Amanuensis: The Programmer's Apprentice

TL;DR

Amanuensis introduces a hybrid AI system that learns from expert engineers through dialogue, combining cognitive neuroscience insights with machine learning to create adaptable digital assistants that enhance human problem-solving capabilities.

Contribution

The paper presents a novel hybrid connectionist-symbolic framework for digital assistants that learn cognitive strategies and heuristics from human experts through continuous interaction.

Findings

Digital assistants improve their problem-solving skills over time

They develop heuristics and cognitive strategies from expert interaction

Enhanced collaboration between humans and AI systems

Abstract

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.