Explore the Global Generative AI Landscape 2024 by AIport
Was this email forwarded to you? Sign up here Our friends from AIport – an online community of AI writers and practitioners – have just released Volume I of the Global Generative AI Landscape 2024. This landscape provides a comprehensive analysis in a reader-friendly format, revealing how international companies are driving GenAI development. The study covers six different continents and extends far beyond the usual focus of American and European entities, spanning a whopping four times more countries than similar projects. Read the report and view the full landscape here to get exclusive insights into 107 international companies, developing 128 generative models. You’re on the free list for TheSequence Scope and TheSequence Chat. For the full experience, become a paying subscriber to TheSequence Edge. Trusted by thousands of subscribers from the leading AI labs and universities. |
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One AI for Navigating Any 3D Environment
Sunday, March 17, 2024
A very impressive new model created by Google DeepMind is able to follow language instructions in any 3D environment. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
📌 Exciting news! The speaker lineup for apply() 2024 is now live
Friday, March 15, 2024
The speaker lineup for apply() 2024 is now live and we can't wait to show you! Join industry leaders, starting Wednesday, April 3rd at 9AM PT, from LangChain, Meta, Pinterest, Samsung, Vanguard,
Edge 378: Meet TimesFM: Google's New Foundation Model for Time-Series Forecasting
Friday, March 15, 2024
The model is about 200M parameters and has been trained in over 100 billion data points. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 377: LLM Reasoning with Reinforced Fine-Tuning
Tuesday, March 12, 2024
A very recent LLM reasoning technique created by ByteDance research. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
📝 Guest Post: Evaluating LLM Applications*
Monday, March 11, 2024
To successfully build an AI application, evaluating the performance of large language models (LLMs) is crucial. Given the inherent novelty and complexities surrounding LLMs, this poses a unique
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