GoKawiil
Tech News
Freelance writer Benjamin Skuse tested IEEE's peer-review defenses by submitting AI-generated scientific papers, in one case simply running an automated 'run experiment' and 'write paper' process with no edits before submission. According to IEEE features editor Eliza Strickland, the combination of human reviewers and research-integrity software currently managed to catch these machine-made submissions.
Hex has raised $100 million in new funding to support a from-scratch redesign of its product, used by more than 2,500 customers. The company says it rebuilt its architecture, data model and compute engine to be 'agent native,' moving away from bolting AI features onto its original 2019-era platform.
Fields Medal-winning mathematician Richard Borcherds has stopped assigning graded homework at UC Berkeley, saying AI tools make it impossible to distinguish student work from machine-generated answers. He now relies mainly on in-person, device-free exams, noting that online exam scores have risen sharply in ways he attributes to AI use rather than learning gains.
AI language models have advanced largely by training on ever-larger text datasets, with dataset sizes reportedly doubling roughly every six months since the early 2010s, according to Epoch AI. But researchers note there is no equivalent large public corpus of robot training data, unlike the estimated 300 trillion tokens of usable text available for language models. This gap has made sourcing and defining robot training data a central open problem for the field.
Christian Kästner, who teaches Machine Learning in Production at Carnegie Mellon, says he has overhauled most assessments in his course because AI agents can now complete take-home assignments convincingly. Instead of grading homework, he now focuses evaluation on TA interactions, exams, and video demonstrations, while keeping the course's core learning goals on engineering tradeoffs and teamwork largely unchanged. Students are permitted to use AI tools freely except during written and oral exams.
Anthropic researchers introduce a mathematical approach to mechanistic interpretability, aiming to reverse-engineer the internal computations of transformer language models. Their initial study focuses on small transformers with two layers or fewer that use only attention blocks, deliberately simpler than models like GPT-3, in order to identify basic patterns before tackling larger systems.