1. Temporal Dynamics of Competition between Statistical Learning and Episodic Memory in Intracranial Recordings of Human Visual Cortex by Sherman et al. (2022). The brain draws on past experiences to make predictions on the future. Research from Yale University suggests an adaptive role for prediction in regulating when we form new memories. Intracranial EEG was used to quantify predictions by the brain during a statistical learning task and link the strength of these predictions to subsequent episodic memory behaviour.
  2. Meaningful by Scott Alexander at Slate Star Codex. Given all the debate about “do Chat GPT and consorts understand what they’re saying?” it’s time for this old-but-gold article on what understanding is even supposed to mean.
  3. Does GPT-4 Really Understand What We’re Saying? Brian Gallagher interviewing David Krakauer for Nautilus. Part II on “understanding”.
  4. Stanford University’s 2023 AI Index Summary by James Vincent at The Verge). Of the significant new machine learning models released in the past year, 32 came from private industry while three came from academia.
  5. Monolith: The Recommendation System Behind TikTok by Josh Tobin at The Gantry Blog. A closer look at undoubtedly one of the best recommendation engines on the planet.
  6. Awesome Twitter Algo by Igor Brigadir and Vicki Boykis. An exploration of the Twitter recommendation algorithm code that was recently released, as well as key papers and related resources.
  7. How many people actually meet physical activity guidelines? Research review by Greg Nuckols at Stronger by Science. An analysis of studies shows that roughly 17% of people do enough aerobic and muscle strengthening exercise, about 5% of people do enough muscle strengthening exercise but not enough aerobic exercise, about 55% of people enough aerobic exercise but not muscle strengthening exercise, and about 22% of people not enough muscle strengthening or aerobic exercise (according to health guidelines).