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.Jeff Dean 's full talk at NeurIPS 24 on Machine Learning for Systems and how Google thinks about these things

54,114 Aufrufe • vor 1 Jahr •via X (Twitter)

5 Kommentare

Profilbild von LLMSalaryman
LLMSalarymanvor 1 Jahr

@JeffDean Hats off to the researchers with the balls to name an unrelated lstm algo “llama” in 2024

Profilbild von dreaming android
dreaming androidvor 1 Jahr

@JeffDean Do you have youtube link?

Profilbild von FSDwhisper
FSDwhispervor 1 Jahr

@JeffDean Nice

Profilbild von Vibhu Pandey
Vibhu Pandeyvor 1 Jahr

@JeffDean @dwarkesh_sp

Profilbild von Rekha@03
Rekha@03vor 1 Jahr

@JeffDean Great talk! What are the key takeaways?

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Everybody is talking about recursive self-improvement (RSI) and meta learning. Here is my old 2020 talk about this [1]. It has aged well. Example: humans still define the starts & ends of trials of many modern meta learners. My RSI systems since 1994 LEARN to (re)define them [2]! [1] Meta Learning Machines in a Single Lifelong Trial (talk for workshops at ICML 2020 and NeurIPS 2021, based on earlier talks since 1994). Abstract: the most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987 [2]. Here I summarize our work on meta reinforcement learning with self-modifying policies in a single lifelong trial (since 1994), and mathematically optimal meta-learning through the self-referential Gödel Machine (since 2003). Many additional publications on meta-learning since 1987 can be found in the RSI overview [2]. [2] J. Schmidhuber (AI Blog, 2020-2025). 1/3 century anniversary of first publication on recursive self-improvement (RSI) and meta learning machines that learn to learn (1987). For its cover I drew a robot that bootstraps itself. 1992-: gradient descent-based neural meta learning. 1994-: meta reinforcement learning with self-modifying policies. 1997: meta RL plus artificial curiosity and intrinsic motivation. 2002-: asymptotically optimal meta learning for curriculum learning. 2003-: mathematically optimal Gödel Machine. 2020-: new stuff!

Jürgen Schmidhuber

224,085 Aufrufe • vor 5 Monaten