Community Research Seminar on AI and Machine Learning Across Borders

Monday 21 December 2020, 15:00–17:00 GMT

Co-facilitated by: Dr Ekaterina Babintseva (Harvey Mudd College), Aaron Mendon-Plasek (Columbia University)

Summary: This session examines the development of expert systems and machine learning research across different national contexts. Speakers' abstracts are as follows:

Dr Ekaterina Babintseva (Harvey Mudd College)
Creativity and Expert Thinking: Soviet Take on a Formal Description of Problem-Solving

In the late 1960s, Soviet psychologist Lev Landa developed what he called a universal model of all possible problems and methods of their solution. Named the Algo-Heuristic Theory (AHT), the model synthesized American and Soviet approaches to the formalization of problem-solving. Landa saw the AHT's special merit in the fact that it offered an account of the mental operations that are responsible for creative thinking, a task which, as Landa explained, Simon and Newell's Logic Theory Machine failed to accomplish. As Landa immigrated to the United States in 1973, the AHT became the core of his methodology for the training of American managers and bureaucrats. In the 1980s, the AHT also became instrumental in the development of expert systems, one of the approaches to AI, which was especially in vogue in the 1970s–1980s. Paying special attention to the socio-economic and institutional contexts within which Landa developed his theory, this presentation will analyze the definitions of creativity and intelligence at the heart of the Algo-Heuristic Theory.

Aaron Mendon-Plasek (Columbia University)
Mechanized Significance and Machine Learning: Why It Became Thinkable and Preferable to Teach Machines to Judge the World

The slow and uneven forging of a novel constellation of practices, concerns, and values that became machine learning occurred in 1950s and 1960s pattern recognition research through attempts to mechanize contextual significance that involved building 'learning machines' that imitated human judgment by learning from examples. By the 1960s two crises emerged: the first was an inability to evaluate, compare, and judge different pattern recognition systems; the second was an inability to articulate what made pattern recognition constitute a distinct discipline. The resolution of both crises through the problem-framing strategies of supervised and unsupervised learning and the incorporation of statistical decision theory changed what it meant to provide an adequate description of the world even as it caused researchers to reimagine their own scientific self-identities.