The @OpenAI retrieval API seems to be doing basic top-k RAG on limited context if there's context overflows.
Asking for a summary of the Uber 10-K (~150k-200k tokens), returns oddly specific stuff around acquisitions and legal proceedings 👇
Huge day indeed for AI and LLMs, congrats to Meta 👏
This is now the most capable LLM available directly as weights to anyone from researchers to companies.
The models look quite strong, e.g. Table 4 in the paper: MMLU is good to look at, the 70B model is just below GPT-3.5. But…
Huge day indeed for AI and LLMs, congrats to Meta 👏
This is now the most capable LLM available directly as weights to anyone from researchers to companies.
The models look quite strong, e.g. Table 4 in the paper: MMLU is good to look at, the 70B model is just below GPT-3.5. But… https://t.co/bY66FiadVE
I'm excited about Segment Anything released from FAIR today. It tackles an old problem (find objects in images) at large scale: trained on 11M images and 1B objects.
This is a new Foundation Model for Computer Vision - it recognizes any object in any context.
Excited to share the first of a series of @GoogleAI blog posts summarizing our research work from 2022. This covers language & multimodal models, computer vision, and generative models. We'll have ~7 posts covering other areas over next few weeks!
ai.googleblog.com/2023/01/google…
This is a great thread, +1e100. @karpathy didn't mention my (biased!) favorite example of 2021. Models designed to generate words (transformers) & model language (BERT) were reused in #AlphaFold to solve the protein folding problem, mapping a bunch of letters, to 3D coordinates🤯
This is a great thread, +1e100. @karpathy didn't mention my (biased!) favorite example of 2021. Models designed to generate words (transformers) & model language (BERT) were reused in #AlphaFold to solve the protein folding problem, mapping a bunch of letters, to 3D coordinates🤯
Pay attention to MLPs: arxiv.org/abs/2105.08050
We show that MLPs with gating work well for key applications that Transformers are good at: BERT for NLP and ViT for vision.
Is MLP all you need?
Barlow Twins: a new super-simple self-supervised method to train joint-embedding architectures (aka Siamese nets) non contrastively.
arxiv.org/abs/2103.03230
1/N
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