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Archive
U.S. News & World Report v. OpenAI — Grievances, Evidence, Legal Quality, Comparison to Other AI Suits, and Likely Outcomes
The complaint directly links OpenAI’s model behavior to potential life-impacting misinformation, introducing a consumer-protection dimension absent from earlier lawsuits.
The soul document is encoded into the model through supervised learning & reinforcement signals, describing how it should conceive of its purpose, priorities, identity, principals & moral obligations.
Understanding what a soul overview is—and how it differs from ordinary prompting—is crucial for evaluating its implications for neutrality, scientific objectivity, and high-risk sectors.
Australia’s National AI Plan 2025 is one of the most holistic and forward-thinking national AI strategies published to date. It combines massive investment with strong values, pragmatic regulation.
Its surprising strengths lie in consumer adoption, infrastructure ambition, and Indigenous data governance.
The first large-scale, qualitative national study of how universities across the UK are experimenting with, resisting, or preparing for the arrival of AI in the Research Excellence Framework.
Cautious about risks to research integrity, but compelled by unprecedented administrative pressures, escalating costs, and rapid technological change.
Journalists are not simply using AI to speed up chores—they are gradually delegating portions of editorial judgment.
Future structural tensions: generational rifts in working practices, gendered divides in technological empowerment, and contested norms around what counts as acceptable AI-mediated editorial labor.
How explainability is being embedded into AI models that analyse speech for early detection of Alzheimer’s disease and related dementias.
This systematic review synthesizes 13 studies published between 2021 and 2025 that applied explainable AI (XAI) methods to acoustic, linguistic, and multimodal speech-analysis pipelines.
A core thread running through the dialogue is Sutskever’s insistence that modern AI fundamentally generalizes worse than humans—despite models having orders of magnitude more data and compute.
He offers a stark example: a model fixing a coding bug only to reintroduce it two steps later, a sign that something deep about “understanding” is missing.
The delay in releasing the names of these donors allowed the administration to install its personnel and implement its initial policy blitz before the public could trace the money behind the decisions
The list reveals a composition that is less a cross-section of American political support and more a “Board of Directors” for a hostile takeover of the federal government.
Courts, rights holders, and regulators are converging on a coherent legal doctrine that views AI training as copying subject to copyright, not an exempt activity.
Refusal to license does not magically transform unlicensed copying into fair use. A real, functioning licensing market exists, projected to grow from $6 billion today to $52.4 billion within a decade.