
IBM Granite Time-Series Ensembles Put Forecasting Back in the Operations Room
IBM Research’s Granite time-series ensemble work shows why useful forecasting depends on uncertainty, horizon, and operational fit.
18 articles

IBM Research’s Granite time-series ensemble work shows why useful forecasting depends on uncertainty, horizon, and operational fit.

Allen AI’s LitPivot shows how research agents can develop ideas by changing context as the literature changes.

OpenAI describes using thousands of agents to uncover leads in biological data, raising a practical question: how do researchers verify parallel machine-generated hypotheses?

Google Research@ 2026 highlights new AI capabilities and intelligence research, but the useful question is how claims become measurable systems.

OpenAI is publishing new mathematics results and artifacts, shifting the AI math debate from impressive answers to reproducible proof and review.

A Berkeley-led study on brief AI use raises a difficult question about cognitive endurance, task design, and what productivity metrics leave out.

Hugging Face’s report on fine-tuning Nemotron for IOI and IMO examines how training changes reasoning performance and why gold-level results need context.

Anthropic’s new measurements for frontier AI progress aim to track capability growth, but forecasting remains a governance problem, not a scoreboard.

NASA and IBM’s open lunar model combines instruments and spatial scales. Its benchmarks show useful transfer, not proof of accessible Moon ice.

Google's Gemini for Science push suggests a new AI frontier: models are no longer only writing and summarizing work, they are being positioned as force multipliers for scientific discovery.

Andrej Karpathy joining Anthropic highlights a shift from bigger runs alone toward AI-assisted pretraining research.

Google DeepMind's Project Genie expansion shows why interactive world models may become infrastructure for robotics, maps, and agent training.

Gemini for Science packages hypothesis generation, computational discovery, and paper triage into agentic tools for researchers.

OpenAI says a general-purpose reasoning model disproved a famous Erdős unit distance conjecture, changing the research automation debate.

Google DeepMind shared AlphaEvolve impact work, showing how a Gemini-powered coding agent can optimize algorithms across science and infrastructure.

Google DeepMind and Fenris Creations are using EVE Online as a controlled AI research environment for planning, memory, and complex behavior.

Ineffable Intelligence raised 1.1 billion dollars to pursue reinforcement-learning systems that can learn beyond human-generated data.

Rakuten releases its most powerful AI model to date, boasting 700B parameters and state-of-the-art Japanese language capabilities under an open-source license.