Google PR counterreaction begins, from Jeff Dean, reminding everyone of the extensive contributions from Google Research across LLMs, ViTs, Multimodal, and Generative text/audio/video: https://
ai.googleblog.com/2023/01/google
-research-2022-beyond-language.html?m=1
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LLMS
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Google Research Responds to AI Criticism with 2022 Achievements Overview
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Memory-Augmented LLMs Achieve Computational Universality
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Google Brain & Alberta U Paper Confirms the Computational Universality of Memory-Augmented Large Language Models https://
syncedreview.com/2023/01/18/goo
gle-brain-alberta-u-paper-confirms-the-computational-universality-of-memory-augmented-large-language-models/
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Cross-attention in Transformer architecture article recommendation
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nice article on cross-attention as supplementary too https://
vaclavkosar.com/ml/cross-atten
tion-in-transformer-architecture
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Clarification on Query and Key in Attention Mechanisms
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nice, exactly! 🙂
(except you're swapping q's and k's – q is the query, the "what am i looking for", k is the key, the "what do i have", and in encoder-decoder the key,value from come from side. admittedly confusing because in dictionaries the _key_ is the "lookup" information.) -
Excitement for 2023 Language AI Research and Team Achievements
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Delighted to see the work from our team (chain-of-thought prompting, emergent abilities, self-consistency, med-palm) featured here, and super excited for language AI research to continue in 2023!
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Google AI Research 2022: Language Models and Generative AI
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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! https://
ai.googleblog.com/2023/01/google
-research-2022-beyond-language.html
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AI21Labs Experimental Models Now Available for Integration
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Also, highlighting one of the earlier points, its now possible to use some of the @AI21Labs experimental models! “`llm = AI21(maxTokens=10, model="j1-grande-instruct")“` Looking forward to playing around with this
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Improved Documentation for Custom LangChain Agents
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Better documentation for understanding how custom agents work @samcwl added some much needed comments in both the documentation and the below notebook on the expected LLM output to get it to work with custom agents https://
langchain.readthedocs.io/en/latest/modu
les/agents/examples/custom_agent.html
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v0.0.65 Release: Documentation Fixes and AI Model Support
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v0.0.65 Doc Fixes – Francis, Chetanya Rastogi
Prompt improvements – @fpingham @brunotorious Support for @AI21Labs experimental models – xettrisomeman
Fix default @CohereAI embedding model – Sasmitha Manathunga Some doc improvements, listed -
FAISS Vectorstore Saving and Loading Guide
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Instructions for saving and loading FAISS vectorstore You just want to create your vectorstore once, and then pass it around. We added some easy documentation on how to do so. https://
langchain.readthedocs.io/en/latest/modu
les/utils/combine_docs_examples/vectorstores.html#saving-and-loading
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