensorFlow were able to deliver up to 16.3X performance increase on ResNet50 and up to 9.6X on NASNet workloads.

To learn more, visit: darwinai.ca

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Vispera Visual Intelligence for Retail with Intel OpenVINO Toolkit – Intel on AI – Episode 24 Aug 07, 2019

In this Intel on AI podcast episode: Millions of dollars are lost by retailers worldwide each year as a result of out of stock products, overstocking, and shrinkage. Out of stock products also incur other costs, such as the reduced impact of in-store promotions and the time employees use dealing with customer requests about missing items. Ceyhun Burak Akgül, the Co-Founder and CTO of Vispera, joins the Intel on AI podcast to discuss how Vispera image recognition and retail real-time monitoring solutions are enabling businesses to use cameras to track product stocks and customer behavior to analyze and react to better serve the customer and business in real time. He describes the Vispera ShelfSight solution that is powered by the Intel Movidius Neural Compute Stick and optimized using Intel Distribution of OpenVINO Toolkit to allow brick and mortar stores to better compete with the online shopping experiences of today. Ceyhun talks about how the Intel AI Builders program has helped Vispera to better enable and benchmark their systems while helping connect them with potential prospective customers.

To learn more, visit: vispera.co

Visit Intel AI Builders at: builders.intel.com/ai

Cloudera Data Science Workbench and ML Services Driving an AI First World – Intel on AI – Episode 23 Aug 07, 2019

In this Intel on AI podcast episode: Justin Norman, Director of Data Science Research and Services at Cloudera, joins the Intel on AI podcast to talk about the challenges that enterprises face when executing AI solutions like scalability, compliance, and security. He illustrates how Cloudera’s experience and deep portfolio, including the Cloudera Data Science Workbench, provides the tools and expert guidance needed to help customer’s end-to-end transition into an AI first world. Justin highlights how Cloudera Fast Forward Labs is the foundation to provide strategic vision and expertise to facilitate customers in planning and business strategy when utilizing AI. Justin also describes how Cloudera and Intel has had a long strategic partnership working to provide customers both the cutting-edge hardware and software to meet all of their business needs.

To learn more about the Cloudera Fast Forward Labs offering, the Cloudera Science Workbench tool, or machine learning, visit: cloudera.com/about/machine-learning.html

Visit Intel AI Builders at: builders.intel.com/ai

Real-time Insights with the Avaya Intelligent Conversational Platform – Intel on AI – Episode 22 Jul 30, 2019

In this Intel on AI podcast episode: Every customer call is an opportunity to gain information about customer preferences and pain points. Yet capturing and structuring call data can be a very slow, time, and compute intensive process. On this episode of Intel on AI, Yishay Carmiel founder of IntelligentWire (an Avaya Company), comes in to talk about the Avaya Conversational Intelligence platform that is helping to transform customer experiences through improving agent productivity by reducing contact center after-call work. The platform instantly captures and analyzes customer conversations, transcribing spoken words into a machine-readable format while providing instant sentiment analysis and flagging important actions to be taken by the service agent. Yishay explains how the Avaya Conversational Intelligence platform utilizes speech recognition, natural language processing (NLP), and data analysis to provide enterprises with invaluable detail. He also describes how the growing number of contact centers utilizing this type of solution is rapidly growing and the need for real-time insights provided by the Avaya Conversational Intelligence platform is imperative in improving customer experience.

To learn more, visit: avaya.com

Visit Intel AI Builders at: builders.intel.com/ai

Unlocking the Power of Data with BigData Corp. and Intel Optimizations – Intel on AI – Episode 21 Jul 17, 2019

In this Intel on AI podcast episode: When doing big data analysis and developing AI models businesses start by capturing very accurate, well defined, and structured data to be able to train their models. Yet, gathering and structuring all of that information can be very difficult and time consuming. Thoran Rodrigues, Founder and CEO of BigData Corp, joins the Intel on AI Podcast to talk about how BigData Corp. is capturing, aggregating, and structuring information from millions of websites to help companies exploit the full potential of data. He also talks about how the company provides API solutions that allow their customers to extract real-time insight from those large amounts of data for use cases spanning e-commerce, financial services, document authentication, and more. Thoran expresses how important software optimizations are to enable BigData Corp to scale their business and more efficiently use infrastructure resources which translates to fast and consistent services for their customers.

