LangChain v0.0.40 Add generic SQLAlchemy cache – @benderville Documentation cleanup – h/t @prof_reed Some BIG LLM improvements from @YouSearchEngine team: Support for local @huggingface models Support for streaming tokens from @OpenAI
LLMS
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AI Glossary Series: NLP, NLU, NLG, Speech Recognition Explained
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In this @Cognilytica #AIToday #podcast AI Glossary Series episode 'Natural Language Processing (NLP), NLU, NLG, Speech-to-Text, TTS, Speech Recognition' hosts @rschmelzer & @kath0134 define these terms & share how they fit into #AI. Full episode: https://
cognilytica.com/2022/12/16/ai-
today-podcast-ai-glossary-series-natural-language-processing-nlp-nlu-nlg-speech-to-text-tts-speech-recognition/?utm_source=dlvr.it&utm_medium=twitter
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#NLP #ML -
What Makes ChatGPT Different: Viral Adoption Over Benchmarks
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It's what makes ChatGPT different that should be the focus, because it became viral and more widely used, people claimed it'd replace search engines! The original GPTs had nowhere near this buzz, despite the benchmarks/metrics/reasoning.
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Galactica’s Training on Scientific Papers Improves Logical Reasoning
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Agree, Galactica showed that training on scientific papers as well as code improved logical reasoning in comparison. Orthogonally, there's another hypothesis that the interactive format has value: whether it produces more value to users when used in a interaction loop.
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GDPR Explainability Requirements vs ChatGPT’s Opacity Contradiction
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GDPR: All algorithmic decisions must be explainable.
ChatGPT: BWAHAHAHA!!! -
Labeling Assumptions in Research: A Scientific Necessity
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I'm fine with that if you label those as assumptions. There has been no comparative test of the abilities that are unique to ChatGPT because, by definition, they are only in ChatGPT and no good benchmarks exist. Dismissing a branch of research via assumptions is not scientific.
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Transformers Learn In-Context Through Gradient Descent
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Transformers learn in-context by gradient descent Oswald et al.: https://
arxiv.org/abs/2212.07677 #MachineLearning #DeepLearning #ArtificialIntelligence -
CALM: Dynamic Computational Effort for Language Models
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Presenting Confident Adaptive Language Modeling (CALM), a novel method that allows language models to dynamically modify computational effort when generating text. Learn how CALM can accelerate text generation while preserving output quality → https://t.co/Nm7yyT8sMA pic.twitter.com/MpuaPBzDMU
— Google AI (@GoogleAI) 16 décembre 2022Presenting Confident Adaptive Language Modeling (CALM), a novel method that allows language models to dynamically modify computational effort when generating text. Learn how CALM can accelerate text generation while preserving output quality → https://
goo.gle/3HJKzbM -
GPT SQL Writing Capabilities and UI Transparency for Applications
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Nice work, app shows application to twitter search but the deeper demo is how good GPTs are in writing SQL. Very broadly applicable. wrt UIUX I like that the decoded SQL is available for verification, imo necessary for higher stake applications. https://t.co/70oLMjZj64
— Andrej Karpathy (@karpathy) 16 décembre 2022Nice work, app shows application to twitter search but the deeper demo is how good GPTs are in writing SQL. Very broadly applicable. wrt UIUX I like that the decoded SQL is available for verification, imo necessary for higher stake applications.
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ROSCOE: New Metrics Suite for Evaluating Step-by-Step Reasoning
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ROSCOE is a first-of-its-kind suite of metrics for scoring step-by-step reasoning. By publishing this study we hope to provide a foundation that enables scalable systematic evaluation and benchmarking of new language models. See the paper on arXiv https://
arxiv.org/abs/2212.07919