AIS is getting better math trouble
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Experimental AI models from Google Defermind and Openiai have achieved a display of gold level at the International Mathematical Olympiad (yours) for the first time.
Companies that hate the opportunity to be important for AIS to be a day to solve scientific problems or maths, but maths are more careful because of the results of the models and how they make the results of models.
Yours, one of the most enthusiastic competition in the world for young mathematicians, a long-looking at AI researchers as a litmus test for mathematical systems that AI systems are more likely to fight.
After the previous year’s competition stopped, UK, Google Deepermainnantned that AI systems developed, called Alphaproof and Alphageometry, equal A performance of silver medal is achieved, But its entries are not taken by the official competition marker.
Before competition this year, held in Queensland, Australia, companies including Google, Huawei and Telto-owners of adaricters that their AI models could have officially graded, said Gregor Dolinarthe president of you. Yours agreed, with the proviso waiting for companies to inform their results until July 28, when completing the complete end of the ceremonies to you.
Opukii also asked if it could have been involved in competition, but after it was announced about the official way, it did not respond or register an entry, saying Dolinar.
In July 19, Opuia is notified That a new AI developed achieved a mark of golden socks marked with three former medals distinct from official competition. AI answers five of six questions correctly in the same 4.5-hour time as participants, says Opuii.
Two days later, Google Defermind also announced that AI systemwhich is called Gemini deep thought, reached gold with the same score and time limits. Dolinar confirmed that this result was given to the official markers to you.
Unlike Google and Alpapeometry systems and alpapeometry systems, made for competition and working with questions written by a computer called Lean, Google and Opuii models working in natural language.
Repair work means that AI output may be checked immediately for correction, but harder for non-expert books. Luong Luong In Google, working in deep thinking of Gemini, says the natural language method can provide more understanding answers, as well as available to average AI systems.
Luong says the ability to verify solutions in a large language model has been made possible thanks to progress with reinforcement learning, a training method in which an ai is taught what success looks like the rules and how to succeed solely through trial and error. This method is the key to the previous Google success to play the game game, like Alphazero.
Google’s model also thinks of many solutions once, in a mode called uniform thinking, as well as trained in an ima, so much.
OpenI has released some details of its system, except for use of constraints in strengthening and “experimental strategies”.
“Progress promises, but is not made in a controlled scientific method, and so I cannot assess this at this stage,” as Terence Tao At the University of California, Los Angeles. “Perhaps once companies involve releasing some papers with large amounts of data, and expected to have something more reliable companies in their own results.”
Geordie Williamson To approval of the University of Sydney in Australia. “I think it’s odd it’s where we are. We’re disappointing what is the little detail of the outsiders of the internals,” says Williamson.
While systems that work in natural language can be useful for non-maths, it can also have a problem if models make high proofs that are difficult to check, as Myers in JosephOne of those who organize you this year. “If AISs often produce solutions to significant problems can be properly but also have an unpleasant evidence of a long evidence of a high-output of a long AI output before attempting to read it.”
Both companies say that, in the coming months, they offer these systems for the test of mathematicians before, before they are released in a broader public. Models can easily help the hardest scientific research problems, as Junehyuk jung To google, working in the deepest mind. “There are many, many unsolved problems to reach,” he said.
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