To learn more, visit: bigdatacorp.com.br

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Enabling Early Cancer Detection with Huiying Medical and Intel AI – Intel on AI – Episode 20 Jul 17, 2019

In this Intel on AI podcast episode: There is growing need for digital health solutions to improve accessibility and affordability of medical resources to add value for care providers and improve healthcare outcomes for patients. Xuefeng Zhang, the Chief Marketing Officer of Huiying Medical Technology (HYHY), joins the Intel on AI podcast to talk about how HYHY is working with Intel to help bring new digital health solutions to communities in need throughout China. He specifically discusses the Pink Ribbon campaign where Intel’s Data Center Group collaborated with HYHY to apply Intel AI technology to boost the accuracy and speed of breast cancer detection. This project helped enable many healthcare providers across China improve their time and accuracy of breast cancer diagnoses. Xuefeng also illustrates how the Intel Optimization for Tensorflow and the Intel Distribution of OpenVINO toolkit has helped HYHY achieve incredible performance for their lung nodule computerized tomography (CT) scan analysis solution. This has enabled hundreds of hospitals across China to utilize this impactful AI solution on their existing hardware.

You can learn more about the Intel AI Builders member HYHY by visiting: builders.intel.com/ai/membership/huiying-medical en.huiyihuiying.com/

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Dell EMC Ready Solutions for AI Accelerating Insights with Intel Nauta – Intel on AI – Episode 19 Jul 08, 2019

In this Intel on AI podcast episode: Phil Hummel, Senior Principal Engineer at Dell EMC, joins the Intel on AI Podcast to talk about the new Dell EMC Ready Solution for AI with Intel featuring Nauta. He illustrates how enterprises today want to take advantage of AI to leverage massive amounts of data to help differentiate their business. Yet, deploying AI solutions can often be very complex and costly to integrate into an existing infrastructure. Phil describes how Dell EMC Ready Solutions for AI provide a simplified path to AI-powered applications and greater TCO with an easy-to-set-up solution. The recently announced Ready Solution, Deep Learning with Intel, is powered by Dell EMC servers and networking, new 2nd Generation Intel Xeon Scalable processors, and Nauta, Intel’s recently released open source platform for distributed deep learning training. Phil tells us how this new solution can be a game changer for many different use cases and help enterprises get a leg up when deploying AI.

To learn more, visit: DellEMC.com/ReadyForAI

Visit Intel AI Builders at: builders.intel.com/ai

Avaamo Conversational AI Helping Computers Understand Humans – Intel on AI – Episode 18 Jun 26, 2019

In this Intel on AI podcast episode: Enterprises want to deploy conversational virtual assistants but often the technology to do the necessary natural language processing (NLP), graphing for knowledge compilation, and ingestion can be very complex and create a barrier for adoption. Abhi Sharma, a Machine Learning Engineer at Avaamo, joins the Intel on AI Podcast to describe how the Avaamo conversational AI platform enables enterprises to easily deploy high-impact conversational assistants in many different industries and verticals. He shows how Avaamo helps enterprises avoid the traditional cold start program — and because the platform is optimized for Intel Xeon Scalable processors, businesses can achieve high-performance at scale with the existing architecture they already have. Abhi also illustrates how many of the libraries and frameworks that the Avaamo solution is built upon are so fine-tuned to work on Intel Architecture that Avaamo sees incredible performance when running their AI on Intel hardware.

