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Industry expectations that AI will soon largely improve itself are widespread, citing LLMs' abilities to write code, create synthetic training data, and optimize hardware. MIT Technology Review reports that despite these capabilities, forecasts of rapid recursive self-improvement may be premature.
TechCrunch AI reports that Amazon is destroying rare physical books to obtain text for training large language models. The article says rare books are valuable because models have already absorbed much publicly available online text.
MIT Technology Review reports that a new wave of startups is pursuing innovations beyond the transformer architecture that has powered large language models for nine years. The piece also notes shifts in AI academic research priorities, suggesting a changing landscape for model design and study.
MIT Technology Review profiles startups aiming to advance large language model design and applications, tracing the field back to the 2017 ‘Attention Is All You Need’ paper. The piece previews trends and companies that could shape future LLM research and products.
A team of researchers argues in a paper presented at the International Conference on Machine Learning that it is impossible to make large language models fully secure against hacks due to a fundamental flaw in how they work. The claim, reported by MIT Technology Review, highlights broad safety implications for LLM deployment.
New research reported by MIT Technology Review says large language models not only absorb human biases from training data but can also develop their own biases when used for hiring. The findings raise questions about whether AI resume screening will judge applicants fairly.
Anthropic researchers built a diagnostic called the Jacobian lens that gives a clearer view of what Claude attends to when reasoning. Their probe revealed structured internal regions used for concept-like processing, with findings ranging from expected to unsettling.
