The real driver of #MachineLearning innovation is the need to build ever more useful products: https://
linkedin.com/posts/damienbe
nveniste_machinelearning-datascience-artificialintelligence-activity-7057378198384181248-FlJ7
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#RecSys #Martech #BigData #Analytics #DataScience #AI #DataScientists #DeepLearning #NeuralNetworks
INNOVATION
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Machine Learning Innovation Driven by Building Useful Products
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State-of-the-Art Computer Vision Models and Techniques Collection
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Github collection of state-of-the-art #ComputerVision models, techniques, and notebooks: https://
github.com/roboflow/noteb
ooks
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#Python #BigData #DataScience #AI #MachineLearning #DeepLearning #Coding #Python #DataScientists #ObjectDetection #PyTorch #NeuralNetworks -
ChatGPT Writes Short Story Time is a Wasteland
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"Time is a Wasteland" — a short story written by #ChatGPT : https://
medium.com/@kirk.borne/ti
me-is-a-wasteland-a-short-story-written-by-chatgpt-fb01325dab8a
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#GenerativeAI #BigData #DataScience #AI #MachineLearning #DeepLearning #StableDiffusion #ConversationalAI #Chatbot #NLProc #NLU #NLG #NeuralNetworks #GPT3 #GPT4 #LLMs #LargeLanguageModels -
Runway ML generates photorealistic ads indistinguishable from reality
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Este vídeo creado por Runwayml nos muestra un hipotético spot de una bebida refrescante.
— Juan Merodio (@juanmerodio) 4 mai 2023
Aunque todavía le queda mucho camino, al ritmo que esto avanza, muy pronto estos vídeos serán indistinguibles de la realidad y esto nos tiene que dar mucho en lo que pensar… pic.twitter.com/1uoLcFTvEHEste vídeo creado por Runwayml nos muestra un hipotético spot de una bebida refrescante. Aunque todavía le queda mucho camino, al ritmo que esto avanza, muy pronto estos vídeos serán indistinguibles de la realidad y esto nos tiene que dar mucho en lo que pensar…
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Advanced AI Features Coming to JavaScript
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Some really cool and advanced features making their way to JS! Constitutional AI Chain Time weighted retriever LLMChain Extractor (compresses documents post retrieval)
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Forking and Routing: Building Blocks for AI Chain Construction
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Why add this? We view this as a basic building block for constructing chains Just as you could want to run a sequence of chains sequentially, there's also the basic building block of forking and routing to the correct chain What are some of the use cases?
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RouterChain Dynamic Prompt Selection with LangChain
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MultiPromptChain This notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects the prompt to use for a given input Basically, route between prompts depending on user input! Docs: https://
python.langchain.com/en/latest/modu
les/chains/examples/multi_prompt_router.html
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MultiRetrievalQAChain: Dynamic Retrieval System Routing
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MultiRetrievalQAChain This notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects which Retrieval system to use Can be used to route questions to the correct retriever (based on content or intent) Docs: https://
python.langchain.com/en/latest/modu
les/chains/examples/multi_retrieval_qa_router.html
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Router Chains: LangChain Classification for Dynamic Chain Selection
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Router Chains A simple (yet much requested) abstraction that started with a @ShreyaR pr months ago and is finally in @langchain
! – Router Chain does classification to choose sub chain to use
– Call the selected chain with that input Lots of potential use cases!