Year: 2018
Type: dissertation
Abstract: Deep Learning has revolutionized artificial intelligence (AI) over the past decade (LeCun et. al. 2015). This has led to the creation of ‘neurally inspired’ deep-neural-networks (DNNs). DNNs are claimed to be biologically realistic in the sense of incorporating key mechanistic or architectural features of the brain, such as a hierarchical structure. Interestingly, they are also said to exhibit similar behavioral capacities, as they are capable of performing ‘near human-level’ on a variety of behavioral tasks (Kriegeskorte 2015). These similarities have led researchers to propose DNNs as biologically realistic models of behavior and brain function (ibid). In this paper, I argue that there are at least two concerns in relating DNNs to the brain. First, I suggest that DNNs might not, as they stand, exhibit biologically realistic behavior. Secondly, I argue that there are key mechanistic dissimilarities between biological and artificial neural networks that impedes a so-called modelmechanism- mapping relationship. Thus there is a mismatch between the two systems at both the behavioral and implementational levels. The explanatory status of DNNs is accordingly called into question. To defend this position, I make recourse to the mechanistic philosophy of science, a framework within which to assess a model’s explanatory status (Craver 2007).
Source: ERA
Author: David C. Harrison
Language: English
Subfield: History and Philosophy of Science
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2954725346