Jay Shah Podcast
Technology Podcasts
Interviews with scientists and engineers working in Machine Learning and AI, about their journey, insights, and discussion on latest research topics.
Location:
United States
Genres:
Technology Podcasts
Description:
Interviews with scientists and engineers working in Machine Learning and AI, about their journey, insights, and discussion on latest research topics.
Twitter:
@jaygshah22
Language:
English
Contact:
4804947171
Email:
jgshah1@asu.edu
Episodes
Risks of AI in real-world and towards Building Robust Security measures | Hyrum Anderson
7/12/2023
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.***
Duration:00:51:33
Being aware of Systematic Biases and Over-trust in AI | Meredith Broussard
7/10/2023
Meredith is an associate professor at New York University and research director at the NYU Alliance for Public Interest Technology. Her research interests include using data analysis for good and ethical AI. She is also the author of the book “More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech” and we will discuss more about this with her in this podcast. Time stamps of the conversation 00:42 Introduction 01:17 Background 02:17 Meaning of “it is not a glitch” in the book title 04:40 How are biases coded into AI systems? 08:45 AI is not the solution to every problem 09:55 Algorithm Auditing 11:57 Why do organizations don't use algorithmic auditing more often? 15:12 Techno-chauvinism and drawing boundaries 23:18 Bias issues with ChatGPT and Auditing the model 27:55 Using AI for Public Good - AI on context 31:52 Advice to young researchers in AI Meredith's homepage: https://meredithbroussard.com/ And her Book: https://mitpress.mit.edu/9780262047654/more-than-a-glitch/ 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.***
Duration:00:37:15
P2 Working at DeepMind, Interview Tips & doing a PhD for a career in AI | Dr. David Stutz
7/10/2023
Part-2 of my podcast with David Stutz. (Part-1: https://youtu.be/J7hzMYUcfto) David is a research scientist at DeepMind working on building robust and safe deep learning models. Prior to joining DeepMind, he was a PhD student at the Max Plank Institute of Informatics. He also maintains a fantastic blog on various topics related to machine learning and graduate life which is insightful to young researchers out there. 00:00:00 Working at DeepMind 00:08:20 Importance of Abstraction and Collaboration in Research 00:13:08 DeepMind internship project 00:19:39 What drives research projects at DeepMind 00:27:45 Research in Industry vs Academia 00:30:45 Interview tips for research roles, at DeepMind or other companies 00:44:38 Finding the right Advisor & Institute for PhD 01:02:12 Do you really need a Ph.D. to do AI/ML research? 01:08:28 Academia vs Industry: Making the choice 01:10:49 Pressure to publish more papers 01:21:35 Artificial General Intelligence (AGI) 01:33:24 Advice to young enthusiasts on getting started David's Homepage: https://davidstutz.de/ And his blog: https://davidstutz.de/category/blog/ Research work: https://scholar.google.com/citations?user=TxEy3cwAAAAJ&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.***
Duration:01:42:28
Negotiating Higher Salary for AI & Tech roles after Job Offer | Jordan Sale
7/10/2023
Rora helps top AI researchers and professionals negotiate their pay -- often as they transition from academia into industry. Moving into tech is a huge transition for many PhDs and post-docs -- the pay is much more significant and the terms of employment are often quite different. In the past 5 years, the Rora team has helped over 1000 STEM professionals negotiate more than $10M in additional earnings from companies like DeepMind, OpenAI, Google Brain, and Anthropic -- and advocate for better roles, more alignment with their managers, and more flexible work. Referral link: 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 (the majority of the STEM PhDs we support are going into tech roles) Rora's negotiation philosophy: https://www.teamrora.com/post/the-biggest-misconception-about-negotiating-salaryhttps://www.teamrora.com/post/job-offer-negotiation-lieshttps://www.teamrora.com/post/roras-3-keys-to-negotiating-a-new-job-offer00:00 Highlights 00:55 Introduction 01:42 About Rora 05:40 Myths in Job Negotiations 08:58 Fear of losing job offers 12:36 30-60-90 day roadmap for negotiation 15:28 Knowing if you should negotiate 20:46 Negotiating with only one offer 24:40 What to negotiate? 29:00 Knowing if you're low-balled in offers 31:31 When negotiations don't workout 35:00 When & How to Negotiate? 43:00 Negotiating promotions 46:45 Is there always room for Negotiation? 49:42 Quick advice to people who have offers in hand 55:32 Wrong assumptions Learn more about Jordan: https://www.linkedin.com/in/jordansale And Rora: https://teamrora.com/jayshah Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com 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.***
