La diferencia es que en el caso de OpenAI usaron como datos de entrenamiento vídeos con gameplays de partidas humanas. Es decir, en cierta forma la IA aprendía de forma supervisada del humano. Pero aquí no. Esto es puro RL -ensayo y error- donde la IA aprende DESDE CERO…!!!
INNOVATION
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Why Manufacturers Need Industrial IoT in 2023
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Why #manufacturers need #IIoT in 2023 http://
ow.ly/Jsoh50MgOlG @fabot70 @RTek_Man_Ltd via @fogoros #OpenAI #iiot #industry40 #digitaltransformation #manufacturing #manufacturingindustry #manufacturingsector -
Tech Migration Shifts Content Distribution on Network Platform
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ugh gonna be confusing for me; still find more non-tech interesting stuff on the child king network, tech moved quite a bit tho.
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FAISS Wrapper Now Returns Similarity Scores in Semantic Search
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Return scores during semantic search – h/t @krrish_dh In addition to getting back the top documents from a vectorstore, the FAISS wrapper now supports returning similarity scores for those documents as well Documentation: https://
langchain.readthedocs.io/en/latest/modu
les/utils/combine_docs_examples/vectorstores.html#faiss-specific
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WolframAlpha Integration Tool for LangChain Framework
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WolframAlpha integration – @nickscamara_ Support for using Wolfram Alpha as a tool Collab Notebook: https://
colab.research.google.com/drive/1AAyEdTz
-Z6ShKvewbt1ZHUICqak0MiwR
… Documentation: https://
langchain.readthedocs.io/en/latest/modu
les/utils/examples/wolfram_alpha.html
… s/o to others @brucehammer for suggesting this a few weeks ago, and others in the discord for reviewing! -
v0.0.60 Release: WolframAlpha Integration and Memory Support
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v0.0.60 – big release! WolframAlpha integration – @nickscamara_ Multiple Memory support – @diwanksingh Doc improvements – @Yoooongtae Return scores during semantic search – h/t @krrish_dh Add namespaces to @pinecone – @m_morzywolek Deployment templates
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Developer Returns to Coding After 1.5 Years Burnout
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It’s funny cause I didn’t write code in like 1.5 years, after selling and burning out. I’ll come back to it slowly tho.
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Exploring Generative AI: Practical Examples and Applications
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Looking to explore these further? We dive into specific examples, and more, in our latest blog post on generative AI. Read it here: https://
txt.cohere.ai/generative-ai-
part-2/
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Simplifying Complex Text: Key Concepts Extraction Technique
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6. Simplify the complex When you want to rewrite a piece of text into a simpler, more accessible copy. Example use cases:
– Simplifying technical explanations
– Understanding complex text
– Extracting key concepts from a passage -
Condensing Information: A Key Prompt Engineering Technique
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5. Condense information When you want to get a summarized version of a document that strips it to its essence. Example use cases:
– Summarizing reports
– Getting the TLDR of articles
– Generating the key points from a podcast