Deepak Khemani

The phrase artificial intelligence has become common in our current day discourse. Fuelled by successes in machine learning, and applications interacting with us in speech and natural language, many commentators have made a leap of faith that behind these successes is a thinking machine, and this has even stoked fears of machines overcoming humankind. In this two-part article, we look at how close we are to the original quest for creating “machines who think”. In the first part, we trace the evolution of mechanical computers and also the notion of the mind up to the era before digital computers appeared on the horizon.

In the first week of the year 2020, we got the news that AI now outperforms doctors in detecting breast cancer. This is in line with a continuous stream of news coming from the world of diagnosis and has lent credence to the sentiment that AI is poised to overcome humankind. However, some perceptive observers have commented that recent advances are largely due to the massive increase in both availability of data and computing power. Moreover, it is only a narrow task of classification that has led the news blitz. Classification can be thought of as a stimulus-response process. Human intelligence is much broader. In particular, humans often display a stimulus-deliberation-response cycle. There is much that goes on in the “thinking” phase that was the original aim of AI before the data and speed started dominating applications. The second of the two-part article on AI traces the evolution in the field since the Dartmouth conference, and takes stock of where we are on the road to thinking machines.

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