Traditional Math cans simplify AI training: Finnish research

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A new study by Finnish researchers has simplified the process of teaching computers to perform artificial intelligence (AI) tasks using traditional mathematics, reported Xinhua, quoting a press release.

The study conducted by researchers at the University of Jyväskylä has shown that AI does not necessarily require deep learning, which involves establishing and simulating neural networks based on the mechanisms of the human brain.

The researchers showed that traditional mathematical optimization methods yield better results, the university stated in a press release published on Monday.

Professor Tommi Kärkkäinen and Ph.D. researcher Jan Hänninen have used traditional mathematics to simplify the AI learning model.

Deep learning is particularly useful for teaching computers to tackle complex tasks such as generating new content, controlling cars and robots, or playing intricate strategy games. However, deep learning models are complex and difficult to comprehend, the researchers said.

"Our new neural network model is more expressive and can effectively summarize large datasets," said Kärkkäinen.

Meanwhile, Hänninen said, "Based on our results, applying neural networks to various tasks will become easier and more reliable."

A simpler network structure allows for easier implementation and better understanding. Since artificial intelligence is now part of almost all modern technology, it is crucial to comprehend its operations and functions.

Kärkkäinen and Hänninen's article was recently published in the prestigious series Neurocomputing.

"We are eager to see how the results will be received within the scientific community and among users of machine learning methods in the industry," Kärkkäinen said.

  •  Math
  •  AI
  •  Training


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