Jay Shah Podcast

Jay Shah
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Jay Shah Podcast Podcast

Interviews with scientists and engineers working in Machine Learning and AI, about their journey, insights, and discussion on latest research topics.

  1. 5 DAYS AGO

    Time series Forecasting using GPT models | Max Mergenthaler Canseco

    Max is the CEO and co-founder of Nixtla, where he is developing highly accurate forecasting models using time series data and deep learning techniques, which developers can use to build their own pipelines. Max is a self-taught programmer and researcher with a lot of prior experience building things from scratch. 00:00:50 Introduction 00:01:26 Entry point in AI 00:04:25 Origins of Nixtla 00:07:30 Idea to product 00:11:21 Behavioral economics & psychology to time series prediction 00:16:00 Landscape of time series prediction 00:26:10 Foundation models in time series 00:29:15 Building TimeGPT 00:31:36 Numbers and GPT models 00:34:35 Generalization to real-world datasets 00:38:10 Math reasoning with LLMs 00:40:48 Neural Hierarchical Interpolation for Time Series Forecasting 00:47:15 TimeGPT applications 00:52:20 Pros and Cons of open-source in AI 00:57:20 Insights from building AI products 01:02:15 Tips to researchers & hype vs Reality of AI More about Max: https://www.linkedin.com/in/mergenthaler/ and Nixtla: https://www.nixtla.io/ Check out TimeGPT: https://github.com/Nixtla/nixtla About the Host: Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

    1h 10m

  2. 5 SEPT

    Generative AI and the Art of Product Engineering | Golnaz Abdollahian

    Golnaz Abdollahian is currently the senior director of big idea innovation at Dolby Laboratories. She has a lot of experience developing and shaping technological products around augmented and virtual reality, smart homes, and generative AI. Before joining Dolby, she had experience working at Microsoft, Apple, and Sony. She also holds PhD in electrical engineering from Purdue University. Time stamps of the conversation 00:00 Highlights 01:08 Introduction 01:52 Entry point in AI 03:00 Leading Big Idea Innovation at Dolby 06:55 Generative AI, Entertainment and Dolby 08:45 How do content creators feel about AI? 10:30 From a Researcher to a Product person 14:27 Traditional Tech products versus AI products 17:52 From concept to product 20:35 Lesson in Product design from - Apple, Microsoft, Song & Dolby 25:34 Interpreting trends in AI 29:25 Good versus Bad Product 31:25 Advice to people interested in productization More about Golnaz: https://www.linkedin.com/in/golnaz-abdollahian-93938a5/ About the Host: Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

    35 min

  3. 14 AUG

    Future of Software Development with LLMs, Advice on Building Tech startups & more | Pritika Mehta

    Pritika is the co-founder of Butternut AI, a platform that allows the creation of professional websites without hiring web developers. Before butternut, Pritika had entrepreneurship experience building some other products, which later got acquired. Time stamps of the conversation 00:00 Highlights 01:15 Introduction 01:50 Entry point in AI 03:04 Motivation behind Butternut AI 05:00 Can software engineering be automated? 06:36 Large Language Models in Software Development 08:00 AI as a replacement vs assistant 10:32 Automating website development 13:40 Limitations of current LLMs 18:12 Landscape of startups using LLMs 19:50 Going from an idea to a product 27:48 Background in AI for building AI-based startup 30:00 Entrepreneurship 34:32 Startup Culture in USA vs. India More about Butternut AI: https://butternut.ai/ Pritika's Twitter: https://x.com/pritika_mehta And LinkedIn: https://www.linkedin.com/in/pritikam/ About the Host: Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

    38 min

  4. 9 JUL

    Instruction Tuning, Prompt Engineering and Self Improving Large Language Models | Dr. Swaroop Mishra

