Summarizing https://importai.substack.com/p/import-ai-341-neural-nets-can-smell
Here's my try:
The article discusses the potential impact of an active learning technique that works with transformers being published on arXiv tomorrow. It also covers recent developments in AI, including neural nets being able to smell, technofeudalism via AI, and China's release of another solid open access model. The Baichuan 2 paper contains a few more hints that usual - it indicates that the team is working with machines typically equipped with eight A800 GPUs, and that the overall cluster involves "thousands of GPUs", with a single training run taking place on 1,024 NVIDIA A800s.
The article also discusses MADLAD-400: A Multilingual And Document-Level Large Audited Dataset (arXiv) and MADLAD-400: A Multilingual And Document-Level Large Audited Dataset (GitHub), which are datasets comprising of more than ~400 distinct languages spread across 3 trillion tokens (5 trillion for the uncleaned and therefore noisier dataset). The authors gathered the dataset by training a LangID model on
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2023-09-26 01:09:36
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2023-09-26 01:09:36Event JSON
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"content": "Summarizing https://importai.substack.com/p/import-ai-341-neural-nets-can-smell\nHere's my try:\n\nThe article discusses the potential impact of an active learning technique that works with transformers being published on arXiv tomorrow. It also covers recent developments in AI, including neural nets being able to smell, technofeudalism via AI, and China's release of another solid open access model. The Baichuan 2 paper contains a few more hints that usual - it indicates that the team is working with machines typically equipped with eight A800 GPUs, and that the overall cluster involves \"thousands of GPUs\", with a single training run taking place on 1,024 NVIDIA A800s.\nThe article also discusses MADLAD-400: A Multilingual And Document-Level Large Audited Dataset (arXiv) and MADLAD-400: A Multilingual And Document-Level Large Audited Dataset (GitHub), which are datasets comprising of more than ~400 distinct languages spread across 3 trillion tokens (5 trillion for the uncleaned and therefore noisier dataset). The authors gathered the dataset by training a LangID model on\n",
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