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Mistaken correlations: Why it's critical to move beyond overly aggregated machine-learning metrics
MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data ...
The internet isn't uniform; algorithms constantly pivot based on local laws and cultural habits. In South Korea, for example, ...
We as an industry need to stop looking for "AI SMEs" and start looking for "mission strategists with AI literacy." ...
The structure of X's "For You" algorithm can now be traced on GitHub. However, how exactly the algorithm evaluates which ...
While standard models suffer from context rot as data grows, MIT’s new Recursive Language Model (RLM) framework treats ...
Algorithms, however, carry no conscience of their own. They simply execute what they are taught. They reflect the priorities, fears, and ambitions of their creators. When leadership forgets this, ...
It’s become something of a New Year’s tradition for Netflix to release a new Harlan Coben show in January. This year’s ...
AI/ML are driving a steep ramp in neural processing unit (NPU) design activity for everything from data centers to edge ...
Existing algorithms can partially reconstruct the shape of a single tree from a clean point-cloud dataset acquired by ...
This Claude Code approach from its creator shares eight steps. Make Claude Code projects smoother while improving speed and code quality ...
Laboratories turned to a smart workaround when COVID‑19 testing kits became scarce in 2020. They mixed samples from several ...
To keep AI coding assistants from running amok, developers must learn to write good specs and develop product management ...
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