70 episodes

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

Jay Shah Podcast Jay Shah

    • Education
    • 5.0 • 11 Ratings

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

    Making Machine Learning more accessible | Sebastian Raschka

    Making Machine Learning more accessible | Sebastian Raschka

    Sebastian Raschka​ is the lead AI educator at GridAI. He is the author of the book "Machine Learning with PyTorch and Scikit Learn" and also a few other books that cover the fundamentals of #machinelearning and #deeplearning techniques and implementing them with Python. He is also an Assistant Professor of Statistics at the University of Wisconsin-Madison and has been actively involved in making ML more accessible to beginners through his blogs, video tutorials, tweets and of course his books. He also holds a doctorate in Computational and Quantitative Biology from Michigan State University.

    Time Stamps of the Podcast
    00:00:00 Introductions
    00:02:40 Entry point in AI/ML that made you interested in it
    00:05:30 How did you go about learning the basics and implementation of various methods?
    00:11:45 What makes Python ideal for learning Machine Learning recently?
    00:21:54 What is your book about and who is this for?
    00:33:55 What goes into writing a good technical book?
    00:40:50 Applying ML to toy datasets vs real-world research problems
    00:47:40 Choosing b/w machine learning methods & deep learning methods
    00:56:22 Large models vs architecture efficient models
    01:01:25 Interpretability & Explainability in AI
    01:08:45 Insights for people interested in machine learning research, academia or PhD
    01:14:17 Keeping up with research in deep learning

    Sebastian's homepage: https://sebastianraschka.com/
    Twitter: https://mobile.twitter.com/rasbt
    LinkedIn: https://www.linkedin.com/in/sebastianraschka/
    His book: https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-scikit-learn-ebook-dp-B09NW48MR1/dp/B09NW48MR1/
    Video Tutorials:  @SebastianRaschka  

    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

    Reach out to 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.***
    Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
    About the author: https://www.public.asu.edu/~jgshah1/

    • 1 hr 22 min
    Current and future state of Artificial Intelligence in Healthcare | Dr. Matthew Lungren

    Current and future state of Artificial Intelligence in Healthcare | Dr. Matthew Lungren

    Dr. Matthew Lungren is currently the Chief Medical Information Officer at Nuance Communications - Microsoft company, and also holds part-time appointments with the University of California San Francisco as an Associate Clinical Professor and also as adjunct faculty at Stanford and Duke University. He is a radiologist by training and has led and contributed to multiple projects that use AI and deep learning for medical imaging and precision medicine. 

    Time stamps from the conversation
    00:00:55 Introduction
    00:01:46 Role as a Chief Medical Information Officer 
    00:05:25  Leading research projects in the industry
    00:08:45 Is AI ready for primetime use cases in the real world?
    00:12:40 Regulations on AI systems in healthcare
    00:17:25 Interpretability vs a robust validation framework
    00:25:22 Promising directions to mitigate data issues in medical research
    00:32:24 Stable diffusion models 
    00:34:06 Making datasets public
    00:39:00 Vision transformers for multi-modal models
    00:44:35 Biomarker discovery
    00:48:20 Sentiment of AI in medicine 
    00:53:26 Bridging the communication gap between computer scientists and medical experts
    01:01:42 Advice to young researchers from medical and engineering schools

    Find Dr. Lungren on social media 
    Twitter: https://twitter.com/mattlungrenmd
    LinkedIn: https://www.linkedin.com/in/mattlungrenmd/

    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.***
    Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
    About the author: https://www.public.asu.edu/~jgshah1/

    • 1 hr 5 min
    AI for improving clinical trials & drug development, entrepreneurship & AI safety | Charles Fisher

    AI for improving clinical trials & drug development, entrepreneurship & AI safety | Charles Fisher

    Dr. Charles Fisher is the CEO and Founder of Unlearn(dot)AI which helps in faster drug development and efficient clinical trials. This year they also raised a series B funding of 50 million dollars. Charles holds a Ph.D. in biophysics from Harvard University and prior to founding Unlearn, he did his Postdoctorate at Boston University, followed by being a principal scientist at Pfizer and a machine learning engineer at a virtual reality company in silicon valley. 

