"True AI should understand the relationship between cause and effect," says Judea Pearl…

"True AI should understand the relationship between cause and effect," says Judea Pearl, a pioneer in the field of artificial intelligence.

Let's teach machines to understand the question "Why?". While in the 1980s we created programmes that primarily calculated probabilities (such as Bayesian networks), this is no longer sufficient. Current successes in AI are largely based on neural networks. These do the same thing as the previous generation of neural networks, but with truly vast amounts of data. "All the impressive successes of deep learning amount to mere curves," he noted recently.

Judea Pearl describes his vision as follows: The new AI should be capable of causal reasoning. For instance, instead of merely correlating fever with malaria, machines need the ability to reason. That is, fever is caused by malaria. According to Pearl's expectations, machines can provide human-level intelligence if they can understand causation. They can communicate with humans more effectively and even act as moral beings with the capacity for free will – even for evil.

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