10 principais timdettmers.com concorrentes

idiap.ch

The following positions are currently available at the Idiap research institute.

darioamodei.com

Dario Amodei is the CEO of Anthropic, a public benefit corporation dedicated to building AI systems that are steerable, interpretable and safe.

natolambert.com

Nathan Lambert's library of talk slides, recordings, and presentations on reinforcement learning, language models, and AI research.

80000hours.org

We took 10 years of research and what we've learned from advising 1,000+ people on how to build high-impact careers, compressed that into an eight-week course to create your career plan, and then compressed that into this three-page summary of the main points. (It's especially aimed at people who want a career that's both satisfying and has a significant positive impact, but much of the advice applies to all career decisions.) 1. Use these factors to clarify what a successful career looks like.

lumenova.ai

Explore the Responsible AI category for insights on AI governance, ethical innovation, regulations, and strategies driving accountability in AI systems.

situational-awareness.ai

Leopold Aschenbrenner, June 2024 You can see the future first in San Francisco. Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to

eleuther.ai

Memorization, or the tendency of large language models (LLMs) to output entire sequences from their training data verbatim, is a key concern for safely deploying language models. In particular, it is vital to minimize a model's memorization of sensitive datapoints such as those containing personal i

arxiv.org

A system of non-tradable credits that flow between individuals like karma, hence proposed under that name, is a mechanism for repeated resource allocation that comes with attractive efficiency and fairness properties, in theory. In this study, we test karma in an online experiment in which human subjects repeatedly compete for a resource with time-varying and stochastic individual preferences or urgency to acquire the resource. We confirm that karma has significant and sustained welfare benefits even in a population with no prior training. We identify mechanism usage in contexts with sporadic high urgency, more so than with frequent moderate urgency, and implemented as a simple (binary) karma bidding scheme as particularly effective for welfare improvements: relatively larger aggregate efficiency gains are realized that are (almost) Pareto superior. These findings provide guidance for further testing and for future implementation plans of such mechanisms in the real world.

lesswrong.com

A community blog devoted to refining the art of rationality