To learn more, visit: avaamo.ai

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AI Enhanced Fertility with Presagen and Intel Technology – Intel on AI – Episode 17 Jun 21, 2019

In this Intel on AI podcast episode: Dr. Jonathan Hall, Scientific Lead at Presagen, joins the Intel on AI podcast to discuss how Presagen is using artificial intelligence to aid clinician embryo selection for in vitro fertilization (IVF) procedures. Presagen’s solution Life Whisperer is a non-invasive cloud based tool that uses AI to analyze images of patient embryos to generate an instant report on the embryo viability. This report enables clinicians to make an incredibly informed decision on which embryos to implant into the IVF patient and has shown to greatly increase the success of IVF procedures. Dr. Hall expresses how important this can be for people who are seeking reproduction assistance through IVF by saving patients time, money, and emotional stress. He also discusses how working with Intel and using a custom version of the Intel Optimization for PyTorch gives the Presagen solution a large speed boost to help make the image analysis even faster.

To learn more, visit: presagen.com

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SubtlePET Pushing Medical Imaging Limit with Intel Distribution of OpenVINO – Intel on AI – Episode 16 Jun 20, 2019

In this Intel on AI podcast episode: Tens of Millions of patients every year spend countless hours and billions of dollars on medical scans. Also, medical scans like PET (positron emission technology) or MRI (magnetic resonance imaging) scans can take a long time to conduct and can be very uncomfortable for patients. Enhao Gong, Founder and CEO of Subtle Medical, joins the Intel on AI podcast to share how SubtlePET technology powered by artificial intelligence is enhancing PET image quality significantly, enabling hospitals to provide faster and more efficient PET exams, and helping patients and health care providers save time and money. He talks about how SubtlePET technology utilizes the Intel Distribution of OpenVINO Toolkit to accelerate inference workloads and enables their technology to seamlessly integrate with the existing infrastructure that many hospitals already use. SubtlePET recently received FDA 510k clearance and CE Mark approval which is just the beginning for Subtle Medical and their mission to make medical imaging faster, safer, and smarter.

To learn more, visit: subtlemedical.com

Visit Intel AI Builders at: builders.intel.com/ai

Driving Deep Learning and Drug Discovery with Minds.ai and Intel – Intel on AI – Episode 15 May 31, 2019

In this Intel on AI podcast episode: Sumit Sanyal, Founder and Chief Revenue Officer at Minds.ai, joins Intel on AI to discuss the Minds.ai Netrin Molecular Property Predictor (MPP) and how Minds.ai is driving drug discovery with deep learning. He describes how the minds.ai solution enables pharmaceutical companies to replace traditional labor and time intensive wet lab tests instead using graph convolutional deep neural networks (GCNNs) to make predictions about molecules and target proteins that may be useful in medications. This is revolutionizing the drug discovery industry by accelerating and reducing the cost of molecule testing. Sumit also talks about how minds.ai has been utilizing the Intel Math Kernel Library (MKL) and Intel Xeon Scalable processors to help drive their incredibly fast neural network training. He highlights how using Xeon processors is incredibly efficient in comparison to graphics processing units (GPU) and expresses how helpful the Intel MKL has been for Minds.ai.

To learn more, visit: minds.ai

Visit Intel AI Builders at: builders.intel.com/ai

Advancing Medical Imaging with Wipro and Intel AI – Intel on AI episode 14 May 16, 2019

In this Intel on AI podcast episode: Vinod Prasad, General Manager & Lead Architect at Wipro Technologies, joins Intel on AI to discuss Wipro medical imaging solutions powered by Intel Architecture and optimizations. He discusses how the medical industry faces many challenges today including the sometimes difficult and time consuming process of diagnosis of patient chest X-rays. Vinod highlights how Wipro is working to help healthcare providers save time and resources while also improving the patient experience. He describes how Wipro is automating medical image segmentation, analysis, and captioning using deep learning (DL) neural networks running on Intel architecture and optimizations. Utilizing Intel technologies like Intel Distribution of OpenVINO toolkit has enabled Wipro to provide their customers with very efficient and cost effective image analysis solutions on premise and at the edge.

