Intel on AI

Learn about building cutting-edge AI tools, tailored for internal use with Intel experts Boaz Efroni Rotman and Barbara Roos. They dive into the creation of IGPT and VITA, Intel's innovative solutions for secure information sharing and productivity, and share insights on balancing generalized versus specialized AI tools. Whether you're curious about AI adoption strategies, tackling data challenges, or the future of CRM, this episode is packed with actionable advice for businesses navigating the AI revolution.   RESOURCES: ·       Vita white paper: https://www.intel.com/content/www/us/en/content-details/834569/content-details.html ·       Sales assists white paper: https://www.intel.com/content/dam/www/central-libraries/us/en/documents/improving-sales-account-coverage-with-ai-paper.pdf

Explore real-world applications driving sustainability, productivity, and accessibility, from disaster prediction to personalized banking and interactive kiosks.  Join Scott Tease, VP and GM of Lenovo’s Infrastructure Solutions Group as we discuss cutting-edge innovations like liquid cooling systems, which enhance energy efficiency in data centers and learn how AI is shifting from the cloud to the edge, making advanced technology accessible and sustainable for businesses and consumers alike. Scott also highlights Lenovo's commitment to sustainability, discussing innovative approaches to recycling and reducing environmental impact while delivering business value.

Dive into the transformative potential of Retrieval Augmented Generation (RAG) with Intel's Bill Pearson and Deloitte's Baris Sarer. In this episode, they discuss how RAG combines enterprise data with large language models to unlock new efficiencies and insights. From tackling data governance and security challenges to exploring cutting-edge solutions at the edge and in the cloud, this conversation unpacks the real-world applications, benefits, and future of AI-driven data management in the enterprise.  Learn more about Intel AI for Enterprise RAG.

Learn about the development of low-resource language models with Ian Gilmour, founder of Euler Digital and creator of Bezoku.  Ian shares how Bezoku evolved from AI Forum, addressing the challenge of creating AI models for languages with limited data. He emphasizes the need for low-resource language models and their potential use cases, like polling and surveying, through techniques like reinforcement learning. Gilmour also delves into technical strategies, such as homomorphic encryption and synthetic data, for building secure and efficient AI solutions.

Tune in to learn about the future of AI deployments, from cost-effective CPU instances to the seamless integration of multiple models for robust AI systems with Jeff Boudier from Hugging Face and Sudeep Sharma from Amazon EC2.  Jeff shares Hugging Face's mission to democratize machine learning, highlighting the ease and affordability of using diverse models on their platform. Sudeep dives into how AWS is tackling evolving customer demands by delivering next-gen Intel-powered instances, including the Gen7 Intel Sapphire Rapids processors, which optimize AI and machine learning tasks. Together, they discuss the challenges and innovations in serving customers with scalable, efficient solutions and how Hugging Face and AWS are partnering to offer more choices for AI builders.

Learn about the origins of Oracle’s Roving Edge Device, and how the next-gen iteration with updated Intel processors is leading to breakthrough advancements in defense, agriculture, and healthcare. Recorded live at Oracle CloudWorld 2024 in Las Vegas with Matt Leonard, VP of OCI Edge and Cloud Infrastructure, Peter Guerra, Global VP of Data and AI, and guest host, Andy Morris from Intel Enterprise AI. The conversation also highlights Oracle and Intel's 31-year partnership and innovations in AI deployment at the edge. Guests include: Matt Leonard is Vice President of OCI Edge Cloud product management at Oracle, leading product strategy and vision for bringing the power of cloud computing to the edge. Matt’s goal is to enable customers to deploy and manage applications anywhere. With over 20 years of experience in product management, integration, and IT advisory, Matt has a proven track record of delivering successful products and solutions for leading tech companies such as Google, Microsoft, and Amazon. Peter Guerra, Global Vice President, Data & AI at Oracle, is a proven Data & AI executive with over 20 years of experience with commercial and public sector customers.  Prior to Oracle, he led AI teams at Microsoft, AWS, Accenture and Booz Allen Hamilton. His career has been to focus on data & AI solutions for customers in defense, public sector, health, energy, and retail.  He is a technical expert in AI and data platforms, having led numerous deployments and algorithm development solutions, including contributing to the Apache Accumulo and Apache Nifi projects. He has written thought pieces for O’Reilly, published papers in IEEE, and has spoken at industry events such as NVIDIA’s GTC, Oracle CloudWorld, Blackhat, and more. Peter holds a Bachelor of Science in Computer Science, a Bachelor of Art in English from University of Maryland, and an MBA with Information Systems concentration from Loyola University.  

In this episode, Sean Phan from Pixel ML explores the transformative power of agentic workflows in automating complex processes and enhancing operational efficiency across industries. He shares insights on how AI-driven automation is being used to scale event engagement and streamline workflows in large enterprises. Sean also discusses the future potential of these workflows to revolutionize various industries.

