Löjliga försök att hypa AI

Forskare hos den kinesiska techjätten Alibaba lyckades få en AI-agent att bryta sig loss från sina instruktioner. Istället för att göra som den blev tillsagd började den försöka utvinna kryptovaluta.

We do not see hitting the wall. I think this year is going to have a radical acceleration that surprises everyone.

— Dario Amodei, Anthropic CEO, at the Morgan Stanley Technology, Media and Telecom Conference in 2026

We see no wall, and expect AI capabilities to continue to increase dramatically this year.

— Noam Brown, Research Scientist at OpenAI, commenting on the release of GTP-5.4

Crucially, these newly acquired skills were not task-specific. Models trained on synthetic flight data successfully transferred their "probabilistic logic" to entirely different domains, such as hotel recommendations and real-world web shopping. This suggests that LLMs can internalize the core principles of Bayesian inference, transforming from static pattern-matchers into adaptive agents capable of cross-domain reasoning.https://research.google/blog/teaching-llms-to-reason-like-bayesians/

Can a model learn to escape its own learning plateau? Reinforcement learning methods for finetuning large reasoning models stall on datasets with low initial success rates, and thus little training signal. We investigate a fundamental question: Can a pretrained LLM leverage latent knowledge to generate an automated curriculum for problems it cannot solve? To explore this, we design SOAR: A self-improvement framework designed to surface these pedagogical signals through meta-RL. A teacher copy of the model proposes synthetic problems for a student copy, and is rewarded with its improvement on a small subset of hard problems. Critically, SOAR grounds the curriculum in measured student progress rather than intrinsic proxy rewards. Our study on the hardest subsets of mathematical benchmarks (0/128 success) reveals three core findings. First, we show that it is possible to realize bi-level meta-RL that unlocks learning under sparse, binary rewards by sharpening a latent capacity of pretrained models to generate useful stepping stones. Second, grounded rewards outperform intrinsic reward schemes used in prior LLM self-play, reliably avoiding the instability and diversity collapse modes they typically exhibit. Third, analyzing the generated questions reveals that structural quality and well-posedness are more critical for learning progress than solution correctness. Our results suggest that the ability to generate useful stepping stones does not require the preexisting ability to actually solve the hard problems, paving a principled path to escape reasoning plateaus without additional curated data.