Intuition is (much) Faster than Intelligence

Artificial Intuition is faster than Artificial Intelligence. Just like human intuition provides you with an answer almost instantaneously, without thinking, our QCM-powered Artificial Intuition outperforms Artificial Intelligence when it comes to speed. But why is this so? Unlike Artificial Intelligence, Artificial Intuition doesn’t require Machine Learning, which needs thousands of examples from which to learn. And this makes it very slow and expensive. From the moment one starts to collect examples in order to build a good training set, to the moment a solution (essentially a guess) is provided, much time can pass. This is particularly the case of anomaly detection. Even more so in the case of very rare anomalies that never reappear in the same guise, preventing any form of learning.

Artificial Intuition, on the other hand, doesn’t need examples. It processes data on the fly and looks out for any sudden complexity increments. In life, when complexity increases suddenly, our gut tells us that something is wrong. In fact, humans instinctively stay away from highly complex situations as they are difficult to comprehend and to control. In effect, Artificial Intuition works like ‘Computational Gut Feeling’. It is instinctive, which is what makes is super fast.

Clearly, it must be said that Artificial Intuition cannot solve all problems that (generative) Artificial Intelligence can. It cannot write songs, create images of inexistent landscapes or conjure up false court cases.

One might think that Artificial Intelligence and Artificial Intuition belong to the same category of tools. Nothing could be farther from the truth. In fact, the two are, mathematically speaking, orthogonal to each other. While Artificial Intelligence functions based on analogy, Artificial Intuition has its roots in physics and information theory. It is a completely different animal.

PS. The term “Machine Learning” is misleading. A more appropriate term is “Machine Mimicking”. Learning is a process which ultimately leads to understanding. If you don’t understand it, you didn’t learn. If one is able to distinguish a cat from an elephant, simply on the basis of having seen thousands of pictures of each, doesn’t mean that one understands what an animal is, what is its biology, where it lives, etc., etc. This is why “Machine Learning” doesn’t answer a question – it can only guess and answer. And if you guess, you can sometimes get it wrong.

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