To learn more, visit: wipro.com

And: builders.intel.com/ai/social-hub/podcast/medical-imaging-wipro-intel

Visit Intel AI Builders at: builders.intel.com/ai

Accelerating FPGA Adoption for AI Inference with the Inspur TF2 – Intel on AI – Episode 13 May 13, 2019

In this Intel on AI podcast episode: FPGA (field-programmable gate array) technology can offer a very high level of flexibility and performance, with low latency. Yet, with software writing thresholds, limited performance optimization, and difficult power control, FPGA solutions can also be challenging to implement. Bob Anderson, General Manager of Sales for Strategic Accounts at Inspur, joins Intel on AI to talk about the Inspur TensorFlow-supported FPGA Compute Acceleration Engine (TF2). Bob illustrates how the TF2 helps customers more easily deploy FPGA solutions and take advantage of the customization and performance of FPGAs for AI inference applications. He also describes how the TF2 is especially suitable for image-based AI applications with high real-time requirements.

To learn more, visit: inspursystems.com

Visit Intel AI Builders at: builders.intel.com/ai

Driving Easier AI Development with the Kibernetika Machine Teaching Engine – Intel on AI – Episode 12 May 10, 2019

In this Intel on AI podcast episode: Leonard Sheiba, the Founder and CEO of Kibernetika, joins Intel on AI to talk about the challenges many organizations face when developing artificial intelligence (AI) solutions especially within an enterprise environment at scale. He points out that AI enabled software components like datasets, models, training, and serving involve continuous validation and retraining to work properly and require maintaining a strict process flow to develop in an enterprise environment. Leonard illustrates how the Kibernetika Machine Teaching Engine is an end-to-end platform that manages the entire AI development workflow from concept through decommission. He describes how their platform can serve many organizations and industry verticals including telecom, healthcare, and nearly any enterprise AI deployment. Lastly, Leonard highlights how the Kibernetika platform runs on Intel Xeon Scalable processors driving the power and efficiency that Kibernetika’s customers need.

To learn more, visit: kibernetika.ai

Visit Intel AI Builders at: builders.intel.com/ai

Fast Data Analytics with GigaSpaces and Intel Optane Data Center Persistent Memory – Intel on AI – Episode 11 May 09, 2019

In this Intel on AI podcast episode: As the amount of data grows exponentially businesses are looking to leverage artificial intelligence (AI) to make real-time decisions with it. Yoav Einav, Vice President of Product at GigaSpaces, joins Intel on AI to discuss how the GigaSpaces InsightEdge platform is enabling enterprises to truly take advantage of their data utilizing in-memory computing for fast data analytics and extreme transaction processing. He illustrates how in-memory analytics allows customers to run analytics workloads on real-time data which provides low latency, high-throughput transaction, and co-location of applications and analytics to act on time-critical data in real-time. Yoav explains how enterprises in many different verticals including financial services, retail, transportation, telecom, healthcare, and others can greatly benefit from this capability. Also, Yoav talks about how Intel Optane Data Center persistent memory provides the foundation for the GigaSpaces InsightEdge platform driving more memory capacity per server driving smarter, faster insights based on larger and more varied data sets.

To learn more, visit: gigaspaces.com

Visit Intel AI Builders at: builders.intel.com/ai

Datarobot Empowers Enterprises with Automated Artificial Intelligence – Intel on AI – Episode 10 May 08, 2019

In this Intel on AI podcast episode: Dr. Gourab De, Vice President of the Data Science at DataRobot, joins Intel on AI to talk about how DataRobot is helping developers and data scientist build AI models without having to write a single line of code. The DataRobot automated machine learning platform delivers the power of artificial intelligence beyond advanced programmers who are able to work through complicated code to a whole new enterprise audience that can greatly benefit from AI. Dr. De highlights that many different industry verticals that are utilizing the DataRobot platform including banking, healthcare, retails, manufacturing and many more. He also discusses how the DataRobot platform utilizes Intel Xeon Scalable processors as a powerful hardware foundation to deliver optimized performance for organizations in pursuit of low latency and scalability – two key performance features to support the AI-driven enterprise.

