The following appeared as a coda in my Ph.D. dissertation.

The phenomena of the world which have to be explained present countless ends to us, of which one only can be the right one; they resemble an intricate tangle of thread, with many false end-threads hanging from it. He who finds out the right one can disentangle the whole. – Arthur Schopenhauer

Looking back on my academic journey, I see a path from a fascination with the possible applications of autonomous systems to a fascination with autonomy in-and-of-itself. As a budding researcher, I saw robust, accurate perception as a means towards an end. An end which entailed truly autonomous systems ‘perceiving’ and ‘interpreting’ their surroundings with the goal of exploring distant planets and navigating busy urban streets. Now, however, I see perception as an end in itself with a plethora of fascinating mathematical, philosophical and ethical challenges that can be tackled in light of, but not subservient to, the potential goals of some grander autonomous system. Throughout this transition, I have become more interested not only in the flesh and blood of perception systems, but also in the spirit of them. If perception is one of the bridges we must build to reach the land of autonomy, I am concerned not only with the structural integrity that lets us cross it today, but also with an elegance and rigour that lets it serve as a model for posterity.

With this in mind, I want to address a concerning shift that has occurred in the research community throughout my academic career. Many researchers who work on algorithms that enable autonomy (not only in perception, but also in planning and controls) have given up on the dream of modelling the world with the tools of Euclid, Newton and Euler in favour of methods that rely on exemplary data to ‘train’ arbitrarily complex predictive black-box models. In my estimation, this shift has brought with it a certain sense of resignation to the overwhelming complexity of the world. We are often content to use vague notions of complexity as reason to avoid building analytic models. Instead, we turn to crude, inscrutable surrogates of our own brains to model what we do not want to. This, I believe, is a tempting mistake. Although these solutions may serve as useful tools to temporarily bridge gaps in our understanding of the world, we will inevitably deplete the low-hanging empirical fruits that they can bear, and we will be left with a deep sense of dissatisfaction that only elegance can fill.

I am certainly not the first or the last person who has taken issue with data-driven methods. Noam Chomsky gives the following critique of purely statistical approaches to science. Consider the study of bee colonies. In order to to extract interpretable models of their behaviour (e.g., there is a queen bee, there are worker bees, etc.), one has to observe these colonies meticulously over generations. So why not avoid that entire endeavour and use a data-driven approach? We could set up a camera to observe a bee colony and collect data over several years. By tracking each bee, we could use the tools of modern machine learning to construct and train a large parametric model of each of their positions. Once complete, we could then query this model with a new image from our camera and recover, with extreme precision, the predicted location of each bee. This may allow us to improve honey production, but what have we learned? Is this an elegant model of bee behaviour? Have we not just transformed the problem of understanding the bees into one of understanding this surrogate model? Now consider doing the same with celestial objects–Kepler be damned!

Some may argue that the entire goal of science is predicting the future states of nature, so elegance is irrelevant. I vehemently disagree. I would rather have an interpretable model that is wrong during specific situations (where I can verify that certain assumptions are violated), rather than an obfuscated model which has vague limits to its predictive power.

If history is any judge, the models that stand the test of time are ones that are born out of our meticulous labour and enlightened insight to extract salient principles out of the complexity of the world. The hope that this labour can be replaced with black-box surrogates that indirectly learn these same principles is troubling and, in my view, unnecessary. No matter how much anthropomorphic language we use to describe these surrogates (endowing them with ‘understanding’, ‘attention’, and ‘forgetfulness’), they will always be limited by our own ability to collect sufficient data, and by our ability to craft them in such a way as to consume significant amounts of training exemplars without `overfitting’ to them. What’s more, if these models have any interaction with the world, they will also affect the world, and we are committing ourselves to an endless game of cat-and-mouse. Although it may seem that our time is best spent crafting ever-more-clever surrogates, we will soon reach a point where we would be better off using the time and resources towards studying a particular problem more directly.

I do not want to cast aspersions flippantly. The transition to data-driven approaches in computer vision happened for good reason and with much hesitation. The elegance of analytic models has historically only been exceeded by their inability to model the often inelegant ‘real world’. At the turn of the twenty-first century, roboticists were joking that the dirty secret of much of computer vision is that it doesn’t work. Recent efforts into combining the connectionist ideas of the 20th century with the computational power accessible in the 21st have undoubtedly created systems that do attain impressive empirical results, and there is a constant stream of new theoretical insights into the types of structures and optimization methods that work well in a given domain.

However, as the world becomes more connected and complex with every passing day, I think it is of utmost importance that autonomy researchers are not tempted to focus solely on empirical results at the cost of elegant solutions. It is now well-accepted that data-driven methods are not the panacea (like it might have seemed for a brief moment a few years ago) to all problems in autonomy. However, this passive agreement may not be enough. Instead, we need to actively suppress the urge to try and solve a problem first through general ‘learning’ methods that are becoming more and more easy to implement and less and less easy to understand. We do not need to relinquish the dream of understanding the world and relegate ourselves to simply predicting it by any means possible. We can instead strive to simplify it and interpret it. If after significant effort we fail at that goal, and only then, should we turn to data-driven learned models to fill in the gaps in our understanding.