The pursuit of a human face: Can interfaces make machine learning accessible?

The adoption of artificial intelligence (AI) and machine learning (ML) in various business processes is on the rise. This is evidenced, for example, by the steadily increasing media coverage, which indicates an increasing relevance of the technology. A growing number of application practices confirm the fact: the ICT.Moscow database, in 2021 alone, collected more than a hundred of them.

However, the use of AI in business processes is accompanied by a rather significant stop factor: in order to effectively use ML algorithms to solve problems, you need to be a specialist in ML and AI. This problem can be solved in various ways. For example, Cornell University in the United States is developing a platform with a “transfer learning” approach that allows people without special skills to use ML algorithms. Data scientist from KPMG Germany Philip Vollet, in turn, talks about a new noticeable trend, the development of machine learning graphical user interface (MLGUI).

Is the field of AI on the verge of a tipping point, when, thanks to such specialized interfaces, ML will in fact become a publicly available tool that does not require deep specialized knowledge? ICT.Moscow discussed this issue with Swati Mishra, the lead author of a scientific article on transfer learning from Cornell University, and with Russian ML-developers from Yandex, Sberbank, consulting company GlowByte, among others. The developers whose solutions are certified for use in the Russian healthcare system (where AI is in strong demand), in turn, talked about their specifics of using and working with ML interfaces.

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Topics: Analysis, Artificial intelligence, Biomedicine, Digital services & Apps, E-health, R&D
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