Duration:00:57:43
P1 Adversarial robustness in Neural Networks, Quantization and working at DeepMind | David Stutz
7/10/2023
Part-1 of my podcast with David Stutz. (Part-2: https://youtu.be/IumJcB7bE20) David is a research scientist at DeepMind working on building robust and safe deep learning models. Prior to joining DeepMind, he was a Ph.D. student at the Max Plank Institute of Informatics. He also maintains a fantastic blog on various topics related to machine learning and graduate life which is insightful to young researchers out there. 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-202300:00:00 Highlights and Sponsors 00:01:22 Intro 00:02:14 Interest in AI 00:12:26 Finding research interests 00:22:41 Robustness vs Generalization in deep neural networks 00:28:03 Generalization vs model performance trade-off 00:37:30 On-manifold adversarial examples for better generalization 00:48:20 Vision transformers 00:49:45 Confidence-calibrated adversarial training 00:59:25 Improving hardware architecture for deep neural networks 01:08:45 What's the tradeoff in quantization? 01:19:07 Amazing aspects of working at DeepMind 01:27:38 Learning the skills of Abstraction when collaborating David's Homepage: https://davidstutz.de/ And his blog: https://davidstutz.de/category/blog/ Research work: https://scholar.google.com/citations?user=TxEy3cwAAAAJ&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.***
Duration:01:32:28
P1 Adversarial robustness in Neural Networks, Quantization and working at DeepMind | David Stutz
7/3/2023
Part-1 of my podcast with David Stutz. (Part-2: https://youtu.be/IumJcB7bE20)
David is a research scientist at DeepMind working on building robust and safe deep learning models. Prior to joining DeepMind, he was a Ph.D. student at the Max Plank Institute of Informatics. He also maintains a fantastic blog on various topics related to machine learning and graduate life which is insightful to young researchers out there.
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
00:00:00 Highlights and Sponsors
00:01:22 Intro
00:02:14 Interest in AI
00:12:26 Finding research interests
00:22:41 Robustness vs Generalization in deep neural networks
00:28:03 Generalization vs model performance trade-off
00:37:30 On-manifold adversarial examples for better generalization
00:48:20 Vision transformers
00:49:45 Confidence-calibrated adversarial training
00:59:25 Improving hardware architecture for deep neural networks
01:08:45 What's the tradeoff in quantization?
01:19:07 Amazing aspects of working at DeepMind
01:27:38 Learning the skills of Abstraction when collaborating
David's Homepage: https://davidstutz.de/
And his blog: https://davidstutz.de/category/blog/
Research work: https://scholar.google.com/citations?user=TxEy3cwAAAAJ&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/
Duration:01:32:28
Promises and Lies of ChatGPT - understanding how it works | Subbarao Kambhampati
6/7/2023
Dr. Subbarao Kambhampati is a Professor of Computer Science at Arizona State University and the director of the Yochan lab where his research focuses on decision-making and planning, specifically in the context of human-aware AI systems. He has been named a fellow of AAAI, AAAS, and ACM in recognition of his research contributions and also received a distinguished alumnus award from the University of Maryland and IIT Madras.
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-salary
https://www.teamrora.com/post/job-offer-negotiation-lies
00:00:00 Highlights and Intro
00:02:16 What is chatgpt doing?
00:10:27 Does it really learn anything?
00:17:28 Chatgpt hallucinations & getting facts wrong
00:23:29 Generative vs Predictive Modeling in AI
00:41:51 Learning common patterns from Language
00:57:00 Implications in society
01:03:28 Can we fix chatgpt hallucinations?
01:26:24 RLHF is not enough
01:32:47 Existential risk of AI (or chatgpt)
01:49:04 Open sourcing in AI
02:04:32 OpenAI is not "open" anymore
02:08:51 Can AI program itself in the future?
