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 research.google
PaLI: Scaling Language-Image Learning in 100+ Languages
| | blog.research.google
A decoder-only foundation model for time-series forecasting Google Research Blog
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| | [AI summary] Google Research introduces TimesFM, a decoder-only foundation model for time-series forecasting with zero-shot capabilities, pre-trained on 100 billion real-world time-points, outperforming existing methods in various domains.
| | deepmind.google
2023: A Year of Groundbreaking Advances in AI and Computing - Google DeepMind
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| | This has been a year of incredible progress in the field of Artificial Intelligence (AI) research and its practical applications.
| | minish.ai
Model2Vec as a fasttext alternative | Minish
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| | Fast open-source NLP models and packages
| | www.greaterwrong.com
Role Architectures: Applying LLMs to consequential tasks - LessWrong 2.0 viewer
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| TL;DR:Strong problem-solving systems can be built from AI systems that play diverse roles, LLMs can readily play diverse roles in role architectures, and AI systems based on role architectures can be practical, safe, and effective in undertaking complex and consequential tasks. This article explores the practicalities and challenges of aligning large language models (LLMs[1]) to play central roles in performing tasks safely and effectively. It highlights the potential value of Open Agency and related role architectures in aligning AI for general applications while mitigating risks.