MIT Technology Review's AI Hype Index highlights unsexy AI developments including Grok's porn-pilled translation feature, Meta's creepy glasses, and Big Tech's skyrocketing emissions, while noting tha…
At EmTech AI 2026, OpenAI's Head of Engineering for ChatGPT, Sulman Choudhry, discussed how his work is transforming daily life. The event highlighted the rise of AI platforms and their growing impact…
MIT alumni and supporters are rallying to defend the institution's mission of advancing scientific research, merit-based admissions, and national prosperity amid perceived threats to academic freedom …
Subquadratic, a Miami AI startup, claims to have broken through the transformer attention bottleneck that limits large language models, introducing Subquadratic Sparse Attention (SSA) to enable effici…
Miami startup Subquadratic claims to have solved a decade-old bottleneck in AI models by replacing dense attention with sparse attention, achieving 56x speed gains and dramatic cost reductions. Indepe…
AI startup Subquadratic claims to have solved a mathematical bottleneck in large language models, enabling faster and cheaper computation, though experts remain skeptical. Meanwhile, brain-computer in…
Subquadratic founders Justin Dangel and Alex Whedon released third-party benchmarks from Appen to support their claim that SubQ, a sparse-attention LLM, outperforms dense transformers on long-context …
Miami-based AI startup Subquadratic claims to have solved a mathematical bottleneck limiting large language models, introducing SubQ, a faster, cheaper, and more energy-efficient model that processes …
Subquadratic launched SubQ, a sub-quadratic sparse-attention LLM with a 12-million-token context window, enabling multi-million token reasoning at linear cost. The model reduces attention compute by n…
Subquadratic released the model card and technical report for SubQ 1.1 Small, a long-context model using Subquadratic Sparse Attention, on June 16. The company claims the model achieves 100% retrieval…
Subquadratic released SubQ 1.1 Small, a sparse attention model that achieves near-perfect long-context retrieval up to 12 million tokens with up to 1,000x compute reduction. The model balances long-co…