    Time stamps of the conversation
    00:00:30 Introduction
    00:01:16 What got you into Machine Learning?
    00:04:10 Learning the basics and implementation
    00:07:55 Digital twins for clinical trials and drug development
    00:13:06 Patient heterogeneity in medical research
    00:16:05 Error quantification of models
    00:17:17 ML models for drug development
    00:22:45 Adoption of AI in medical applications
    00:25:35 Building trust in AI systems 
    00:35:10 How to show AI models are safe in the real world?
    00:38:38 Moving from academia to industry to entrepreneurship
    00:45:08 Research projects in startups vs academia vs big companies
    00:53:12 Routine as a CEO
    00:57:50 Is a Ph.D. necessary for a research career in the industry?
    01:01:20 Taking inspiration from biology to improve machine learning
    01:05:25 Advice to young people

    About Charles:
    LinkedIn: https://www.linkedin.com/in/drckf/
    More about Unlearn: https://www.unlearn.ai/

    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.***
    Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
    About the author: https://www.public.asu.edu/~jgshah1/

    • 1 hr 12 min
    Recommendation systems, being an Applied Scientist & Building a good research career | Mina Ghashami

    Recommendation systems, being an Applied Scientist & Building a good research career | Mina Ghashami

    Mina Ghashami is an Applied Scientist in the Alexa Video team at Amazon Science alongside being a lecturer at Stanford University. Prior to joining Amazon, she was a Research Scientist at Visa Research working on recommendation systems built on transactions from users and a few other projects. She completed her Ph.D. in Computer Science from the University of Utah followed by a PostDoctoral position at Rutgers University. At Amazon, she is mainly focused on Video-based ranking recommendation systems, something we talk about in detail in this conversation. 

    Time stamps of the conversation
    00:00:50 Introductions
    00:01:40 Alexa Video - Ranking and Recommendation research
    00:05:25 Feature engineering for recommendation systems
    00:08:30  Ground truth for training recommendation systems
    00:12:46 What does an Applied Scientist do? (at Amazon)
    00:19:17 What got you into AI? And specifically recommendation systems
    00:24:30 Matrix approximation
    00:27:15 Challenges in recommendation research
    00:32:00 What's more interesting, theoretical or applied side of research?
    00:37:10 Over parametrization vs generalizability 
    00:39:55 Managing academic and industry positions at the same time
    00:46:26 Should one do a Ph.D. for research roles in the industry?
    00:50:00 Skills learned while pursuing a PhD
    00:54:22 Deciding industry vs academia
    00:56:20 Coping up with research in deep learning
    01:02:14 What makes a good research dissertation?
    01:04:16 Advice to young students navigating their interest in machine learning

    To learn more about Mina:
    Homepage: https://mina-ghashami.github.io/
    Linkedin: https://www.linkedin.com/in/minaghashami
    Research: https://scholar.google.com/citations?user=msJHsYcAAAAJ&hl=en

    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.***
    Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
    About the author: https://www.public.asu.edu/~jgshah1/

    • 1 hr 15 min
    Role of a Principal Scientist do & AI in medicine | Alberto Santamaria-Pang, Microsoft

    Role of a Principal Scientist do & AI in medicine | Alberto Santamaria-Pang, Microsoft

    Alberto Santamaria-Pang is a Principal Applied Data Scientist at Microsoft.  He did his Ph.D. in computer science from the University of Houston and has a long experience in research and development on various AI projects including but not limited to medical imaging and deep learning. Prior to Microsoft, he was a principal scientist at GE research. He has led many research projects in industry and also government-funded projects, a few of which we will be discussing today. 

