AutoAdapt automates the design and tuning of domain adaptation... workflows for large language models. It improves performance without requiring additional compute, making deployment more accessible:show more

Sam Altman "Internally, we've reached GPT-4.5, and getting to... GPT-5.5 won't require 100x more compute" - progress in reasoning models and RL techniques have greatly improved compute efficiency - it allows smaller models like internal GPT-4.5 to achieve GPT-6 level performance without requiring 100x more computing powershow more

Introducing Computer for Enterprise Computer runs multi-step workflows across... research, coding, design, and deployment. It routes tasks across 20 specialized models and connects to 400+ applications.show more

we're turning University of Waterloo into a supercomputer! arceus... is a cross-device distributed compute network for training large models, using model/tensor/pipeline parallelism. you can train anything, from deep neural networks to language models on the network. oss & deployment soon!show more

AI is changing design and 3D printing, making it... easier for anyone to create and sell products worldwide. The future of making is here—and it's more accessible than ever. #AI #3DPrintingshow more

Fine-tune FunctionGemma to resolve tool selection ambiguity and build... more accurate agentic workflows. The FunctionGemma Tuning Lab lets you train on your own dataset and test natural language to software actions without writing code or managing infrastructure.show more

Introducing Parallax, the first fully distributed inference and serving... engine for large language models. Try it now: 🧵show more

TIPS: Besides 3D wallpaper, the latest XR Animator also... improves the performance of some Transformers-based AI models. For example, object detection AI based on Transformers is slower but more accurate, and this update makes these models more usable💨show more

The introduction of Workflows, Model Fine-tuning, Apps for Advertising... and more. Get caught up on what happened This Week with Runway.show more

What we’re seeing: frontier robotics teams are rebuilding data... pipelines from scratch because generic licensed datasets aren’t moving their models forward. The next wave won’t be about more data. It’ll be about precise, domain-specific data that actually improves performance. The bottleneck isn’t compute. It’s the right data. 🎥 Our CCO Arthur at Wrapped’s Multichain Day (ETHDenver 🏔🦬🦄)show more

We got our robots to wash pans, clean windows,... make peanut butter sandwiches, and more! Fine-tuning our latest model enables all of these tasks, and this has interesting implications for robotics, Moravec's paradox, and the future of large models in embodied AI. More below!show more

The lack of understanding of how current AI/ large... language models work makes us susceptible to thinking that AI is more powerful and all-knowing than it actually is. That’s what concerns me the most.show more

⚡ GLTF Optimizer Optimize your 3D models for the... web in seconds. Reduce file size, improve performance, and keep your #threejs scenes smooth all without losing quality. Perfect for faster 3D workflows. 👉 Creator: Simon #threejs #WebGL #3D #Optimization #Workflow #Performance #3DDevshow more

New Experiment: SerendipityLM - Interactive evolutionary exploration of generative... design spaces with large language models. Check out the extensive experiment report, incl. many generative art examples & link to open source repo: Find out more in this 🧵show more

To Adapt or Not to Adapt? Real-Time Adaptation for... Semantic Segmentation paper page: The goal of Online Domain Adaptation for semantic segmentation is to handle unforeseeable domain changes that occur during deployment, like sudden weather events. However, the high computational costs associated with brute-force adaptation make this paradigm unfeasible for real-world applications. In this paper we propose HAMLET, a Hardware-Aware Modular Least Expensive Training framework for real-time domain adaptation. Our approach includes a hardware-aware back-propagation orchestration agent (HAMT) and a dedicated domain-shift detector that enables active control over when and how the model is adapted (LT). Thanks to these advancements, our approach is capable of performing semantic segmentation while simultaneously adapting at more than 29FPS on a single consumer-grade GPU. Our framework's encouraging accuracy and speed trade-off is demonstrated on OnDA and SHIFT benchmarks through experimental results.show more

📈 NVIDIA tops AI leaderboards and benchmarks with open... models driven by extreme co-design across compute, networking, memory, storage, and software. This includes models for biology, AI physics, agentic AI, physical AI, robotics, and autonomous vehicles. By being vertically integrated and horizontally open, NVIDIA enables its ecosystem to scale agentic workflows at every layer of the stack with the lowest cost per token.show more

Amera automates claims processing workflows for health insurance payers.... It cuts admin costs by 20-30%, turns hours of manual work into minutes, and helps members access more affordable, transparent care. Congrats on the launch, Deep Kapur & Louise Tanski!show more

Demis Hassabis says despite the advent of models like... DeepSeek, AI needs "more compute than ever" for experimentation and innovation, serving models and inference because "the more computing time you spend, you get more powerful and better answers"show more

🚀 Excited to announce the first release of a... novel open source programming language and platform for language model interaction! Combining prompts, constraints & scripting, LMQL elevates the capabilities of large language models. 🧵1/6 A quick tour.show more

Google presents AudioPaLM: A Large Language Model That Can... Speak and Listen paper page: introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt.show more