Machine Reason: Algorithmic Insight for Humanists

Colloquium
Lynn Holt,  Clint Johnson,  Ross Mc Cool,  Jonathan Barlow  

Machine learning is on the verge of re-discovering Aristotle. This is doubly ironic: while pursuing the four hundred year old algorithmic ideal of reason, the new wave of deep learning not only takes a two millennium retro turn, it rejects the anti-Aristotelianism animating early modern thinkers. But the Aristotelian quality of deep learning should not give hope to Luddites and atavists. On the contrary, we hope that it spurs humanists to engage and contribute to the frontiers of artificial intelligence. Our four person project has aims at three distinct but interwoven levels: Theoria. To embed the history of machine learning in the wider history of the early modern calculative re-conception of reason, to articulate the distinctively apprehensive nature of Aristotelian reason, to show how calculative reason and machine learning requires apprehensive reason to accomplish its own aims, and to examine the transformation of objectivity which results. Techné. To explore innovative ways of constructing and informing artificial neural networks with the goal of instantiating machine analogues of Aristotelian intellectual “virtues”, and to break out of the replacement paradigm with circular modes of human-machine interaction. Praxis. To put theoria and techne to the test in a practical and open-ended setting in the domain of career counseling for teens and young adults. We aim to make an existing app smarter and make it interact with human counselors. In addition to brief presentations on theoria, techné, and praxis, our colloquium will include a propaedeutic on algorithmic machine learning with a focus on neural networks.