"Evolution of life is just one of many fields in which  possibility landscapes play a major role. But these landscapes are playing essential roles in thinking  and learning and are fundamental  to the emerging field of  artificial intelligence (AI). The goal of AI  is to create systems capable of thinking and solving complex tasks, whether it recognizes content of image, predict structure of proteins, write a poem or play chess. Many of these tasks, like vision or language understanding are trivial to humans but have proven extremely hard for computers. It turns out that finding the right way to solve a given task out of an almost infinite number of possible methods has proven to be  almost impossible. Attempting to tackle the task by simply analyzing the problem and writing the rules that define the solution have proven futile for even the simplest tasks. As it turns out, even for trivial tasks like walking or seeing our ability to perform the task does not translate to understanding how the task is done. To understand  why this problem is so hard, let's go back to the landscape of possibilities. Consider for example the landscape of all possible strategies for playing chess, this landscape will be made of all the chess strategies that can ever  be conceived. The strategy of every chess player that ever lived, as well as many others that were never tried are all scattered on this landscape. Once again the more similar the methods are the closer they will be, and the better a strategy is the higher its altitude will be on the terrain.  The landscape will have peaks and valleys, the peaks will  be the strategies of the  best chess player that could ever exist. The valleys will contain the worst chess strategies. This is an immense landscape for the number of ways to play chess is infinite, and all but a tiny fraction are terrible, hence it will have a vast area of low valleys and sparse scattered peaks.  Trying to guess the right solution to this infinite landscape is hopeless, like blindly dropping a pin from space and hoping it will fall on the top of mount Everest. We can of course find a chess grandmaster and ask them to write their strategy for us, but as it turns out none of of them can actually explain it.
 
And chess is a relatively simple game with only a handful of possible moves. Imagine now the landscapes of all the ways you can drive a car, diagnose disease, or write a poem.  The possibilities landscapes for each of these tasks are so large that for any practical purposes it's infinite, containing large amounts of low valleys and tiny peaks of true intelligence and skill. 
For this reason AI research seems for decades to lag hopelessly beyond basic human intelligence.  The main breakthrough, that transformed AI from a science fiction to the reality of ChatGPT and self-driving cars, is based on a method named gradient descent. The approach is simple: start with a random solution, test it, to see how it works (it will work badly), tweak each of the parameters of the solution by a tiny amount and see if it improves the solution if it does keep this change. Repeat again and again and after a large number of tiny improvements you will get a solution that can match and even exceed the best human expert (like in the case of chess). In other words, find a random point in the landscape of possibilities and  alway walk upward and you are bound to reach the top of the one of the highest peaks.

Once again this approach is both trivial and implausible. Trivial because small tweaking of existing solutions and keeping what works is an obvious way to improve anything. 


And yet it's easy to see why this should  never work. Try to improve anything by only small tweaks and you will end up with a slightly better system. But rarely if ever a completely new method. Similarly try to climb real mountains by always stepping  upward and you will end up a few steps away standing on a big rock  or bump with no way to go but down.


For that reason while gradient descent methods were known before first computers, only few believe it can work beyond  simple optimization tasks. The idea that a near human level reasoning can emerge from complete randomness using only tiny gradual changes each leading to tiny improvement would have been considered impossible up to a few years ago.
And yet it works not to a specific case like language, or vision, but to every field from playing chess or any other board game, to driving a car, predicting structure of proteins or diagnosing diseases, inventing new molecules and making images and videos. All learned simply by starting from one random solution and gradually climbing upward in tiny steps of improvement,  never getting stuck along the way. Once again the question  of what so many different tasks and in so many different domains will have possibilities landscapes that share these ever ascending trails that seem so rare in the world we live in, remain unanswered. "

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