Amanuensis: The Programmer's Apprentice
Thomas Dean, Maurice Chiang, Marcus Gomez, Nate Gruver, Yousef Hindy,, Michelle Lam, Peter Lu, Sophia Sanchez, Rohun Saxena, Michael Smith, Lucy, Wang, Catherine Wong

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…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Scientific Computing and Data Management · Reinforcement Learning in Robotics
