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Running inference at scale is both the hardest and most critical challenge for turning AI into a product. Baseten [4] combines software, expertise, and research to deliver an inference platform with everything required to run a production-grade AI-native product, spanning:
Google Assistant is transitioning into Gemini, a more personalized and AI-driven assistant designed to integrate with apps and services while leveraging generative AI.
Google outlines its vision for U.S. AI policy, advocating for investments in AI infrastructure, streamlined government adoption, and international pro-innovation standards to maintain leadership in the AI space.
Japanese AI startup Sakana used its AI Scientist-v2 to generate a paper accepted at an ICLR workshop but withdrew its post-initial review due to transparency concerns. Though Sakana highlights AI's potential, critics argue that human judgment was crucial and acceptance was facilitated by higher workshop acceptance rates. Skeptics remain wary, emphasizing AI's technical flaws and the need for established norms to prevent degrading the scientific review process.
Most Transformer models are normalized with a layer norm. This can be seen as equivalent to normalizing the model to a sphere. However, if you use carefully placed tanh functions, you can eliminate layer norm while maintaining stability. This is now equivalent to normalizing to a hypercube.
DeepMind has released a paper detailing scaling laws for the powerful cross data center training algorithm DiLoCo. These show how stable model training can be even with gradients synchronized across continents.
WSI repurposes the Whisper ASR encoder for multilingual speaker identification using joint loss optimization. It outperforms Pyannote, ECAPA TDNN, and Xvector in recognizing speakers across diverse languages and environments.
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Solda builds AI Sales Agents generating millions of dollars for enterprises – better, faster, and cheaper than humans. Banks and insurers widely use Solda AI to boost revenue.
Toolkit for training VLMs to have improved grounding and reasoning capabilities.
OWL is an agentic framework that seems sensible and performant. It allows for straightforward composition and can even replicate functionality from some closed source agents.
HiRAG introduces a hierarchical knowledge-based approach to Retrieval-Augmented Generation (RAG), improving semantic understanding and indexing in domain-specific tasks.
Luma Chief Scientist Jiaming Song, who created the earliest accelerated algorithm for diffusion models, has unveiled Inductive Moment Matching (IMM)—a new multi-modal pre-training method that surpasses diffusion models with superior sample quality and 10x greater efficiency.
ThunderKittens is a powerful and simple abstraction for writing efficient and performance CUDA kernels. This post explores how to use the framework for the newest Blackwell series of GPUs from Nvidia. The main difference is thinking in terms of data flow.
Many AI models labeled as "open" come with restrictive licensing terms. Google's new Gemma 3 models and similar releases from Meta raise concerns about commercial limitations, potentially impacting smaller companies relying on these technologies.
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Find out how German legal AI startup Xayn is developing highly capable language models while adhering to tight sovereignty, security,
Amazon's VP of Artificial General Intelligence, Vishal Sharma, has confirmed AI's integration across AWS, robotics, and Alexa, highlighting the company's extensive AI deployment.
The PANORAMA study is an international initiative evaluating AI models and radiologists in detecting pancreatic cancer in CECT scans.
Leading AI firms like OpenAI, Microsoft, and Meta are using "distillation" to create more cost-effective models by training smaller systems with a "teacher" LLM.