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submitted by /u/Tiny-Independent273 [link] [comments]

submitted by /u/bllshrfv [link] [comments]

ChatGPT's explanation: "Metaphor: AI proliferation is like an ever-expanding mirror maze built in the heart of a forest. At first, humanity entered with curiosity, marveling at the reflections—amplified intelligence, accelerated progress, infinite potential. But as the maze grew, the reflections multiplied, distorting more than revealing. People wandered deeper, mistaking mirrored paths for real ones, losing their sense of direction, and forgetting they once lived outside the glass." submitted by /u/MetaKnowing [link] [comments]

Just listened to the recent TED interview with Sam Altman. Frankly, it was unsettling. The conversation focused more on the ethics surrounding AI than the technology itself — and Altman came across as a somewhat awkward figure, seemingly determined to push forward with AGI regardless of concerns about risk or the need for robust governance. He embodies the same kind of youthful naivety we’ve seen in past tech leaders — brimming with confidence, ready to reshape the world based on his own vision of right and wrong. But who decides his vision is the correct one? He didn’t seem particularly interested in what a small group of “elite” voices think — instead, he insists his AI will “ask the world” what it wants. Altman’s vision paints a future where AI becomes an omnipresent force for good, gui...

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submitted by /u/MetaKnowing [link] [comments]

submitted by /u/MetaKnowing [link] [comments]

submitted by /u/eternviking [link] [comments]

The article at https://www.techspot.com/news/106874-ai-accelerates-superbug-solution-completing-two-days-what.html highlights a Google AI CoScientist project featuring a multi-agent system that generates original hypotheses without any gradient-based training. It runs on base LLMs, Gemini 2.0, which engage in back-and-forth arguments. This shows how “test-time compute scaling” without RL can create genuinely creative ideas. System overview The system starts with base LLMs that are not trained through gradient descent. Instead, multiple agents collaborate, challenge, and refine each other’s ideas. The process hinges on hypothesis creation, critical feedback, and iterative refinement. Hypothesis Production and Feedback An agent first proposes a set of hypotheses. Another agent then critiques...

submitted by /u/MetaKnowing [link] [comments]