Amanuensis: The Programmer's Apprentice
This addresses the problem of enhancing software engineering productivity through AI collaboration, but it appears incremental as it builds on existing hybrid connectionist and symbolic reasoning systems.
The paper tackles the problem of creating digital assistants that can collaborate with software engineers by learning from continuous dialog, aiming to develop cognitive strategies and intuitions to serve as versatile collaborators. The result is a system that integrates human and machine intelligence to amplify human thinking patterns and improve access to computing resources, though no concrete numbers are provided.
This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems that leverage and extend the state of the art in machine learning by integrating human and machine intelligence. As a concrete example we focus on digital assistants that learn from continuous dialog with an expert software engineer while providing initial value as powerful analytical, computational and mathematical savants. Over time these savants learn cognitive strategies (domain-relevant problem solving skills) and develop intuitions (heuristics and the experience necessary for applying them) by learning from their expert associates. By doing so these savants elevate their innate analytical skills allowing them to partner on an equal footing as versatile collaborators - effectively serving as cognitive extensions and digital prostheses, thereby amplifying and emulating their human partner's conceptually-flexible thinking patterns and enabling improved access to and control over powerful computing resources.