AI works out a way to create more AI

Stefan Mileschin · 27th January 2022, 12:03

What could go possibly wrong?

Ontario’s University of Guelph boffins, who have clearly never read a sci-fi book in their lives, have emerged from their smoke-filled labs claiming to have come close to building an AI which can build more AI.

By using hyper networks, the boffins can now pre-emptively fine-tune artificial neural networks, saving some of the time and expense of training.

Artificial intelligence is a numbers game. When deep neural networks, a form of AI that learns to discern patterns in data, began surpassing traditional algorithms 10 years ago, it was because we finally had enough data and processing power to make full use of them.

Training AI requires carefully tuning the values of millions or even billions of parameters that characterise these networks, representing the strengths of the connections between artificial neurons. The goal is to find ideal values for them, a process known as optimisation, but training the networks to reach this point isn't easy.

https://fudzilla.com/news/54275-ai-w...create-more-ai

27th January 2022, 12:03	#1
Stefan Mileschin [M] Reviewer Join Date: May 2010 Location: Romania Posts: 148,812	AI works out a way to create more AI What could go possibly wrong? Ontario’s University of Guelph boffins, who have clearly never read a sci-fi book in their lives, have emerged from their smoke-filled labs claiming to have come close to building an AI which can build more AI. By using hyper networks, the boffins can now pre-emptively fine-tune artificial neural networks, saving some of the time and expense of training. Artificial intelligence is a numbers game. When deep neural networks, a form of AI that learns to discern patterns in data, began surpassing traditional algorithms 10 years ago, it was because we finally had enough data and processing power to make full use of them. Training AI requires carefully tuning the values of millions or even billions of parameters that characterise these networks, representing the strengths of the connections between artificial neurons. The goal is to find ideal values for them, a process known as optimisation, but training the networks to reach this point isn't easy. https://fudzilla.com/news/54275-ai-w...create-more-ai

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