AI saves scientists time — but that time does not go into depth
A new theoretical study argues research could get worse even if the model worked perfectly. The cause is not error but opportunity cost.
Papers, technical findings, lab output.
A new theoretical study argues research could get worse even if the model worked perfectly. The cause is not error but opportunity cost.
According to one analyst, February 6, 2026 may have been the last day humans consumed more tokens than agents. Agent usage has grown 14x since.
Robot bodies are improving while robot brains produce little value. The industry's own diagnosis comes down to one word: data.
Hinton predicted in 2016 that radiologists would be replaced within five years. The field is growing — but the job description is changing.
The Pew Research Center analyzed nearly half a million pages. Commercial domains carry AI text roughly ten times more often than school or government sites.
A Stanford study finds workers aged 22 to 25 in the most AI-exposed occupations are 19 percent behind their peers. Older workers appear unaffected.
A language model processes vastly more words than a child hears while mastering a mother tongue. The gap has a name but no explanation.
AlgorithmWatch tested four leading models. In at least one query in four, the ideological stance of the linked organization went undisclosed.
Nvidia research argues that on long-horizon tasks the decisive factor is not the model but the software layer wrapped around it.
Methods borrowed from psychological testing show that reducing AI safety benchmarks to a single number is misleading.
An agent is a system given a goal that then works step by step. This guide covers how an agent is assembled, what components it has, where it breaks, and how to measure its performance.
IBM Research scaled its agentic memory evaluation to eight models. The finding fits in one sentence: the right dose differs by model tier, with strong models wanting the full guideline set and saturated models showing no measurable gain.
AI-powered toys marketed as companions for neurodivergent children are spreading. MIT Technology Review examines what that bond actually is, through 10-year-old Xander, who has grown up with Moxie for six years.
Multiverse Computing proposes two changes for distillation, the most expensive step in shrinking large language models: caching the teacher's top-K logits once, and a new KL loss that never materialises the full vocabulary matrix.
IBM Research compared its agent memory system ALTK-Evolve against ACE on the same agent. Both refuse to compress the lessons learned; they part ways on how those lessons reach the model, and that is where the bill comes from.
Fatty liver disease affects roughly 30 percent of adults worldwide and progresses without symptoms. Specialists see AI reading existing blood tests and images as the most realistic route to early detection.
Attention, the mechanism that made transformers powerful, now drives up the cost of long-context and agent workloads. Three startups are attacking the problem with sparse attention, rolling summaries and liquid neural networks.
The conditions that produced AlphaFold are rare: a data bank assembled over 53 years of international cooperation and roughly $21 billion of experimental work. The acceleration of science, MIT Technology Review argues, will come from agents.
Dyna Robotics has released Dyna-2, pre-trained on more than a million hours of egocentric human video. The work tests whether ordinary human video can substitute for teleoperation data.
Timothy Gowers and Peter Sarnak credit large language models with serious mathematical skill but see hard limits on genuinely new ideas. The gap is not knowledge but the intuition to pick the right route.
A Bloomberg analysis finds Chinese citizens are more optimistic about AI than Americans, because it is seen there as a practical tool and disrupts a smaller share of the population.
A study involving Google researchers shows that training chatbots not to claim consciousness also changes their stance on animal rights, religion and life satisfaction.
Fields Medallist Timothy Gowers says almost all the famous mathematics problems solved by language models so far were solved with counterexamples rather than proofs.
A representative Epoch AI survey finds 20% of employed Americans hand at least one task previously done by a person to AI, usually accepting the output with little or no editing.