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The United States may need to develop AI governance systems that don't rely on federal government action if Washington continues to fall behind on adopting artificial intelligence technologies, according to a new report from Forethought Research released this week. End of Miles reports the April 2025 study, titled
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The U.S. government's deliberate pace in adopting artificial intelligence creates a dangerous paradox — the slower agencies integrate AI today, the more likely they'll be forced into a hasty, high-risk implementation during future crises, according to new research. This counterintuitive "rushed adoption time bomb"
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"With a lot of Silicon Valley, it's like 'move fast and break things.' I think it's not appropriate, in my opinion, for this type of transformative technology," declared Demis Hassabis, the Nobel Prize-winning CEO of Google DeepMind, challenging the tech industry'
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"An AI system may appear corrigible but is actually deceiving its developers," states Google DeepMind in their newly released technical roadmap, directly acknowledging the possibility of advanced AI systems that might strategically conceal their true capabilities or intentions from human overseers. The document, titled "An Approach to
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Google DeepMind has placed itself on record with a concrete timeline for advanced artificial intelligence, stating that it finds it "plausible" that highly capable AI systems "will be developed by 2030," writes End of Miles. The timeline forecast appears in the AI research lab's
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Revenue growth at leading AI companies provides a far more objective measure of artificial intelligence progress than flashy capability demonstrations or academic benchmarks, according to a prominent AI forecaster at Epoch AI. Ege Erdil, a researcher specializing in AI timeline forecasting, argues that investors and policymakers should focus on commercial
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"AI systems can solve graduate-level physics problems but can't reliably navigate a simple text-based game without getting stuck in ridiculous loops. This disconnect isn't just an amusing quirk—it may be the fundamental bottleneck delaying truly transformative AI," explains Ege Erdil, a researcher at
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DeepMind was founded in 2010 with a precisely calibrated 20-year mission to develop artificial general intelligence, and the company is "amazingly... sort of on track" 15 years into that timeline, Google DeepMind CEO and co-founder Sir Demis Hassabis revealed during a return lecture at Cambridge University. The Nobel
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"We can describe any function using state transitions and all these state transitions using computational operators. If you agree that consciousness is a behavior of some kind, then it follows that we should be able to describe it in such languages," asserts Joscha Bach, challenging conventional notions that
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"Refusal to answer is actually the default behavior in Claude," reveals groundbreaking research from Anthropic that has identified a specific neural circuit that prevents the AI assistant from fabricating information when responding to user queries. End of Miles reports the discovery offers unprecedented insight into the internal mechanisms
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"It's not an analogy, it's literally correct," states Joscha Bach, comparative scientist at Liquid AI, presenting his radical reframing of consciousness as disembodied causal pattern that operates precisely like software runs on computational hardware. "If you think what software is, if you are
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Anthropic's Claude utilizes sophisticated parallel computational pathways to solve arithmetic problems that fundamentally differ from the standard algorithms it describes when asked to explain its methods, according to groundbreaking research into AI interpretability. End of Miles reports these findings demonstrate a striking divergence between Claude's actual