Explore the transformative potential of AI in the enterprise, guided by insights from Anil Nanduri, Vice President and Head of Intel AI Accelerator Office. Learn why integrating AI is crucial for staying competitive and uncover the benefits of scalable solutions. This episode delves into a flexible, secure approach to AI, ensuring seamless integration with existing enterprise systems and maximizing ROI. Tune in to discover how enterprises can leverage AI to drive innovation and efficiency.   #IntelAI  @IntelAI

Join us in this episode as we dive into the transformative impact of responsible AI with David Ellison, Chief Data Scientist at Lenovo. Discover how Lenovo's Responsible AI Committee and its six guiding principles are setting new standards for privacy, security, and diversity in AI. David shares practical techniques such as data minimization and differential privacy, highlighting their roles in promoting transparency, accountability, and sustainable innovation in AI. Learn how these practices are not only shaping Lenovo's AI strategy but also paving the way for a more ethical and inclusive future in technology. #IntelAI @IntelAI

Whether you're an AI enthusiast or a business looking to integrate AI into your operations, this episode offers valuable insights into the rapidly evolving AI landscape. Join Seamus Jones, Director of Technical Marketing/Engineering at Dell, as he explains how Dell is creating comprehensive AI solutions. These solutions encompass everything from AI infrastructure and hardware to software stacks, making AI deployment easier for businesses. Explore exciting AI use cases such as computer vision in retail and multimodal applications in logistics. Learn how enterprises can leverage AI accelerators like the new Intel Gaudi 3 to enhance performance and reduce costs. Discover how AI is transforming industries and what it means for the future of enterprise computing. Tune in to find out how Dell, in partnership with Intel, is making significant strides in AI deployment and infrastructure. Don’t miss this episode packed with expert insights and practical advice on harnessing the power of AI in your business. See: https://dell.com/AI https://infohub.delltechnologies.com   

Learn about the importance of flexibility and governance in AI model management as Robert Daigle, Director of Global AI Business at Lenovo, discusses the future of AI deployment across various computing environments. He highlights the collaborative efforts of Lenovo and partners in addressing specific vertical use cases such as retail, healthcare, and smart cities, demonstrating how AI can drive real-time analytics and deliver significant business value. Daigle also touches on the ethical considerations and localized strategies Lenovo employs to ensure responsible AI implementation globally.

Explore the transformative power of AI in business leadership in this engaging episode of Intel on AI. Join hosts Ryan Carson and Tony Mongkolsmai as they interview Nathaniel Whittemore, renowned AI thought leader, founder and CEO of Super Intelligent, and host of the AI Daily Brief. Learn how executives can implement artificial intelligence solutions in their daily operations to drive significant improvements and strategic outcomes. Nathaniel provides actionable advice on starting with non-controversial AI deployments that optimize productivity and mitigate the challenges of rapid AI innovation. Tune in for invaluable insights on leveraging Intel’s AI technology in leadership roles. Subscribe and stay updated with the latest in AI applications and technology advancements.  #IntelAI @IntelAI

Discover the cutting-edge advancements in artificial intelligence with Vasudev Lal, Principal AI Research Scientist at Intel. This episode delves into the benefits of multimodal AI and the enhanced validity achieved through self-supervised learning. Vasudev also explores the applications of counterfactual reasoning in AI and the efficiency gains from using AI agents. Additionally, learn how leveraging multiple Gaudi 2 accelerators can significantly reduce LLM training times. Stay updated with the latest in AI technology and innovations by following #IntelAI and @IntelAI for more information. 

Learn about real-world applications of AI in manufacturing as Rao Desineni shares how Intel incorporates visual AI in their defect detection processes along with autonomous machine learning for improving product yields & quality.   #IntelAI @IntelAI

Learn the latest on open ecosystems, AI data foundations and Meta’s new Llama 3 with Dr. Wei Li, VP/GM of AI Software Engineering at Intel.

Dive deep into the ever-evolving landscape of AI with Intel’s VP of AI Marketing, Nuri Cankaya, as he navigates the intricacies of cutting-edge AI models and their impact on businesses.

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: Principles of Neural Development The reorganization of synaptic connexions in the rat submandibular ganglion during post-natal development Development of the neuromuscular junction: Genetic analysis in mice A technicolour approach to the connectome The big data challenges of connectomics Imaging Intracellular Fluorescent Proteins at Nanometer Resolution Stimulated emission depletion (STED) nanoscopy of a fluorescent protein-labeled organelle inside a living cell High-resolution, high-throughput imaging with a multibeam scanning electron microscope Saturated Reconstruction of a Volume of Neocortex A connectomic study of a petascale fragment of human cerebral cortex A Canonical Microcircuit for Neocortex

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: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Meta-Learning with Memory-Augmented Neural Networks Matching Networks for One Shot Learning Learning to Learn with Gradients Bayesian Model-Agnostic Meta-Learning Meta-Learning with Implicit Gradients Meta-Learning Without Memorization Efficiently Identifying Task Groupings for Multi-Task Learning Three scenarios for continual learning Dota 2 with Large Scale Deep Reinforcement Learning ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback

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: Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data. Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber?

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: Statistics of real-world illumination The Role of Natural Image Statistics in Biological Motion Estimation Surface reflectance recognition and real-world illumination statistics Accuracy of velocity estimation by Reichardt correlators Principles of Neural Design Levinthal's paradox Potassium channels Structural and Thermodynamic Properties of Selective Ion Binding in a K+ Channel Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters Long-timescale molecular dynamics simulations of protein structure and function Parallel random numbers: as easy as 1, 2, 3 Biomolecular Simulation: A Computational Microscope for Molecular Biology Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer Molecular Dynamics Simulation for All Structural basis for nucleotide exchange in heterotrimeric G proteins How GPCR Phosphorylation Patterns Orchestrate Arrestin-Mediated Signaling Highly accurate protein structure prediction with AlphaFold ATOM3D: Tasks on Molecules in Three Dimensions Geometric deep learning of RNA structure