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Brain-inspired randomness makes artificial vision more reliable

Humans are usually remarkably good at recognizing objects, even under poor conditions. Artificial vision systems, by contrast, reach their limits more quickly. Researchers at the Hertie Institute for Clinical Brain Research have now investigated what artificial vision systems can learn from the brain. They found that combining two brain-inspired features can make image recognition considerably more reliable. The research findings are published in the journal Patterns.

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AI expands mathematics: A new frontier of reverse problem generation for computational AI, Q&A

Artificial Intelligence (AI) is rapidly advancingβ€”and with it, the promise of solving some of academia's toughest problems. But while many researchers focus on using AI to find answers, Associate Professor Hiroshi Kera of the Institute for Advanced Academic Research/Graduate School of Informatics is asking a different question: How can we design better problems for AI to solve?

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No, AI systems don't have a drive to surviveβ€”developmental psychologist explains why it only looks that way

In controlled safety tests described earlier this year, researchers asked AI models to solve a series of simple math problems. Partway through the exercise, the instructors warned the bots that if they tried to solve the next problem, the computer environment they were operating in would be shut down. In some runs, the shutdown occurred as specified. In others, models interfered with the shutdown script and continued with the remaining problems.

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Cyber attacks? Bioterrorism? To 'Pace the Frontier' of AI effectively, we must improve our anticipatory thinking

Anthropic CEO and co-founder Dario Amodei has published an essay titled "We Must Pace the Frontier". He expressed ongoing concerns over the "misuse of AI for cyberattacks and bioterrorism" and fears that a swarm of AI agents could theoretically take over the entire internet within six to 12 months.

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