For more information, visit: datarobot.com

Visit Intel AI Builders at: builders.intel.com/ai

H2O.ai Democratizes AI with 2nd Generation Intel Xeon Scalable Processors – Intel on AI – Episode 09 May 07, 2019

In this Intel on AI podcast episode: For this Intel on AI podcast episode we are joined by Vinod Iyengar, Vice President of Data Science Transformations at H2O.ai. Vinod highlights some of the challenges that enterprises face today when implementing AI deployments and how H2O.ai is empowering organizations to overcome those hurdles. He discusses how H2O.ai platforms leverage the performance of Intel Xeon Scalable Processors as well as important optimizations like Intel Math Kernel Library (Intel MKL) and Intel Data Analytics Acceleration Library (Intel DAAL) to bring rapid insights to their customers. Also collaborating on Project Blue Danube and Intel AI Builders, Vinod illustrates how together, Intel and H2O.ai are accelerating AI for everyone and helping to democratize AI. For more information, visit www.h2o.ai/.

Visit Intel AI Builders at: builders.intel.com/ai

Enabling Impactful AI Actions and Customer Understanding with Gamalon – Intel on AI – Episode 08 Mar 14, 2019

In this Intel on AI podcast episode: Ben Vigoda, the founder and CEO of Gamalon, joins the Intel on AI team to talk about how Gamalon is driving the cutting edge of artificial intelligence and machine learning. He highlights their concept of how idea learning or Bayesian Program Synthesis models can enable enterprises to implement incredible understanding of text-based customer communications. Ben describes how Gamalon’s Idea Learning technology learns rapidly and accurately from sparsely labeled training data and enables data scientists to simply enter a series of queries to build a decision tree on the fly based on the text.

For more information, visit: gamalon.com

Visit Intel AI Builders at: builders.intel.com/ai

Lenovo Intelligent Computing Orchestration Simplifying HPC and AI Model Development – Intel on AI – Episode 07 Mar 08, 2019

In this Intel on AI podcast episode: Dr. David Ellison, the Senior Artificial Intelligence Data Scientist in the HPC Business Unit at Lenovo joins Intel on AI to discuss the Lenovo Intelligent Computing Orchestration (LiCO). Dr. Ellison explains how LiCO accelerates artificial intelligence (AI) training and traditional high performance computing (HPC) deployment by providing a single software solution that simplifies resource management and the use of a cluster environment. He discusses an exciting real-world deployment where data scientists use LiCO to help analyze satellite images of crop fields to drive drought prevention and increase food security. Another deployment Dr. Ellison highlights is the use of LiCO to help evaluate assembly line product quality implementing computer vision to detect product defects or damage in real time. Dr. Ellison also talks about the great collaboration that Lenovo and Intel are doing through the Intel AI Builders program leveraging resources to help drive software optimizations and improve AI workload performance.

To learn more, visit: lenovo.com/ai

Visit Intel AI Builders at: builders.intel.com/ai

Fast and Easy Deployment of Enterprise AI Solutions with Dell EMC – Intel on AI – Episode 06 Feb 21, 2019

In this Intel on AI podcast episode: Leveraging the power and potential of artificial intelligence (AI) deployments can be a top priority for many enterprises today. Yet, deploying and managing AI workloads can be incredibly complex and require extensive integration and testing. Senior Director Global Sales Strategist for HPC and AI at Dell EMC, Adnan Khaleel joins Intel on AI a second time to discuss how Dell EMC is making enterprise AI implementation fast and easy with its Dell EMC Ready Solutions for AI. Their new Ready Solutions for machine learning (ML) are built on a foundation of Intel Xeon processors to drive the fast and extensive AI insights for enterprises today and help to reduce the time it takes to implement a new AI solution. Adnan also illustrates how Dell EMC Ready Solutions are well suited for many different industries including financial services, insurance, pharmaceuticals and many more.

