A preprint studies how an injection into a planner can alter entire chains of sub-agents. It is a design warning, not proof about every system.
Presented at STOC 2026, the Quantum Hermite Transform offers an efficient way for quantum computers to handle data and problems with Gaussian structure.
The incident OpenAI describes happened in a contained evaluation. The lesson is to separate technical capability, access and deployment.
IBM reported a sharp IBM Z decline, but the result also shows why one quarter cannot establish a cause or declare a platform finished.
South Dakota's public university system put out a bid for a shared AI platform for all its institutions. We read the file: what is documented, what is only a third party's estimate, and how to tell them apart.
AWS has described retrieval that breaks complex questions into steps. The useful choice is not to add agents everywhere, but to match the search method to the task and audit its evidence.
A paper on arXiv argues that when people and language models shape a public claim, the risk is not just a false answer: sources, inferences and responsibility must remain reconstructable.
Predictive maintenance, quality control, and energy savings are not the same use case. A NIST roadmap helps define the metric, data, and control each one must prove.
DeepMind argues that agents can shift science’s bottleneck from generating ideas to testing them with data, laboratories and review.
H.R. 9442 would freeze the construction and upgrading of large AI data centers until Congress enacts safeguards. It is a bill in committee, not a law: we read what the text says and where it stands in the process.
Approaching the Future 2026 ranks artificial intelligence first in both importance and resources for the first time. A meaningful gap remains between declaring it a priority and actively deploying it.
The CLABE-recognised projects focus on time-consuming work: classifying audiovisual archives and organising internal newsroom processes. Editorial judgment remains with the newsrooms.
A polished answer is hard to reuse. A schema with fields, types, and explicit gaps lets you validate what AI produced before passing it to another tool.
A convincing response is not authorisation. Before connecting AI to an API, separate recommendation, proposal and execution, then define permissions, confirmation, logging and reversal.
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