The race to dominate AI requires billions in investment for research, infrastructure, and talent, with companies like Google and OpenAI spending heavily to avoid irrelevance. Falling behind in AI mean…
An £8.2bn AI datacentre project in Lanarkshire, Scotland, by US firm CoreWeave and Scottish company DataVita faces a power supply issue despite promises of on-site renewable energy by 2030, with publi…
AI's rapid expansion is hitting economic turbulence as infrastructure costs balloon and profits flatline, with the Bank for International Settlements warning of parallels to past tech booms. Hyperscal…
Ornn, backed by Andreessen Horowitz, raised $33 million to build a marketplace for trading AI computing power as a commodity, aiming to bring price stability to the AI industry. Goldman Sachs estimate…
The U.S. government's June 12, 2026, restriction on Anthropic's Mythos models has eroded trust in closed-source AI, accelerating a shift toward open-weight models from China. Companies like Palantir a…
In 2026, AI development requires a clear understanding of loop and harness engineering, as confusion between these disciplines leads to production failures in AI agents. Loop engineering manages task …
Continual learning, which mimics human cognition by retaining past knowledge while learning new tasks, is gaining traction in AI. Its alignment with Bayesian inference offers a promising solution for …
Researchers have developed a method for human value detection in AI that incorporates Schwartz's circular motivational continuum, using a Schwartz-aware energy decoder to improve label coherence witho…
AutoSpec, a new neural network framework, reinvents spectral algorithms for large-scale tasks, achieving up to tenfold accuracy gains on real-world matrices. Developed by researchers, it adapts to inp…
Researchers have introduced normalized entropy and the STAPO framework to address trajectory neglect in reinforcement learning, where AI models lose track of goals during complex tasks. Tests on ALFWo…
TacReasoner, a dynamic tactile-language framework with 7 billion parameters, outperforms larger models like the 14B VTV-LLM on tactile commonsense reasoning tasks by integrating dynamic tactile percep…
Researchers have developed a hands-free VR locomotion method powered by large language models, allowing natural language navigation. A study comparing it to controller-based teleportation and voice st…
CompLLM, a new compression method for large language models, achieves linear scaling with context length by segmenting and independently compressing text chunks, slashing processing time and KV cache …
Researchers introduced the MRCL benchmark to test reinforcement learning's effectiveness in continual post-training for vision-language models, finding that RL alone does not prevent catastrophic forg…
Evolutionary Game Theory models, including the Hawk-Dove Game, Iterated Prisoner's Dilemma, and War of Attrition, offer insights into how humans and AI may co-evolve toward strategic equilibria. The a…
Researchers have developed a method using AI and musculoskeletal simulations to generate synthetic IMU data, addressing data scarcity in movement analysis and improving classification accuracy for phy…
STRMAC, a state-aware routing framework for multi-agent systems powered by large language models, achieves a 23.8% performance improvement on collaborative reasoning benchmarks and reduces data collec…
Unsupervised deep learning is transforming cardiac MRI reconstruction, with a new image-domain dual-branch INR framework (I-FP-INR) outperforming baseline methods. This approach eliminates the need fo…
TransitNet, a 1.5 MB deep-learning model, detects Earth-size planets with 95.2% accuracy in low-signal environments, outperforming traditional methods like TLS and BLS. It is 12-25 times faster than C…
Reinforcement learning agents using REINFORCE and PPO algorithms achieved a 3.5x improvement over Round-Robin and 48% over Shortest-Queue in multi-GPU routing optimization, boosting throughput by 60% …