The Top ML Papers of the Week (Jan 22 – Jan 28): – WARM
– Medusa
– AgentBoard
– MambaByte
– Knowledge Fusion of LLMs
– Resource-efficient LLMs & Multimodal Models
…
LLMS
-
Top Machine Learning Papers Week January 22-28
By
–
-
LangChain Improves Function and Tool Calling Documentation
By
–
Updated tool calling docs More and more model APIs are starting to expose `function` and `tool` calling parameters We've improved the DevEx around this, and updated key docs Function calling guide: https://
python.langchain.com/docs/modules/m
odel_io/chat/function_calling
… Tool calling output parser: https://
python.langchain.com/docs/modules/m
odel_io/output_parsers/types/openai_tools
… -
Mathematics and Art Intersection in Data Science and AI
By
–
Math and Art! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/Math-Art -
LLMs Face Text Data Scarcity, Video Data Offers New Opportunities
By
–
LLMs may be running out of text data: they've already scraped most of the web.
— Erik Brynjolfsson (@erikbryn) 28 janvier 2024
But beyond that, there are many other types of data, notably video. But that will require new approaches.@DaphneKoller and @ylecun explain it well in this short clip.https://t.co/spaJV6Io3YLLMs may be running out of text data: they've already scraped most of the web. But beyond that, there are many other types of data, notably video. But that will require new approaches. @DaphneKoller and @ylecun explain it well in this short clip.
-
Chatbot Arena: Language Models Racing for Global Champion Status
By
–
chatbot arena is a really fun measure of progress. it's exciting to see new models pop up with surprisingly high ELO scores language model development is turning into international chess, with a public leaderboard and a very clear global champion at all times
-
Tokenization in NLP: Breaking Down Text into Pieces
By
–
In NLP we breaking down text into smaller pieces, commonly known as tokens, usually words or subwords. An essential initial step in that transforms raw text into a format that can be consumed by embedding(vectors) generators. Here's how we do it in a typical Text Dataset Class:
-
Is ChatGPT the Ask Jeeves of AI generation?
By
–
I wonder sometimes if ChatGPT is the Ask Jeeves of this generation. Then who the Google is.
-
SageMaker Retrieval-Augmented Generation for Data Science
By
–
SageMaker Retrieval-Augmented Generation! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #Books #100DaysofCode https://
geni.us/SageMaker-RAG -
AWS RAG Architecture for AI and Data Science Applications
By
–
Retrieval Augmented Generation Architecture based on #AWS! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #CloudComputing #DataScientist #Linux #Programming #Coding #Books #100DaysofCode https://
geni.us/RAG-Architectu
re
…
