I’ve been recently involved in drafting a proposal for a project tender regarding the use of AI in the diagnosis of prostate cancer. Incoming data will consist of ultrasound images. Now, medical equipment is not an expertise of mine, so in the process I learnt a couple of things about the industry and I’ve come out quite frustrated. The reason for this is that some external limitations turned out to be imposed on the set of admissible AI solutions, artificially restricting the search space and, consequently, predictably forcing us to eventually settle on a sub-optimal solution. …

A few days ago I elaborated here on how points, due to their infinite precision nature, are no good as foundations for understanding space. As with all idealizations, they can be useful or not, depending on the situation. Here I’d like to present a simple, practical situation I recently run into in which points fail. I will then use it to briefly illustrate the idea behind renormalization in theoretical physics.

Consider a grey-scale image. We want to model its pixels with a random variable. Since they take values in the interval [0,1], it makes sense to model them using a…

It is humbling to see how, after the demise of Pythagorean cosmology spelled by irrational numbers, we laboriously managed to prop up what was left via the infinitesimal calculus and Newtonian cosmology… Only to see it fall again for reasons that don’t seem too different: our notion of space as a continuum of points and the related conception of matter as a collection of atoms is no good.

What is a point? If one is tempted to disregard this question with a necessarily vague answer along the lines of “points are what space is made of”, that is because one…

Mathematical physicist turned AI researcher. Knows something about deep learning and the mathematics of quantum fields. Currently works at Ennova Research.