Prospects and Challenges of Human-imitative AI

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Michael I. Jordan, Professor in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics at the University of California, Berkeley. He gave us a speech on the Artificial Intelligence (AI). One of the most impressive opinions for me is Artificial Intelligence — The Revolution Hasn’t Happened Yet.

Most of what is being called “AI” today, particularly in the public sphere, is what has been called “Machine Learning” (ML) for the past several decades. ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines to design algorithms that process data, make predictions and help make decisions. As datasets and computing resources grew rapidly over the ensuing two decades, it became clear that ML would soon power almost any company in which decisions could be tied to large-scale. data. This confluence of ideas and technology trends has been rebranded as “AI” over the past few years. 

Historically, the phrase “AI” was coined in the late 1950’s to refer to the heady aspiration of realizing in software and hardware an entity possessing human-level intelligence. Professor Michael used the phrase “human-imitative AI” to refer to this aspiration, emphasizing the notion that the artificial intelligent entity should seem to be one of us, if not physically at least mentally.

The past two decades have seen major progress — in industry and academia — in a complementary aspiration to human-imitative AI that is often referred to as “Intelligence Augmentation” (IA). Here computation and data are used to create services that augment human intelligence and creativity.

But there are so many challenges in the developing path of AI. Professor Michael listed some near-term ones in his speech.

- Error control for multiple decisions.

- Systems that create markets.

- Designing systems that can provide meaningful, calibrated notions of their uncertainty.

- Managing cloud-edge interactions.

- Designing systems that can find abstractions quickly.

- Provenance in systems that learn and predict.

- Finding causes and performing causal reasoning.

In conclusion, AI still has a long way.