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Mathematicians say Google's AI tools are supercharging their research AI tools developed by Google DeepMind are surprisingly effective at assisting mathematical research and could usher in a wave of AI-powered mathematical discovery at a previously unseen scale, say mathematicians who have tested the technology. In May, Google announced an AI system called AlphaEvolve that could find new algorithms and mathematical formulae. The system works by exploring many possible solutions, produced by Google's AI chatbot Gemini. Crucially, though, these are fed to a separate AI evaluator that can filter out the nonsensical solutions that a chatbot inevitably generates . At the time, Google researchers tested AlphaEvolve on more than 50 open mathematical problems and found that, in three-quarters of cases, the system could rediscover the best-known solutions found by humans.

AI may blunt our thinking skills - here's what you can do about it There is growing evidence that our reliance on generative AI tools is reducing our ability to think clearly and critically, but it doesn't have to be that way Socrates wasn't the greatest fan of the written word. Famous for leaving no texts to posterity, the great philosopher is said to have believed that a reliance on writing destroys the memory and weakens the mind . Some 2400 years later, Socrates's fears seem misplaced - particularly in light of evidence that writing things down improves memory formation . A growing number of psychologists, neuroscientists and philosophers worry that ChatGPT and similar generative AI tools will chip away at our powers of information recall and blunt our capacity for clear reasoning. What's more, while Socrates relied on clever rhetoric to make his argument, these researchers are grounding theirs in empirical data.

Fox News Flash top headlines are here. Check out what's clicking on FoxNews.com. It's called a biometric gate check -- more commonly known as facial recognition technology. U.S. Customs and Border Protection (CBP) has used it to process more than 100 million travelers at airports in the U.S. But now a bipartisan pair of U.S. senators is asking how the data is being used and trying to determine if it's an invasion of privacy, similar to daily life in communist China.

Pain is an indispensable tool for survival. The prick of a nail underfoot is a warning that protects you from a deep, dirty wound--and maybe tetanus. The sizzle of a steel skillet is a deterrent against a third-degree burn. As much as it sucks, pain, oddly enough, keeps us from hurting ourselves. It's a luxury that prosthetic users don't have.

The Defense Department has plans to open a joint office focused on developing artificial intelligence prototypes. Michael Griffin, the newly appointed and first undersecretary of Defense for research and engineering, said Friday during a discussion at the Hudson Institute that his office is creating a so-called "Joint Artificial Intelligence Office" and similar offices for other emerging technologies, like hypersonics. Griffin's remarks came on the tail of Defense Secretary Jim Mattis' Senate testimony Thursday in which he described his aggressive agenda for Pentagon research and engineering. "We're going to move things into production, prototyping," Mattis said. "We're not going to have more papers, we're going to move on hypersonics, move on AI." He explained that means coordinated office across the department, "not a bunch of different organizations all feeling their way forward."

In almost any application of social media analysis, the user is interested in studying a particular topic or research question. Collecting posts or messages relevant to the topic from a social media source is a necessary step. Due to the huge size of social media sources (e.g., Twitter and Facebook), one has to use some topic keywords to search for possibly relevant posts. However, gathering a good set of keywords is a very tedious and time-consuming task. It often involves a lengthy iterative process of searching and manual reading. In this paper, we propose a novel technique to help the user identify topical search keywords. Our experiments are carried out on identifying such keywords for five (5) real-life application topics to be used for searching relevant tweets from the Twitter API. The results show that the proposed method is highly effective.