Intel on AI

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: Computing machinery and intelligence A quantitative description of membrane current and its application to conduction and excitation in nerve From System 1 Deep Learning to System 2 Deep Learning The Consciousness Prior BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation A deep learning theory for neural networks grounded in physics

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: Gödel, Escher, Bach: an Eternal Golden Braid Fluid Concepts and Creative Analogies: Computer Models Of The Fundamental Mechanisms Of Thought A computational model for solving problems from the Raven's Progressive Matrices intelligence test using iconic visual representations A Framework for Representing Knowledge On the Measure of Intelligence The Abstraction and Reasoning Corpus (ARC) Human-level concept learning through probabilistic program induction Why AI is Harder Than We Think We Shouldn't be Scared by 'Superintelligent A.I.' (New York Times opinion piece)

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: Spatially Distributed Local Fields in the Hippocampus Encode Rat Position Beyond inspiration: Three lessons from biology on building intelligent machines The Chinese Room Argument Digital tissue and what it may reveal about the brain Principles of Neural Design (Bruno calls this a "must read") Experiencing and Perceiving Visual Surfaces Analog VLSI Implementation of Neural Systems OIM: Oscillator-based Ising Machines for Solving Combinatorial Optimisation Problems

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. Follow Abigail on Twitter: twitter.com/abigailhingwen Follow Amir on Twitter: twitter.com/khosra

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. Follow Congresswoman Kelly on Twitter: twitter.com/reprobinkelly Follow Abigail on Twitter: twitter.com/abigailhingwen

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." Follow Genentech on Twitter: twitter.com/genentech Follow Abigail on Twitter: twitter.com/abigailhingwen Learn more about Intel's work in AI: intel.com/ai

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. Follow Terah on Twitter: twitter.com/terahlyons Follow Abigail on Twitter: twitter.com/abigailhingwen Learn more about Intel's work in AI: intel.com/ai

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

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

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. Follow Rana on Twitter: twitter.com/kaliouby Follow Abigail on Twitter: twitter.com/abigailhingwen Learn more about Intel's work in AI: intel.com/ai

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. Follow Intel AI Research on Twitter: twitter.com/intelairesearch Follow Hanlin on Twitter: twitter.com/hanlintang Follow Abigail on Twitter at: twitter.com/abigailhingwen Learn more about the Intel's global research at: intel.com/labs

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

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

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

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

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. Follow MIT Chemical Engineering on Twitter at: twitter.com/mitcheme Follow Abigail on Twitter at: twitter.com/abigailhingwen Learn more about the future of AI at: intel.com/ai

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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