02:25:08 Deep & Narrow AI to Broad & Shallow AI
02:30:03 AI as assistive technology - understanding its strengths & limitations
02:44:14 Summary
Articles referred to in the conversation
https://thehill.com/opinion/technology/3861182-beauty-lies-chatgpt-welcome-to-the-post-truth-world/
More about Prof. Rao
Homepage: https://rakaposhi.eas.asu.edu/
Twitter: https://twitter.com/rao2z
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
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/
Duration:02:46:43
Building a company in middle of War, Pandemic and Economic Crisis | Karyna Naminas
6/4/2023
Karyna Naminas is the CEO of Label Your Data which provides data annotation services to different organizations interested in developing AI-based solutions.
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-salary
https://www.teamrora.com/post/job-offer-negotiation-lies
00:00:00 Introduction and Sponsors
00:02:28 Background before being a CEO
00:06:38 Fascinating aspects of AI
00:09:10 Data annotation outside of AI
00:10:21 Effect of COVID, Russia-Ukraine War, and economic crisis on Business
00:18:47 Sourcing data annotators
00:22:40 Challenges in annotation
00:31:00 Data annotation for Military applications in Ukraine
00:41:42 Tools used for annotation
00:44:56 Segment anything and chatgpt to facilitate annotation
00:51:00 Key responsibilities as a CEO
00:53:58 Metrics for performance evaluation
00:59:56 Building leadership
01:06:06 Advice to aspiring entrepreneurs
01:09:34 Dealing with failures as a CEO
Learn more about Karyna: https://www.linkedin.com/in/karyna-naminas-923908200
Label Your Data: https://labelyourdata.com/
LinkedIn: https://www.linkedin.com/company/label-your-data/
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
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/
Duration:01:14:10
Video recommendations using Machine Learning at Facebook, News feed & Ads ranking | Amey Dharwadker
6/4/2023
Amey Dharwadker works as a Machine Learning Tech Lead Manager at Meta, supporting Facebook's Video Recommendations Ranking team and working on building and deploying personalization models for billions of users. He has also been instrumental in driving a significant increase in user engagement and revenue for the company through his work on News Feed and Ads ranking ML models. As an experienced researcher, he has co-authored publications at various AI/ML conferences and patents in the fields of recommender systems and machine learning. He has undergraduate and graduate degrees from the National Institute of Technology Tiruchirappalli (India) and Columbia University.
Time stamps of the conversation
00:00:46 Introduction
00:01:46 Getting into recommendation systems
00:05:25 Projects currently working on at Facebook, Meta
00:06:55 User satisfaction to improve recommendations
00:08:25 Implicit Metrics to improve engagement
00:11:34 Video vs product recommendations based on fixed attributes
00:13:20 Understanding video content
00:15:55 Working at Scale
00:20:02 Cold start problem
00:22:41 Data privacy concerns
00:24:36 Challenges of deploying machine learning models
00:30:56 Trade-off in metrics to boost user engagement
00:33:47 Introspecting recommender systems - Interpretability
00:37:14 Long video vs short video - how to adapt algorithms?
00:42:17 Being a Machine Learning Tech Lead Manager at Meta - work routine
00:45:00 Transitioning to leadership roles
00:50:55 Tips on interviewing for Machine Learning roles
00:57:23 Machine Learning job interviews
01:02:30 Finding your interest in AI/machine learning
01:05:24 Transitioning to ML roles within the industry
01:08:36 Remaining updated to research
01:12:00 Advice to young computer science students
More about Amey: https://research.facebook.com/people/dharwadker-amey-porobo/
Linkedin: https://www.linkedin.com/in/ameydharwadker/
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
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/
Duration:01:16:06
Using AI to improve maternal & child health in underserved communities of India | Aparna Taneja
5/11/2023
Dr. Aparna Taneja works at Google Research in India on innovative projects driving real-world social impact. Her team collaborates with an NGO called ARMMAN with the mission to improve maternal and child health outcomes in underserved communities of India. Prior to Google she was a Post-Doc at Disney Research, Zurich, and has a PhD from the Computer Vision and Geometry Group in ETH Zurich and a Bachelor's in Computer Science from the Indian Institute of Technology, Delhi.
