Generative causal testing turns opaque predictions of language-related brain responses into hypotheses tested with new stories and fMRI.
Nano Banana 2 Lite arrives as a stable, lower-cost image model; Gemini Omni Flash offers conversational video but remains in preview.
UK car marketplace Motorway and AWS have built a system to evaluate its search agent. Incorrect results fell from one in eight queries to one in 50.
The open-source project places a gateway between AI agents and external APIs so models never receive the real keys. It centralizes permissions, rotation and access logs.
The profiler reveals kernels, copies, and SDPA paths that source code does not show. The key is measuring your own case before changing backends.
Weblica combines HTTP replicas and synthetic sites to train agents that navigate from screenshots. Its benchmark gains are real, but the live web remains more complex.
Meta built a context layer to help agents understand a large internal pipeline. The case is promising, but it does not show that more documentation always helps.
A weight is a number an AI model adjusts during training to reduce its errors. Millions or billions of those numbers, combined, are the only thing a model "learns" — nothing else is stored inside.
We explain gradient descent, hyperparameters and overfitting, but almost never the thing that actually gets adjusted: the weight. It's a number, and a model is millions of those numbers. That single idea collapses a lot of AI mythology — and with the right sources, you can say precisely how much memorization is real and why "70B" doesn't measure quality.
'Open weights' is not 'open source,' and the difference has real consequences: from user thresholds to territorial exclusions buried in the fine print. Using Llama 4's actual license and the counterexample of OLMo 2, this piece teaches three questions to check for yourself, document in hand, whether an AI model billed as 'open' really is.
OpenAI’s AGI Deployment leader has said she is leaving her full-time executive role and moving to part-time advisory work.
On July 6, Anthropic published a real finding about how Claude reasons internally (we already covered it). What came next was unusual: it invited three groups of experts to comment, without editing them into agreement, and they don't agree. Here's what separates the three, why none of them confuses a finding with an interpretation, and how to apply that same discipline next time.
The Baltimore university will launch a B.S. in Artificial Intelligence this autumn, transforming its cloud-computing programme into a 120-credit degree.
The Robert Koch Institute will hold its third symposium on AI in public-health research in September, focusing on data, evaluation and governance.
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