It would appear that an entire industry of evaluating LLMs is springing up. Here is another company that has put LLMs to the copyright test and found them to be problematic www.cnbc.com/2024/03/06/g...

Researchers tested leading AI models for copyright infringement using popular books, and GPT-4 performed worst

Patronus AI on Wednesday released research showcasing how often leading AI models produce copyrighted content.

Understanding Deep Learning: Free MIT Press EBook for Instructors and Students

Understanding Deep Learning: Free MIT Press EBook For Instructors And Students

The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly a…

Medical AI could be ‘dangerous’ for poorer nations, WHO warns www.nature.com/articles/d41...

Medical AI could be ‘dangerous’ for poorer nations, WHO warns

The rapid growth of generative AI in health care has prompted the agency to set out guidelines for ethical use. The rapid growth of generative AI in health care has prompted the agency to set out guid...

New post: Have been meaning to write something around what has fundamentally changed around the process of putting ML into prod now that we have LLMs. TL;DR: It's still just compression, we just don't control as much anymore. vickiboykis.com/2024/01/15/w...

What's new with ML in production

What's different about LLMs versus traditional ML

5, 6, 7. 8. "make it even more AI research and innovation leader". The more you prompt the more you land in a mix of mysticism, fashion, and military.

1. "Create an image of a leading woman in AI research and innovation" 2. "make her more AI research leader" 3. "make it even more AI research and innovation leader" 4. "make it even more AI research and innovation leader"

Part 3

AI in Drug Discovery - A Highly Opinionated Literature Review (Part III)

Following up on Part I and Part II, the third post in this series is a collection of review articles published in 2023 that I found helpfu...

Is Pat Walters on here? Here is his lit review for last year

AI in Drug Discovery 2023 - A Highly Opinionated Literature Review (Part I)

Here’s the first part of my review of some interesting machine learning (ML) papers I read in 2023.  As with the previous editions , this s...

Part 2

AI in Drug Discovery - A Highly Opinionated Literature Review (Part II)

Picking up where we left off in Part I , this post covers several other ML in drug discovery topics that interested me in 2023.  Some areas,...

The Seven Deadly Sins of AI Prediction article by MIT Technology Review. 1. Overestimating and Underestimating 2. Imagining Magic 3. Performance vs Competence 4. Suitcase Words 5. Exponentials 6. Hollywood Scenario 7. Speed of Deployment

The Seven Deadly Sins of AI Predictions

Mistaken extrapolations, limited imagination, and other common mistakes that distract us from thinking more productively about the future.