“The episode discusses the ability to successfully steal the last layer of production language models including ChatGPT and PaLM-2.”

“Markus’ first paper, Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing, focuses on tackling activation quantization issues introduced by the attention mechanism and how to solve them.”

“DreamBooth enables “subject-driven generation,” that is, the creation of personalized generative models using a small set of user-provided images about a subject.”

“The article doesn't contain a direct quote.”

“In our conversation, we discuss the current state of adversarial machine learning research, the dynamic of dealing with privacy issues in black box vs accessible models, what privacy attacks in vision models like diffusion models look like, and the scale of “memorization” within these models.”

“Dr. Lewis's research focuses on the intersection of information retrieval techniques (IR) and large language models (LLMs).”

“The episode discusses Brehmer's paper "Weakly supervised causal representation learning".”

“Noam Brown, a research scientist at FAIR, Meta AI, co-creator of AI that achieved superhuman level performance in games of No-Limit Texas Hold’em and Diplomacy.”

“Beyond interpretability: developing a language to shape our relationships with AI”

“The article doesn't contain a direct quote.”

“The complete show notes for this episode can be found at twimlai.com/go/528.”

“In Tim’s approach, he utilizes a game called NetHack, which is much more rich and complex than the aforementioned environments.”

“The complete show notes for this episode can be found at twimlai.com/go/514.”

“Self-supervised learning is the dark matter of intelligence”

“Georgia describes her experiences as a computer vision researcher prior to the 2012 deep learning explosion, and how the entire landscape has changed since then.”

“The article doesn't contain a direct quote, but the focus is on how gates are used to drive efficiency and accuracy, while decreasing model size.”

“In our conversation, we discuss diversification of NeurIPS, with groups like Black in AI, WiML and others taking huge steps forward, trends in the fairness community, quite a few papers, and much more.”

“We discuss his talk, including how Spotify's large music catalog enables such an experiment to even take place, the methods they use to train algorithms to isolate and remove vocals from music, and how architectures like U-Net and Pix2Pix come into play when building his algorithms.”

“Block sparsity is a property of certain neural network representations, and OpenAI’s work on developing block sparse kernels helps make it more computationally efficient to take advantage of them.”

“The article doesn't contain a direct quote, but it discusses the application of deep learning for warehouse optimization.”