An artificial intelligence has discovered alternative laws of physics


This exploratory work could one day lead to revolutionary advances in many disciplines. Modern science is largely based on the principle of iteration. We start from certain simple and verifiable statements to build even more complicated theories which, once validated in turn, will be used to establish new models and so on. This approach has proven to be sound, and we owe an immense amount of progress to it today, which has certainly moved our civilization forward…but that doesn’t necessarily mean it was the only possible path. Had the circumstances been different, our scientific method might have evolved in a very different way. This is a question that most science fiction fans have already wondered; for example, many, many observers have wondered how an extraterrestrial species could have conceptualized what we call physics or mathematics. Until very recently, all this reasoning was more a matter of thought experiment; but the game has started to change with the explosion of artificial intelligence. This technology is incredibly powerful when juggling different elements that can be very numerous and especially quite abstract. It is for this reason that AI is working wonders in areas such as computer vision. Reinventing physics from scratch Columbia University researchers have therefore decided to participate in a very original experiment: they asked an AI to rediscover for itself the laws of physics that govern the behavior of matter. But above all, I had to do it only on the basis of specific examples. He had no access to any theoretical basis such as Newton’s theorems, nor to any information about geometry. © Dan Cristian Pădureț – Unsplash His work is based on a camera that observes the evolution of a physical system, like a pendulum. And this is the only resource available to him. From these simplistic visual examples, the AI ​​is responsible for determining the number of parameters needed to describe the behavior of the system in question. In a very colorful way, it’s a bit like a scientific genius rediscovering physics in real time in a parallel dimension. Take the well-known example of the double pendulum: a pendulum hanging from the end of another pendulum. To describe it in the framework of physics as it was formalized by Newton, four parameters are needed—we are talking about state variables—that is, the angle and the angular velocity of each of the two arms. So the researchers were curious to see if the AI ​​would also find four parameters, which could possibly indicate that it would have followed the same reasoning as humans. But the proposed answer was very surprising: to describe the double pendulum, the system estimated that it would take… 4.7 parameters. AI has its reasons that reason ignores. At this point, the problem thickens. Because the “reasoning” process of these neural networks is by nature very difficult for humans to decipher; one can understand the meaning of the proposed result, but it is often impossible to determine precisely what algorithmic tricks allowed the system to reach this conclusion. Therefore, the researchers could not know what this figure corresponded to, to say the least curious. How can a number of parameters not be an integer? What can this 0.7 mean in practice? Does it make sense for humans to reason with fractional parameters? To try to answer these questions, researchers have launched a slew of new computer simulations. The goal: to compare these virtual parameters with those in real life. They were able to determine that two of the parameters proposed by the AI ​​roughly corresponded to the angle of the arms…but for the others, they have no idea. And it’s not for lack of looking. “We tried to correlate the other variables with absolutely everything and anything,” says Boyuan Chen, lead author of the study. “Angular and linear velocities, kinetic and potential energies, various combinations of other known parameters…” he quotes. “But nothing matched perfectly,” he laments. “We still don’t understand the mathematical language that AI speaks,” he summarizes. And this is where the problem becomes fascinating. Because even if the researchers do not understand the path of his algorithm, he still managed to predict the behavior of the studied systems with great precision. Bottom line: whatever the reasoning behind it, it works great. The alternative physics model built by the AI ​​is just as good as ours, even if it is incomprehensible. A true generator of “Eureka moments”? Therefore, the researchers repeated the experiment with other already well-documented mechanical systems. And each time, the result was the same: the algorithm consistently managed to predict the evolution of the mechanical system based on completely new variables that did not correspond to any parameters of Newtonian physics. “Without any prior knowledge of the physical mechanisms at play, our algorithm discovered the intrinsic dimensions of the observed dynamics and identified sets of state variables,” the researchers explain. In short, this AI doesn’t just think outside the box; she frankly imagines new ways to move. This highly exploratory work may seem as pointless as it is anecdotal, but its implications may in fact be extremely profound. They reinforce the idea that there are potentially many other ways to describe observable reality. And some of these approaches could be even more efficient than what we know today. Therefore, the challenge will be to explore these new approaches in the hope of identifying those that could be exploited by humans. This could generate major conceptual revolutions in already very advanced disciplines, where the slightest progress requires enormous efforts of imagination and experimentation on the part of humans. © FLY:D – Unsplash Concrete potential in certain areas In all honesty, there is little chance that humanity will end up becoming the “new physics” formalized by an AI; Blowing up the current foundations of science would probably be counterproductive, at least in the short term. On the other hand, this approach could work wonders in certain disciplines working on rather obscure phenomena. The most obvious example is undoubtedly that of quantum computing. Everyone agrees that this technology has enormous potential, but it is still progressing rather slowly; some of the underlying mechanisms remain poorly understood, often forcing researchers to find their way, very empirically. In such a context, one can imagine that an AI could offer very interesting clues that would allow humans to approach these problems in a radically different way, enough to pave the way for revolutionary advances. Starting from scratch each time, it would be possible to reinvent certain concepts from radically different and potentially more relevant bases. In the case of this study, the parameters formalized by the AI ​​concerned the movement of physical systems, but the concept as a whole goes far beyond this field. We could also use this approach in much more specific areas such as logistics, urban planning, climatology or public health, for example. These are activities where AI has already caused major disruption. But until then it was only complementary elements that made it possible to optimize concepts imagined by humans. Such a system, on the other hand, could make it possible to highlight phenomena and approaches that have so far completely escaped researchers… even about the workings of AIs themselves! There’s definitely a lot to be excited about for the future of AI in research, with AIs already revolutionizing scientific research, writing scientific papers about themselves, and the like. Technical documentation related to this work is available here.
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