Three different terms, three different architectures, one underlying problem: how do you connect a large language model to the rest of the…Continue reading on Towards AI »
Reinforcement-learning agents — AI systems that learn by trial and error — can convert computation into new knowledge. That’s the focus of a new engineering-level collaboration between NVIDIA and Ineffable Intelligence, the London-based AI lab founded by AlphaGo architect David Silver in the wake of Ineffable’s emergence from stealth last week. “The next frontier of […]
Thinking Machines Lab has introduced a research preview of TML-Interaction-Small, a 276B parameter Mixture-of-Experts model with 12B active parameters, built around a multi-stream, time-aligned micro-turn architecture that processes 200ms chunks of audio, video, and text simultaneously — eliminating the need for external voice-activity detection harnesses. Unlike standard turn-based models that freeze perception during generation, the system runs two components in parallel: a real-time interaction model that maintains continuous full-duplex exchange with the user, and an asynchronous background model that handles sustained reasoning and tool use while sharing the full conversation context throughout.
The post Mira Murati’s Thinking Machines Lab Introduces Interaction Models: A Native Multimodal Architecture for Real-Time Human-AI Collaboration appeared first on MarkTechPost.
Researchers at Tilde Research have released Aurora, a new optimizer for training neural networks that addresses a structural flaw in the widely-used Muon optimizer. The flaw quietly kills off a significant fraction of MLP neurons during training and keeps them permanently dead. Aurora comes with a 1.1B parameter pretraining experiment, a new state-of-the-art result on […]
The post Tilde Research Introduces Aurora: A Leverage-Aware Optimizer That Fixes a Hidden Neuron Death Problem in Muon appeared first on MarkTechPost.
Every AI answer feels effortless — but somewhere, the meter is running.Continue reading on Medium »
Researchers from Meta FAIR and Stanford propose three inference methods for the Byte Latent Transformer that reduce memory-bandwidth cost by over 50% without subword tokenization.
The post Meta and Stanford Researchers Propose Fast Byte Latent Transformer That Reduces Inference Memory Bandwidth by Over 50% Without Tokenization appeared first on MarkTechPost.
Article Overview Evaluate data engineering services by moving beyond price to focus on governance and low-latency logic. Select data engineering companies that prioritize business outcomes and unit economics over simple data movement. Audit data engine…
AI just quietly crossed the line from experimental technology to critical infrastructure — and most people haven’t realized it yet.Continue reading on Technology Hits »
Sakana AI and NVIDIA Researchers demonstrate that simple L1 regularization can induce over 99% sparsity in feedforward layers with negligible downstream performance impact, and translate that sparsity into real GPU throughput gains using new sparse data formats and fused CUDA kernels.
The post Sakana AI and NVIDIA Introduce TwELL with CUDA Kernels for 20.5% Inference and 21.9% Training Speedup in LLMs appeared first on MarkTechPost.
We built incredible AI tools. Then we built walls between them, and forgot to lay the road infrastructure.7 min read — by Vektor Memory · vektormemory.comHow Via solves the context amnesia problem across Claude, Cursor, Windsurf, ChatGPT and every othe…