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AI Trends 2023: Causality and the Impact on Large Language Models with Robert Osazuwa Ness - #616
Data-Centric Zero-Shot Learning for Precision Agriculture with Dimitris Zermas - #615
How LLMs and Generative AI are Revolutionizing AI for Science with Anima Anandkumar - #614
AI Trends 2023: Natural Language Proc - ChatGPT, GPT-4 and Cutting Edge Research with Sameer Singh - #613
AI Trends 2023: Reinforcement Learning - RLHF, Robotic Pre-Training, and Offline RL with Sergey Levine - #612
Supporting Food Security in Africa Using ML with Catherine Nakalembe - #611
Service Cards and ML Governance with Michael Kearns - #610
Will ChatGPT take my job? - #608
Real-Time ML Workflows at Capital One with Disha Singla - #606
Stable Diffusion & Generative AI with Emad Mostaque - #604
Exploring Large Language Models with ChatGPT - #603
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This was a simply amazing episode. so much depth of information about real life and life changing AI/ML
Best podcast on machine learning an ai
Thanks a lot for introducing us to the genius of our age. Tremendously inspiring.
A very good insightful episode, Maki Moussavi explains the various points in a lucid manner. Truly, we are the captain of our life's ship. We are responsible for our own emotions and actions. Being proactive rather than reactive is the key to success and happiness! I will be reading this book! Thanks for sharing this interesting podcast. Have a great day!
I love this channel and all the great podcasts. The topics are very relevant and the speakers are well informed experts so the episodes are very educative. Only request, please change the opening music note of the podcast. It is very unpleasant tune sets a jarring effect right at the beginning. Otherwise all these episodes are very interesting in the field of innovations in Artificial Intelligence and Machine Learning! Regards!
so smart you can smell it
Phenomenal discussion. Thank you! Particularly enjoyed the parts on generative models and the link to Daniel Kahneman.
This is a very realistic and proper episode which explains quantum computing even as alone.
Hello all, Thanks for podcast Can we combine the two agent learnings from same environment to find the best actions Thanks
notes : * Data scientists are not trained to think of money optimisations. plotting cpu usage vs accuracy gives an idea about it. if u increase data 4x as much just to gain 1% increase in accuracy that may not be great because you're using 4 times as much CPU power * a team just decicated to monitoring. i. monitor inputs : should not go beyond a certain range for each feature that you are supposed to have. Nulls ratio shouldn't change by a lot. ii. monitor both business and model metrics. sometimes even if model metrics get better ur business metrics could go low....and this could be the case like better autocompletion makes for low performance spell check OR it could also depend upon other things that have changed. or seasonality. * Data scientists and ML engineers in pairs. ML Engineers get to learn about the model while Data Scientists come up with it. both use same language. ML Engineers make sure it gets scaled up and deployed to production. * Which parameters are somewhat stable no matter how many times you retrain vs what parameters are volatile. the volatile ones could cause drastic changes. so u can reverse engineer this way.
great podcast. do we reference to papers that were discussed by Ganju. good job
Super.. very informative. Thanks
there is no content lol. Host, please invite real scientists
This is an incredible interview. Dopamine as a correlate of prediction error makes so much sense. Best Twiml talk to date!
conversations drag too much. gets boring. stop the marketing and get to the content