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109 articles
Researchers are looking to the infant brain for inspiration as they seek to develop more intelligent AI systems. Babies' natural learning abilities offer a promising path toward creating AI that learns efficiently and adapts like humans do.
Prime Intellect has released Verifiers v1, a modular platform for agentic reinforcement learning training and evaluations, featuring tasksets, harnesses, and runtimes.
This article explains the concept of AI safety and how OpenAI's restructuring of its research and safety teams reflects a critical shift toward embedding safety considerations directly into AI development processes.
Hugging Face CEO Clem Delangue emphasizes the growing importance of open source AI, highlighting its role in democratizing access and accelerating innovation in the industry.
Sixtyfour's innovative 'Eval Stack' approach challenges traditional AI development by rigorously evaluating agents against expert benchmarks, ensuring accuracy over assumption.
Google AI Studio now allows developers to import code from GitHub directly into Build mode, streamlining the process of creating and deploying AI applications.
Google AI Studio introduces 'Import from GitHub' in Build mode, allowing developers to convert existing repositories into editable, deployable AI applications with ease.
Amazon Web Services is shutting down its crowdsourcing service Mechanical Turk, the original 'Artificial Artificial Intelligence', starting July 30, 2026.
Learn what Amazon's Mechanical Turk was, how it helped train AI systems, and why it's being shut down. This explains the role of human workers in AI development.
Meta's AI agent push is moving slower than planned, with CEO Mark Zuckerberg acknowledging internal restructuring challenges, while the AI chief remains more optimistic.
This article explains how language model fine-tuning and personalization work in AI systems, using the comparison between Gemini and Claude's email reply generation as a practical example.
Google AI experts explore the concept of full-stack AI infrastructure, defining the integrated system required for modern AI development and deployment. The discussion highlights the comprehensive approach needed to support end-to-end AI workflows from data management to model monitoring.
