Economic impacts research at OpenAI
Call for expressions of interest to study the economic impacts of large language models.
Safety & Alignment
Book, deep-learning, Machine Learning
Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn has been a long time in the making, and I am excited to finally get to talk about the release of my new book…
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Understanding Deep Learning Algorithms that Leverage Unlabeled Data, Part 1: Self-training
Deep models require a lot of training examples, but labeled data is difficult to obtain. This motivates an important line of research on leveraging unlabeled data, which is often more readily available. For example, large quantities of unlabeled imag…
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Stanford AI Lab Papers and Talks at AAAI 2022
The 36th AAAI Conference on Artificial Intelligence (AAAI 2022) is being hosted virtually from February 22th – March 1st. We’re excited to share all the work from SAIL that’s being presented, and you’ll find links to papers, videos and blogs below. F…
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Learning with not Enough Data Part 2: Active Learning
This is part 2 of what to do when facing a limited amount of labeled data for supervised learning tasks. This time we will get some amount of human labeling work involved, but within a budget limit, and therefore we need to be smart when selecting whi…
ai, business
Top 10 Artificial Intelligence Trends To Watch In 2022
Hi there! If you ended up here, you might be interested in the current report for 2023, available here: Top 10 AI Trends to Watch in 2023. Every year, artificial intelligence takes a giant leap forward. The only way to keep up with the pace of change is to hear from those at the […]
Artykuł Top 10 Artificial Intelligence Trends To Watch In 2022 pochodzi z serwisu DLabs.AI.
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EvoJAX: A Hardware-Accelerated Neuroevolution
EvoJAX is a hardware-accelerated neuroevolution toolkit built on top of JAX. It can help run a wide range of evolution experiments within minutes on a TPU/GPU, compared to hours or days on CPU clusters.
Redirecting to github.com/google/evojax/, where the repo resides.
Research
Solving (some) formal math olympiad problems
We built a neural theorem prover for Lean that learned to solve a variety of challenging high-school olympiad problems, including problems from the AMC12 and AIME competitions, as well as two problems adapted from the IMO.