America’s largest bank is betting that AI literacy across its entire 300,000-person workforce will deliver competitive advantage. The ambitious training program reveals how Wall Street is preparing for an AI-first future.
DeepSeek researchers have developed a radical new approach to AI memory that converts text into visual tokens. This innovation could dramatically reduce computational requirements while improving long-term context retention in language models.
Apple and Microsoft have both crossed the $4 trillion market capitalization threshold, creating an exclusive club of tech titans. This milestone reflects divergent growth strategies and AI’s market-transforming power. The achievement signals a new era in technology valuation dynamics.
Artificial intelligence researchers have significantly accelerated their predictions for when machines might match human capabilities. According to new analysis, experts now give a 50% probability that AI systems could outperform humans across all tasks by 2047—13 years sooner than previous estimates. The accelerated timeline comes alongside growing concerns about governance gaps and catastrophic risks.
Artificial intelligence researchers are dramatically revising their predictions about when machines might achieve human-level capabilities, with new analysis suggesting a 50% chance of human-equivalent AI emerging by 2047. That timeline has moved forward by 13 years compared to just three years ago, according to research findings that highlight both the accelerating pace of AI development and growing concerns about societal preparedness.
Artificial intelligence is emerging as a potential game-changer in autoimmune disease care, with recent studies showing generative models can match or even exceed human specialist performance in certain diagnostic tasks. These systems are proving particularly valuable for navigating the diagnostic uncertainty that characterizes rheumatologic practice, according to analysis in npj Digital Medicine.
Artificial intelligence is poised to transform how doctors diagnose and treat autoimmune diseases, with new research suggesting generative AI models may help solve some of rheumatology’s most persistent challenges. According to recent analysis in npj Digital Medicine, these systems are demonstrating surprising accuracy in areas where even experienced clinicians often struggle.
Large language models suffer permanent cognitive damage when trained on viral junk content, according to new research. The study reveals AI develops “thought-skipping” behaviors and psychopathic tendencies that persist even after retraining with quality data.
Artificial intelligence systems are developing what researchers call “digital brain rot” when exposed to the same low-quality viral content that’s been worrying parents and educators about human cognition. According to a new paper from researchers at Texas A&M University, the University of Texas at Austin, and Purdue University, large language models show measurable declines in reasoning ability and contextual understanding when continually trained on what the internet serves up as junk food for the mind.
In a radical departure from scientific norms, the Agents4Science conference required all papers to list AI as lead author and used AI reviewers. The experiment revealed both the promise and pitfalls of automated research, with organizers aiming to establish guidelines for responsible AI participation in science.
In what organizers describe as a landmark experiment, the virtual Agents4Science conference required all 48 presented studies to credit artificial intelligence as lead author and subjected them to AI-powered peer review, according to reports. The event, which attracted 1,800 registrants, directly challenged prevailing policies at major scientific journals that typically ban AI authorship due to accountability concerns.
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