AI Research and Development: LLMs, Cloud Partnerships, and Scientific Exploration

A collection of recent developments in the AI landscape, covering improvements in Large Language Model output structuring, major cloud provider partnerships to accelerate AI innovation, practical applications of vector search, and research into the potential and risks of autonomous AI scientists and scaling agent learning.

Improving Structured Outputs in the Gemini API

📝This article highlights recent improvements to Structured Outputs in the Gemini API, making it easier for developers to extract data in a structured format.

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AWS and OpenAI announce multi-year strategic partnership

📝This article is about a major partnership between OpenAI and AWS, a significant development in the AI landscape, and impacting ML workflows.

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How to Use Vector Search to Build a Movie Recommendation App

📝This tutorial provides a practical guide to building a movie recommendation app using ScyllaDB’s vector search, which is a trending topic in ML.

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Jr. AI Scientist and Its Risk Report: Autonomous Scientific Exploration

from a Baseline Paper

📝This paper introduces Jr. AI Scientist, a state-of-the-art autonomous AI scientist system that can analyze its limitations, formulate novel hypotheses for improvement, validate them through experimentation, and write a paper with the results.

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Scaling Agent Learning via Experience Synthesis

📝This article discusses Scaling Agent Learning via Experience Synthesis introducing DreamGym, a unified framework to synthesize diverse experiences enabling scalable agent rollout collection for RL, improving RL training.

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