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Friday, April 20, 2018

Making a Thinking Machine

Leah Winerman
The Monitor on Psychology - April 2018

Here is an excerpt:

A 'Top Down' Approach

Now, psychologists and AI researchers are looking to insights from cognitive and developmental psychology to address these limitations and to capture aspects of human thinking that deep neural networks can’t yet simulate, such as curiosity and creativity.

This more “top-down” approach to AI relies less on identifying patterns in data, and instead on figuring out mathematical ways to describe the rules that govern human cognition. Researchers can then write those rules into the learning algorithms that power the AI system. One promising avenue for this method is called Bayesian modeling, which uses probability to model how people reason and learn about the world. Brenden Lake, PhD, a psychologist and AI researcher at New York University, and his colleagues, for example, have developed a Bayesian AI system that can accomplish a form of one-shot learning. Humans, even children, are very good at this—a child only has to see a pineapple once or twice to understand what the fruit is, pick it out of a basket and maybe draw an example.

Likewise, adults can learn a new character in an unfamiliar language almost immediately.

The article is here.
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