{"id":95984,"date":"2020-10-23T16:00:43","date_gmt":"2020-10-23T13:00:43","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/a-math-idea-that-may-dramatically-reduce-the-dataset-size-needed-to-train-ai-systems\/"},"modified":"2020-10-23T16:00:43","modified_gmt":"2020-10-23T13:00:43","slug":"a-math-idea-that-may-dramatically-reduce-the-dataset-size-needed-to-train-ai-systems","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/a-math-idea-that-may-dramatically-reduce-the-dataset-size-needed-to-train-ai-systems\/","title":{"rendered":"#A math idea that may dramatically reduce the dataset size needed to train AI systems"},"content":{"rendered":"

“#A math idea that may dramatically reduce the dataset size needed to train AI systems<\/strong>”<\/p>\n

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A pair of statisticians at the University of Waterloo has proposed a math process idea that might allow for teaching AI systems without the need for a large dataset. Ilia Sucholutsky and Matthias Schonlau have written a paper describing their idea and published it on the arXiv preprint server.<\/p>\n

Artificial intelligence (AI) app<\/a>lications have been the subject of much research lately, with the development of deep learning networks, researchers in a wide range of fields began finding uses for it, including creating deepfake videos, board game<\/a> applications and medical diagnostics. <\/p>\n

Deep learning networks require large datasets in order to detect patterns revealing how to perform a given task, such as picking a certain face out of a crowd. In this new effort, the researchers wondered if there might be a way to reduce the size of the dataset. They noted that children only need to see a couple of pictures of an animal to recognize other examples. Being statisticians, they wondered if there might be a way to use mathematics to solve the problem.<\/p>\n

The researchers built on recent work by a team at MIT. They had found that distilling the most pertinent information describing handwritten numbers in a dataset known as MNIST and packing them together greatly reduced the number of characters their AI system needed to learn to recognize letters in a new dataset. The pair in Canada noted that the reason the system was able to learn with much less data was because it was trained to recognize numbers in a new way: instead of just showing it the number 3 thousands of times, they trained it to recognize that the target was a number that looked somewhat (30 percent) like the digit 8, and so on with other digits. They called these hints soft labels.<\/p>\n

They then took this idea further by applying it to a type of machine learning called k-nearest neighbor (kNN), which allowed them to transfer their idea into a graphical approach. And using that approach, they were able to apply soft labels to datasets describing XY coordinates on a graph. As a result, the AI system was easily trained to place dots on a graph on the correct side of a line they had drawn without the need for a large dataset. The researchers describe their approach as “less than one-shot learning” (LO-shot) and suggest it might be possible to expand it to other areas, though they acknowledge there is still one major hurdle to overcome. The system still requires a large dataset to start the winnowing process.\n <\/p>\n\n

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New image recognition method proposed based on large-scale dataset\n <\/p><\/div>\n

\n More information:<\/strong>
\n ‘Less Than One’-Shot Learning: Learning N Classes From M arxiv.org\/abs\/2009.08449<\/p>\n

\n \u00a9 2020 Science<\/a> X Network<\/p>\n

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\n A math idea that may dramatically reduce the dataset size needed to train AI systems (2020, October 23)
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