Remodelling machine learning: An AI that thinks like a scientist

ruticker 06.03.2025 12:21:00

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Modern science generates a lot of data, and it mounts far beyond the ability of humans to process on their own. This is why scientists often rely on computational methods, such as **machine learning**, a type of artificial intelligence, to crunch big data. In fact, it's pretty commonplace today and is even used in applications like **Spotify** and **Netflix** to help predict your media preferences. But there's a problem. Traditional approaches to machine learning, such as **deep learning**, are focused on classification and statistical correlation. This makes them very useful for recognizing and comparing patterns in things like audio or images, but not very good at discovering what might have generated the patterns in the first place. Understanding these so-called **generating mechanisms**—in other words, the dynamics of cause and effect—is a fundamental challenge across all scientific fields. From decoding how a complex biological cell arises from DNA to figuring out how a thunderstorm forms from differences in air temperature and humidity, such tasks require abilities such as inference or reasoning, which most artificial intelligences are not designed for. To tackle these challenges, one team of scientists is developing a revolutionary new form of machine learning, which they've called **causal deconvolution by algorithmic generative models**. This basically means breaking down complicated and interconnected data sets to look at the underlying mechanisms or code that generate them. It aims to find the individual underlying algorithms that give rise to a data set, in a sense treating natural phenomena as computer programs generating observed data. We can think about this with a visual analogy: if we have a table with several objects on it and we lay a cloth over it, we're presented with a unique 3D terrain. This represents a mass of collected data; some of it relates to the objects underneath, and some doesn't. Modern machine learning, which is based on classical statistics, is good at identifying objects by comparing features such as shape to previous examples. It's been shown that what it can't do is tell us much about how the objects underneath were formed. The aim of this new methodology is to first work out which bits of the terrain relate to the different objects—this is the **deconvolution** part—and then to identify the objects by looking at how they might be generated. This is done by testing lots of possible coffee cup, hat, or book generating algorithms and looking at how these could influence the tablecloth arrangement. So, unlike traditional machine learning, it attempts to infer how the objects might have been generated to match the observed data. But this isn't just intended to work on imaginary dinosaurs; it's hoped that it can be applied to help scientists unravel the dynamics of cause and effect in fields such as **genetics** and **cell biology**. For example, in understanding the complicated gene interactions that give rise to cancer. That's because this methodology moves away from classification and towards questions of causation, providing insights and models to help us understand the underlying mechanisms behind observed data. So, as machine intelligence continues to develop, researchers may become increasingly reliant on AIs that think less like machines and more like scientists.

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