AI as a Tool to Understand Our World & Science

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TLDR:

Leaders: Neural Networks as the Secret for Scientific Discovery

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Regarding AI, today’s narrative is all about productivity.

It’s what every single product pitch is telling us: “Here’s your AI companion”, “Look at your brand new work accelerator”, “Become a 10x programmer”, and so forth.

But one exciting trend has been emerging for some time and is picking up speed lately: AI as a tool for scientific discovery. In other words, using the power of Neural Networks (NNs) to uncover the mysteries of the world.

Sounds too good to be true, but there are amazing pieces of research to back it.

And the very same people pushing for this vision have another great plan, a foundation model for science that could change the world’s rate of innovation like nothing we’ve ever seen before.

The Great Algorithm

Although the first signs of neural networks date back to 1943 and the perceptron was discovered back in 1958, it wasn’t until the 2010s with the publication of AlexNet that we managed to prove that the theory behind them actually worked.

But first, what is a Neural Net?

Mimicking the Brain… Kind of

Neural networks are Machine Learning algorithms that were conceived as a way to teach machines to learn by mimicking the functioning of the human brain.

NNs are formed by a set of parameters, unsurprisingly named ‘neurons’, that learn from the data.

In broad terms, by activating and deactivating themselves for a given input, they create different combinations that generate a map between the input data and the predictions we actually want the machine to perform.

Thus, the objective of this learning process is to “learn” this mapping between the inputs and the outputs.

This can be done through stochastic gradient descent variants, in which we measure the loss (the difference between the ground truth and the model’s prediction) and ‘tune’ the parameters of the network by measuring their gradients (indicating the rate of change they have over the loss) to, over time, minimize it.

Broadly speaking, it’s a trial-and-error exercise.

The most common optimization algorithm is not SGD, but Adam.

Today, NNs represent the absolute state-of-the-art of the industry, fueling almost every breakthrough that has taken place over the last 10 years.

But why are they so powerful?

The Universal Approximation Theorem

The reason behind the success of NNs is the Universal Approximation Theorem, which is "a formal proof that, with enough hidden units, a shallow neural network can describe any continuous function to arbitrary precision."

I won’t get into the technical details (for that, I highly recommend the following book) but the key intuition is that we can use NNs to model basically anything.

In other words, for any…

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