Andrews – Machine Learning and the Scientific Method – DS² 2021
Channel: CEFISES at UCLouvain
Date: 2021-03-16

The talk “Machine learning and the scientific method: the case of the Free Energy Principle” by Mel Andrews (University of Cincinnati), recorded at the DS² 2021 online conference, March 15, 2021.



Philosophers have long made progress on understanding natural systems by analogising them with technological advances. Memory is like etchings upon a stone tablet. An animal is like a furnace. The mind is a clockwork. The organism is an automaton. Genetic material encodes data. The brain is a computer. Cognition is an algorithm that functions to predict the hidden causes of our sensations.

New technologies also serve as formal, physical, and conceptual models of target systems, and their development is informed by empirical discoveries in the sciences. Emerging technologies in machine learning promise a radical disruption of industry, society, and scientific practice. With the increasing prevalence and influence of machine learning comes an imperative to understand how these techniques come into play in an empirical context. Machine learning poses a unique puzzle in this respect, as the history of innovation in this domain is intrinsically tied to the history of discovery in the cognitive and neurosciences. Thus the relation between the empirical study of mind and brain and the development of techniques in artificial intelligence is one of bidirectional influence. In this essay, I map out this nascent area of study for the philosophy of science, making several key distinctions by way of a relevant case study: the concept of the brain as a predictive engine, as exemplified by a recent mathematical framework known as the free energy principle. I delineate three ways of interpreting the notion of brain and body as engaging in Bayesian inference: (approximate) realism, metaphor, and model or research heuristic.

Is artificial intelligence built up from our empirical knowledge of biological intelligence? Or is our empirical investigation of biological intelligence enabled by technological developments in artificial intelligence? The history of the cognitive and brain sciences reveal a generative dialectic between advances in our understanding of biological intelligence and advances in artificial intelligence. Further, the methods of machine learning serve a dual role in their support of empirical science, doubling as both conceptual tool and formal representation. Because the process of discovery in cognitive science and of invention in machine learning are so tightly interwoven, disentangling representations and heuristics from claims intended in a realist spirit can be involute.

In this paper I will urge that machine learning plays three conceptually distinct—yet functionally entangled—roles in cognitive science, neuroscience, and biology. As a metaphor, it lends explanatory grasp over complex natural systems, which can be fruitfully understood as predicting states in their environments, as well as their own future states. The formal structure of machine learning---both computational and mathematical---is also employed in modelling cognitive and biological phenomena, with and without the associated analogy, by treating biological or behavioural dynamics as though they ought to conform approximately to a utility or reward function, or by simulating them in-silico. Lastly, as we gain empirical insights into the function of the brain, at every level of abstraction, these come to inform developments in machine learning. As such, there is an extent to which the form and function of machine learning algorithms echoes the form and function of their biological counterparts. This, in turn, seems to warrant a degree of realism about the notion of the brain as a predictive engine precluded in historically antecedent technological analogies.



As the technological prominence of machine learning grows, it will come to play an ever greater role in science. This places a demand on philosophers of science to sort out how exactly it aids, and is informed by, empirical work in the cognitive, neuro, and biological sciences. I take preliminary steps towards this end in extrapolating the conclusions I reach on the free energy principle to the convoluted relationship between machine learning and empirical science at large. Three distinct roles for machine learning in scientific practice come to the fore: one analogical, one as a heuristic representation, one as a literal, if approximate, representation.