Time stamps of the conversation
00:00:46 Introductions
00:01:20 Background and Interest in AI
00:03:59 Satellite imaging and AI at Google
00:08:30 Multi-Agent systems for social impact - part of AI for social good
00:10:30 Awareness of AI benefits in non-tech fields
00:13:42 Project SAHELI - improving maternal and child health using AI
00:20:05 Intuition for methodology
00:22:07 Measuring impact on health
00:27:42 Challenges when working with real-world data
00:32:58 Problem scoping and defining research statements
00:38:16 Disconnect between tech and non-tech communities while collaborating
00:43:22 What motivates you, the theoretical or application side of research
00:47:17 What research skills are a must when working on real-world challenges using AI
00:50:33 Factors considered before doing a PhD
00:54:08 Significance of Ph.D. for research roles in the industry
00:58:15 Choosing industry vs Academia
01:02:38 Managing personal life with a research career
01:07:58 Advice to young students interested in AI on getting started
Learn more about Aparna here: https://research.google/people/106890/
Research: https://scholar.google.com/citations?user=XtMi1L0AAAAJ&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/
Duration:01:15:15
Fixing fake news and misinformation online using Robust AI models | Prof. Srijan Kumar
5/3/2023
Dr. Srijan Kumar is an Assistant professor at Georgia Tech with research interests in combating misinformation and harmful content on online platforms, building robust AI models prone to adversarial attacks, and behavior modeling for more accurate recommender systems. Before joining Georgia Tech, he was a postdoctoral fellow at Stanford University and completed his Ph.D. in computer science from the University of Maryland. He has received multiple awards for his research work, including Forbes 30u30 and being named a Kavli Fellow by the National Academy of Sciences.
Time stamps of the conversation
00:01:00 Introductions
00:01:45 Background and Interest in AI
00:05:27 Current research interests
00:09:50 What is misinformation?
00:15:07 ChatGPT and misinformation
00:23:40 How can AI help detect misinformation?
00:39:15 Twitter's Birdwatch platform to detect fake/misleading news
00:56:38 Detecting fake bots on Twitter
01:03:39 Adversarial training to build robust AI models
01:05:31 Robustness vs Generalizability in machine learning
01:11:40 Navigating your interest in the field of AI/machine learning
01:19:22 Doing a Ph.D. and working in Industry vs Academia
01:24:22 Focusing on Quality of Research rather than Quantity
01:31:23 Advice to young people interested in AI
Dr. Kumar's homepage: https://cc.gatech.edu/~srijan/
Twitter: https://twitter.com/srijankedia
Linkedin: https://www.linkedin.com/in/srijankr
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/
Duration:01:33:34
Combining knowledge of clinical medicine and Artificial Intelligence | Emma Rocheteau
3/31/2023
Emma is a final-year medical student at the University of Cambridge and also pursuing her Ph.D. in Machine Learning. With her knowledge of clinical decision-making, she is working on research projects that leverage machine-learning techniques to improve clinical workflow. She will be taking her role as an academic doctor post her graduation.
Time stamps of the conversation
00:00:00 Introduction
00:02:08 From clinical science to learning AI
00:13:15 Learning the basics of Artificial Intelligence
00:20:12 Promise of AI in medicine
00:30:13 Do we really need interpretable AI models for clinical decision-making?
00:38:47 Using AI for more clinically-useful problems
00:50:55 Facilitating interdisciplinary efforts
00:54:06 Predicting length of stay in ICUs using convolutional neural networks
01:03:04 AI for improving clinical workflows and biomarker discovery
01:07:55 Clustering disease trajectories in mechanically ventilated patients using machine learning
01:16:37 ChatGPT for medical research or clinical decision making
01:25:21 Quality over quantity of AI works published nowadays
01:31:07 Advice to researchers
Emma's Homepage: https://emmarocheteau.com/
LinkedIn: https://www.linkedin.com/in/emma-rocheteau-125384132/
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
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/
Duration:01:36:51
Why are Transformer so effective in Large Language Models like ChatGPT
3/29/2023
Understanding why and how transformers are so efficient in large language models nowadays such as #chatgpt and more.