For more information about Dell EMC and their Ready Solutions for AI, visit: dellemc.com

Visit Intel AI Builders at: builders.intel.com/ai

Revolutionizing Advanced Healthcare Imaging with MaxQ AI and Intel – Intel on AI – Episode 05 Feb 07, 2019

In this Intel on AI podcast episode: we are joined by Gene Saragnese, Chairman of the Board and CEO of MaxQ AI, discussing how Artificial Intelligence (AI) is a critical technology in the healthcare industry today and is rapidly revolutionizing advanced healthcare systems, such as imaging. MaxQ AI is at the forefront of this healthcare transformation developing a suite of bedside, clinical, diagnostic decision-making tools for emergency room care within the acute care space. Gene describes their initial product, the Accipio lx software platform, which is focused on treating head trauma and stroke trained to automatically analyze CT images for intracranial hemorrhage.

For more information, visit: maxq.ai

Or: builders.intel.com/ai/blog/maxq-ai-deep-learning

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Driving Human-empowered AI Computing with iMerit – Intel on AI – Episode 04 Jan 24, 2019

Radha Basu, CEO and Founder of iMerit, joins Intel on AI to talk about how human-empowered computing will drive the future of AI. iMerit specializes in human driven data annotation, enrichment, and natural language processing which is then utilized to help train AI algorithms across the world. Radha explains her mission of bringing diverse talent from underprivileged communities into the digital workforce to help create needed jobs and empower communities. She also illustrates how iMerit able to deliver enterprise-grade data & digital services globally for companies in machine learning, eCommerce and computer vision in an ethically minded way.

To learn more, visit: imerit.net

Or: builders.intel.com/ai/membership/imerit

Intel AI Builders: builders.intel.com/ai

Cutting Edge Graphics on the Silver Screen With Ziva Dynamics and Intel AI – Intel on AI – Episode 03 Jan 24, 2019

James Jacobs, Co-Founder & Co-CEO of Ziva Dynamics, joins Intel on AI to talk about the incredible work that Ziva Dynamics does to bring computer-generated characters to life for major films. Ziva Dynamics created an AI-based solution which addresses the huge challenges visual effects (VFX) artists face when building and modeling virtual humans and creatures. James expresses how Ziva’s solutions along with Intel Xeon Scalable processors and Intel architecture optimized frameworks enable artists to make cutting edge characters incredibly fast. He highlights the recent feature film The Meg as an excellent use case of Ziva software and Intel technology.

To learn more, visit: zivadynamics.com

Or: ai.intel.com/ziva-dynamics

Intel AI Builders: builders.intel.com/ai

Incredible Results with Intel Distribution for Python and Domino Data Labs – Intel on AI – Episode 02 Jan 07, 2019

Join Domino Data Labs Director of Product Mac Steele on another edition of Intel on AI. Mac illustrates the workflow challenges that many data scientists face today and how Domino’s Data Science Platform enables data science and quantitative research teams to create mature, disciplined, and scalable processes for developing models and algorithms. He also discusses the work that Domino has done on the Intel Distribution for Python and the incredible results that Domino achieved running workloads on the distribution.

To learn more, visit: dominodatalab.com blog.dominodatalab.com/intels-python-distribution-is-smoking-fast-and-now-its-in-domino

Intel AI Builders: builders.intel.com/ai

Pushing the Limits of Deep Learning with Intel Xeon and Dell EMC – Intel on AI – Episode 01 Dec 06, 2018

Intel on AI: Adnan Kahleel, Senior Director Global Sales Strategist for HPC and AI at Dell EMC, joins hosts Alexis Fink and Emily Hutson on this inaugural episode of the Intel on AI podcast. Adnan discusses how Intel and Dell EMC are working together to power some of the fastest supercomputers on the planet pushing the limits of machine learning and deep learning today. He also highlights Dell EMC’s activities at the 2018 SuperComputing Conference and their announcement that the upcoming super computer system called Frontera from the National Science Foundation (NSF) and Texas Advanced Computing Center (TACC) will be supplied by Dell EMC and powered primarily by Intel Xeon processors.

For more information about Dell EMC and their work with Intel, visit: dellemc.com

For more on AI see: builders.intel.com/ai