Superslow AI Newsletter
Writing about (what I think are) the most interesting stories in AI. Part news, part research, part fun.
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Interpreting AI 2027's timelines in light of METRs long task research
What are embeddings and how do they work? Let's dive in.
Diving into Meta's Llama 2 model and how it compares to SOTA open- and closed-source LLMs.
Bigger isn't always better in LLMs. Larger context windows fail across a variety of LLM tests.
Models suffer from catastrophic forgetting and data poisoning when trained on synthetic data, new research shows.
GPT-4 is rumored to be a Mixture-of-Experts, totaling 1.76T parameters. How does MoE work and why is it so powerful?
How red-teaming regulates LLMs, synthetic vs real data and fun out-painting with Photoshop.
What are the Chinchilla scaling laws? How to read DeepMind's paper on Compute-Optimal Scaling Laws
Today we’re going to break down what CNNs are, how they work, and what applications we’re seeing them in so far.
Knowledge Distillation, Neuralink’s Human-Trials, and QLoRA.
Constitutional AI, Dromedary and biased human-labeled datasets.