Watch the full podcast with Dr. Surbhi Goel here: https://youtu.be/stB0cY_fffo
Find Dr. Goel on social media
Website: https://www.surbhigoel.com/
Linkedin: https://www.linkedin.com/in/surbhi-goel-5455b25a
Twitter: https://twitter.com/surbhigoel_?lang=en
Learning Theory Alliance: https://let-all.com/index.html
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/
Duration:00:09:43
History of Large Language Models, Trustworthy AI, ChatGPT & more | Dr. Anupam Datta
2/23/2023
Anupam is the co-founder and President of TruEra and prior to that, he was a Professor at Carnegie Mellon University for 15 years. TruEra provides AI solutions that help enterprises use machine learning, improve and monitor model quality, and build trust. His research and other efforts are focused on privacy, fairness, and building trustworthy machine-learning models. He holds a Ph.D. in computer science from Stanford University and Bachelor’s degree in same from IIT Kharagpur in India.
Time stamps of the conversation
00:50 Introductions
01:45 Background and TruEra
05:30 Trustworthy AI
11:55 Validating Large models in the real world
16:15 History of NLP and large language models
29:25 Opportunities and challenges with ChatGPT
36:52 Evaluating the reliability of ChatGPT
39:10 Existing tools that aid explainability
43:12 AI trends to look for in 2023
More about Dr. Datta
Website: https://www.andrew.cmu.edu/user/danupam/
Linkedin: https://www.linkedin.com/in/anupamdatta
Research: https://scholar.google.com/citations?user=oK3QM1wAAAAJ&hl=en
About TruEra: https://truera.com/
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/
Duration:00:46:21
Theory of Machine Learning, Transformer models, ChatGPT & tips for research career | Dr. Surbhi Goel
2/16/2023
Surbhi is an Assistant Professor at the University of Pennsylvania. She got her Ph.D. in Computer Science from UT Austin and prior to joining UPenn as an Assistant Professor, she was a postdoctoral researcher at Microsoft Research NYC in the Machine Learning group. She has research expertise in theoretical computer science & machine learning, with a particular focus on developing theoretical foundations for modern deep learning paradigms. She also is a part of building the Learning Theory Alliance community that organizes and conducts several events useful for researchers and students in their careers.
Time stamps of the conversation
00:00:54 Introduction
00:01:54 Background and research interests
00:05:03 Interest in Machine Learning Theory
00:13:02 Understanding how deep learning works
00:16:30 Transformer architecture
00:25:40 Scale of data and big models
00:31:28 Reasoning in deep learning
00:38:52 Theoretical perspective on AGI, consciousness, and sentience in AI
00:46:00 Remaining updated to the latest research
00:53:38 Should one do a Ph.D.?
00:57:45 Is a Ph.D. mandatory for machine learning industry positions?
01:01:38 What makes a good research thesis?
01:05:30 Some best practices in research
01:12:20 Learning Theory Alliance Group
01:14:25 Job interviews in academia for researchers
01:20:00 Advice to young researchers and students
01:25:02 Decision to become a Professor
Find Dr. Goel on social media
Website: https://www.surbhigoel.com/
Linkedin: https://www.linkedin.com/in/surbhi-goel-5455b25a
Twitter: https://twitter.com/surbhigoel_?lang=en
Learning Theory Alliance: https://let-all.com/index.html
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/
Duration:01:31:25
Making Machine Learning more accessible | Sebastian Raschka
12/29/2022
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/
Duration:01:22:21
Current and future state of Artificial Intelligence in Healthcare | Dr. Matthew Lungren
12/28/2022
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/
Duration:01:05:25
AI for improving clinical trials & drug development, entrepreneurship & AI safety | Charles Fisher
10/31/2022
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/
Duration:01:12:16
Recommendation systems, being an Applied Scientist & Building a good research career | Mina Ghashami
9/14/2022
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/
Duration:01:15:26
Role of a Principal Scientist do & AI in medicine | Alberto Santamaria-Pang, Microsoft
9/12/2022
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/
Duration:01:34:20