Nowadays computers have learned how to translate languages, drive cars or diagnose a disease. They outsmart profesionals at strategic games, recognize complicated patterns and provide weather.

In spite of the fact that they are already powerful, artificial intelligence has its borders.

When it comes to situations they haven’t come across before, machine-learning systems can be confused and will not execute their tasks properly.

Such thing exist because of one reason. AI systems do not understand causation. They see that some events have something in common with other ones, but can not grasp cause and effect. It is like if you knew that if the presence of sun made the hot day more possible, but you did not know the sun caused the rise of temperature.

Grasping cause and effect is huge part of what we know as common sense. Artificial Inteligence does not have it. There is a growing consent that AI progress will be grounded ic computers will not handle causation better. If machines would learn how to differ cause and effect, there would not be a need to teach them everything anew all the time. They could use its skills form one area and try to implement it to another one. What is more, the trust to the AI-powered machines would rise extremely if we knew that they trustworthy as they would not make silly mistakes.

We can not predict how long will it take for computers to get reasonable causal reasoning abilities. The first move will be to develop machine-learning tools that mix data with available scientific knowledge.“We have a lot of knowledge that resides in the human skull which is not utilized.”



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Grams Bartosz


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