Sketching: A Cognitively inspired Compositional Theorem Prover that Learns to Prove

Brando Miranda

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https://hdl.handle.net/2142/112737 Copy

Description

Title

Sketching: A Cognitively inspired Compositional Theorem Prover that Learns to Prove

Author(s)

Brando Miranda

Issue Date

2018-12-11

Keyword(s)

• agi
• theorem proving
• sketching
• sketch
• machine learning
• artificial intelligence

Abstract

Current advances in Deep Learning have accelerated A.I. in an unprecedented way. Now we can solve tasks that seemed beyond our reach like language translation, beating top human Go players and generating images indistinguishable from real ones. The progress is impressive but it has been at the cost of transparency. As models become larger and more complex it has become increasingly difficult to understand what they do reliably at many levels of abstraction. One level that inspires me is the mathematical level. Do current Deep Learning models generalize because of implicit regularization? Is the role of over-parameterization the factor that leads to learnability via gradient descent in highly non-convex landscapes? Are classical statistical learning bounds useful in characterizing Deep Neural Networks? With the increased investment in A.I. and people fearlessly building, designing and hacking, the race to explain what all these models do becomes unmanageably complex with our current mathematical tools.

Type of Resource

text

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http://hdl.handle.net/2142/112737

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