    Swaroop is a research scientist at Google-Deepmind, working on improving Gemini. His research expertise includes instruction tuning and different prompt engineering techniques to improve reasoning and generalization performance in large language models (LLMs) and tackle induced biases in training. Before joining DeepMind, Swaroop graduated from Arizona State University, where his research focused on developing methods that allow models to learn new tasks from instructions. Swaroop has also interned at Microsoft, Allen AI, and Google, and his research on instruction tuning has been influential in the recent developments of LLMs. Time stamps of the conversation: 00:00:50 Introduction 00:01:40 Entry point in AI 00:03:08 Motivation behind Instruction tuning in LLMs 00:08:40 Generalizing to unseen tasks 00:14:05 Prompt engineering vs. Instruction Tuning 00:18:42 Does prompt engineering induce bias? 00:21:25 Future of prompt engineering 00:27:48 Quality checks on Instruction tuning dataset 00:34:27 Future applications of LLMs 00:42:20 Trip planning using LLM 00:47:30 Scaling AI models vs making them efficient 00:52:05 Reasoning abilities of LLMs in mathematics 00:57:16 LLM-based approaches vs. traditional AI 01:00:46 Benefits of doing research internships in industry 01:06:15 Should I work on LLM-related research? 01:09:45 Narrowing down your research interest 01:13:05 Skills needed to be a researcher in industry 01:22:38 On publish or perish culture in AI research More about Swaroop: https://swarooprm.github.io/ And his research works: https://scholar.google.com/citations?user=-7LK2SwAAAAJ&hl=en Twitter: https://x.com/Swarooprm7 About the Host: Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

    1h 32m

  5. 23 APR

    Role of Large Language Models in AI-driven medical research | Dr. Imon Banerjee

    Dr. Imon Banerjee is an Associate Professor at Mayo Clinic in Arizona, working at the intersection of AI and healthcare research. Her research focuses on multi-modality fusion, mitigating bias in AI models specifically in the context of medical applications & more broadly building predictive models using different data sources. Before joining the Mayo Clinic, she was at Emory University as an Assistant Professor and at Stanford as a Postdoctoral fellow. Time stamps of the conversation 00:00 Highlights 01:00 Introduction 01:50 Entry point in AI 04:41 Landscape of AI in healthcare so far 06:15 Research to practice 07:50 Challenges of AI Democratization 11:56 Era of Generative AI in Medical Research 15:57 Responsibilities to realize 16:40 Are LLMs a world model? 17:50 Training on medical data 19:55 AI as a tool in clinical workflows 23:36 Scientific discovery in medicine 27:08 Dangers of biased AI models in healthcare applications 28:40 Good vs Bad bias 33:33 Scaling models - the current trend in AI research 35:05 Current focus of research 36:41 Advice on getting started 39:46 Interdisciplinary efforts for efficiency 42:22 Personalities for getting into research More about Dr. Banerjee's lab and research: https://labs.engineering.asu.edu/banerjeelab/person/imon-banerjee/ About the Host: Jay is a PhD student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

    47 min

  6. 27/10/2023

    Algorithmic Reasoning, Graph Neural Nets, AGI and Tips to researchers | Petar Veličković

    Dr. Petar Veličković is a Staff Research Scientist at Googe DeepMind and an Affiliated lecturer at the University of Cambridge. He is known for his research contributions in graph representation learning; particularly graph neural networks and graph attention networks. At DeepMind, he has been working on Neural Algorithmic Reasoning which we talk about more in this podcast. Petar’s research has been featured in numerous media articles and has been impactful in many ways including Google Maps’s improved predictions. Time stamps 00:00:00 Highlights 00:01:00 Introduction 00:01:50 Entry point in AI 00:03:44 Idea of Graph Attention Networks 00:06:50 Towards AGI 00:09:58 Attention in Deep learning 00:13:15 Attention vs Convolutions 00:20:20 Neural Algorithmic Reasoning (NAR) 00:25:40 End-to-end learning vs NAR 00:30:40 Improving Google Map predictions 00:34:08 Interpretability 00:41:28 Working at Google DeepMind 00:47:25 Fundamental vs Applied side of research 00:50:58 Industry vs Academia in AI Research 00:54:25 Tips to young researchers 01:05:55 Is a PhD required for AI research? More about Petar: https://petar-v.com/ Graph Attention Networks: https://arxiv.org/abs/1710.10903 Neural Algorithmic Reasoning: https://www.cell.com/patterns/pdf/S2666-3899(21)00099-4.pdf TacticAI paper: https://arxiv.org/abs/2310.10553 And his collection of invited talks:  @petarvelickovic6033  About the Host: Jay is a PhD student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