    Time stamps of conversations:
    00:00:37 Introduction
    00:01:25 Background before you got into the industry
    00:04:17 Interest in AI and Medical Imaging
    00:05:54 What does a Principal Scientist do?
    00:10:00 What drives research in industry? Product or Theoretical pursuit?
    00:11:35 Learning skills relevant to a principal scientist
    00:15:14 Principal Investigator vs Principal Scientist
    00:21:00 How do industry and academia collaborate on research projects?
    00:25:30 Promise & challenges of AI in medical research and applications
    00:31:53 What should explainable AI look like?
    00:38:35 Adoption of AI in medical research
    00:43:00 Is AI generalizable? 
    00:44:36 AI for biomarker discovery
    00:51:42 Are large models useful in AI & Med space
    00:58:00 Why is there a lack of datasets?
    01:01:02 Do you think AI is scary?
    01:04:00 Where do we need innovation in AI precisely?
    01:10:20 Getting inspiration from bio-research to improve algorithms
    01:13:19 AI and molecular pathology for cancer research
    00:20:30 Should one get a Ph.D.?
    01:27:38 Advice for young people 

    About Alberto:
    His research works: https://scholar.google.com/citations?user=sVahJxsAAAAJ&hl=en
    LinkedIn: https://www.linkedin.com/in/alberto-santamaria

    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.***
    Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
    About the author: https://www.public.asu.edu/~jgshah1/

    • 1 hr 34 min
    Explainability, Human Aware AI & sentience in large language models | Dr. Subbarao Kambhampati

    Explainability, Human Aware AI & sentience in large language models | Dr. Subbarao Kambhampati

    Are large language models really sentient or conscious? What is explainability (XAI) and how can we create human-aware AI systems for collaborative tasks? Dr. Subbarao Kambhampati sheds some light on these topics, generating explanations for human-in-loop AI systems and understanding 'intelligence' in context to AI systems. He is a Prof of Computer Science at Arizona State University and director of the Yochan lab at ASU where his research focuses on decision-making and planning specifically in the context of human-aware AI systems. He has received multiple awards for his research contributions. He has also been named a fellow of AAAI, AAAS, and ACM and also a distinguished alumnus from the University of Maryland and also recently IIT Madras.

    Time stamps of conversations:
    00:00:40 Introduction
    00:01:32 What got you interested in AI?
    00:07:40 Definition of intelligence that is not related to human intelligence
    00:13:40 Sentience vs intelligence in modern AI systems
    00:24:06 Human aware AI systems for better collaboration
    00:31:25 Modern AI becoming natural science instead of an engineering task
    00:37:35 Understanding symbolic concepts to generate accurate explanations
    00:56:45 Need for explainability and where
    01:13:00 What motivates you for research, the application associated or theoretical pursuit?
    01:18:47 Research in academia vs industry
    01:24:38 DALL-E performance and critiques
    01:45:40 What makes for a good research thesis? 
    01:59:06 Different trajectories of a good CS PhD student
    02:03:42 Focusing on measures vs metrics 
    02:15:23 Advice to students on getting started with AI

    Articles referred in the conversation
    AI as Natural Science?: https://cacm.acm.org/blogs/blog-cacm/261732-ai-as-an-ersatz-natural-science/fulltext
    Polanyi's Revenge and AI's New Romance with Tacit Knowledge: https://cacm.acm.org/magazines/2021/2/250077-polanyis-revenge-and-ais-new-romance-with-tacit-knowledge/fulltext

    More about Prof. Rao
    Homepage: https://rakaposhi.eas.asu.edu/
    Twitter: https://twitter.com/rao2z

    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.***
    Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
    About the author: https://www.public.asu.edu/~jgshah1/

    • 2 hr 24 min

Customer Reviews

5.0 out of 5
11 Ratings

11 Ratings

jangadi ,

AI podcast

One the best podcast series to get started with Machine Learning and AI for beginners. Simply explained and would highly recommend to students and researchers.

Priyanka Komala ,

Passionate ML host

Jay Shah is an energetic Machine Learning expert who brings the best guests on his show to unveil nuances of ML. His engaging conversation style makes this podcast a must hear on your playlist.

AJ163! ,

Jay shah’s machine learning podcast

These podcasts are really insights and understandable in laymen terms. I have learned a lot personally; definitely recommend anyone new to machine learning!

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