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OpenAI has unveiled GPT-5, its most capable model to date, featuring native multimodal reasoning across text, images, audio, and video. The model supports a 2 million token context window and demonstrates significant improvements in mathematical reasoning and code generation.
Anthropic has closed a $5 billion funding round led by Lightspeed Venture Partners, valuing the company at $60 billion. The funds will be used to expand compute infrastructure and accelerate Claude's development.
DeepMind's latest AlphaFold iteration achieves a 50% improvement in predicting how proteins interact with other molecules, including DNA, RNA, and small molecules. The breakthrough could accelerate drug discovery timelines by years.
Meta has open-sourced Llama 4, a 400B parameter model with 256K context window support. The model outperforms GPT-4 on several benchmarks while being available for free commercial use.
The European Union's AI Act has entered its first enforcement phase, requiring foundation model providers with over 10^25 FLOPs to comply with transparency obligations.
Hugging Face has released Inference API v2, promising up to 10x speedup for popular open-source models through optimized kernel fusion and dynamic batching.
Researchers at Microsoft have published a new prompt optimization method called 'AutoPrompt v3' that automatically refines prompts for specific tasks.
In a widely-shared blog post, Simon Willison argues that the most impactful AI developments are happening in small, efficient models rather than frontier-scale systems.
A new paper from researchers at Stanford and MIT achieves 99.2% top-1 accuracy on ImageNet using a novel training technique called 'Progressive Distillation'.
Amazon Web Services has announced Trainium3, the next generation of its AI training chips, promising 4x performance per watt over Trainium2.
A lively discussion on r/MachineLearning explores the limitations of current AI safety benchmarks and proposes new evaluation frameworks.
NVIDIA has released CUDA 12.8 with native Python bindings, making GPU programming more accessible to Python developers without C++ knowledge.