    1h 12m

  7. 10/10/2023

    Combining Vision & Language in AI perception and the era of LLMs & LMMs | Dr. Yezhou Yang

    Dr. Yezhou Yang is an Associate Professor at Arizona State University and director of the Active Perception Group at ASU. He has research interests in Cognitive Robotics and Computer Vision, and understanding human actions from visual input and grounding them by natural language. Prior to joining ASU, he completed his Ph.D. from the University of Maryland and his postdoctoral at the Computer Vision Lab and Perception and Robotics Lab. Timestamps of the conversation 00:01:02 Introduction 00:01:46 Interest in AI 00:17:04 Entry in Robotics & AI Perception 00:20:59 Combining Vision & language to Improve Robot Perception 00:23:30 End-to-end learning vs traditional knowledge graphs 00:28:28 What do LLMs learn? 00:30:30 Nature of AI research 00:36:00 Why vision & language in AI? 00:45:40 Learning vs Reasoning in neural networks 00:53:05 Bringing AI to the general crowd 01:00:10 Transformers in Vision 01:08:54 Democratization of AI 01:13:42 Motivation for research: theory or application? 01:18:50 Surpassing human intelligence 01:25:13 Open challenges in computer vision research 01:30:19 Doing research is a privilege 01:35:00 Rejections, tips to read & write good papers 01:43:37 Tips for AI Enthusiasts 01:47:35 What is a good research problem? 01:50:30 Dos and Don'ts in AI research More about Dr. Yang: https://yezhouyang.engineering.asu.edu/ And his Twitter handle: https://twitter.com/Yezhou_Yang About the Host: Jay is a PhD student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Check-out Rora: https://teamrora.com/jayshah Guide to STEM PhD AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-2023 Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

    1h 54m

  8. 12/07/2023

    Risks of AI in real-world and towards Building Robust Security measures | Hyrum Anderson

    Dr Hyrum Anderson is a Distinguished Machine Learning Engineer at Robust Intelligence. Prior to that, he was Principal Architect of Trustworthy Machine Learning at Microsoft where he also founded Microsoft’s AI Red Team; he also led security research at MIT Lincoln Laboratory, Sandia National Laboratories, and Mendiant, and was Chief Scientist at Endgame (later acquired by Elastic). He’s also the co-author of the book “Not a Bug, But with a Sticker” and his research interests include assessing the security and privacy of ML systems and building Robust AI models. Timestamps of the conversation 00:50 Introduction 01:40 Background in AI and ML security 04:45 Attacks on ML systems 08:20 Fractions of ML systems prone to Attacks 10:38 Operational risks with security measures 13:40 Solution from an algorithmic or policy perspective 15:46 AI regulation and policy making 22:40 Co-development of AI and security measures 24:06 Risks of Generative AI and Mitigation 27:45 Influencing an AI model 30:08 Prompt stealing on ChatGPT 33:50 Microsoft AI Red Team 38:46 Managing risks 39:41 Government Regulations 43:04 What to expect from the Book 46:40 Black in AI & Bountiful Children’s Foundation Check out Rora: https://teamrora.com/jayshah Guide to STEM Ph.D. AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-2023 Rora's negotiation philosophy: https://www.teamrora.com/post/the-biggest-misconception-about-negotiating-salaryhttps://www.teamrora.com/post/job-offer-negotiation-lies Hyrum's Linkedin: https://www.linkedin.com/in/hyrumanderson/ And Research: https://scholar.google.com/citations?user=pP6yo9EAAAAJ&hl=en Book - Not a Bug, But with a Sticker: https://www.amazon.com/Not-Bug-But-Sticker-Learning/dp/1119883989/ About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

    52 min
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About

Interviews with scientists and engineers working in Machine Learning and AI, about their journey, insights, and discussion on latest research topics.

Information

  • Creator
    Jay Shah
  • Years Active
    2020 - 2024
  • Episodes
    91
  • Rating
    Clean
  • Copyright
    © Jay Shah
  • Show Website
    Jay Shah Podcast

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