Combining Diverse Forms of Human and Machine Intelligence

Artificial Intelligence algorithms never operate in isolation but are always part of broader processes that often involve humans, other computer algorithms, incentive structures, and interfaces which modulate the interaction between them. This thesis takes this perspective and considers these broader processes by studying specific combinations of three forms of intelligence: symbolic artificial intelligence, neural artificial intelligence, and human intelligence. First, diverse forms of Neuro-Symbolic AI through three pipelines consisting respectively of neural perception with symbolic reasoning, symbolic inputs with neural reasoning, and a dual-integration that learns representations which are simultaneously symbolic and neural (Chapter 2); second, the AI research community as a Human-Symbolic combination through the presentation of a taxonomy of AI models, tasks and datasets (Chapter 3); and third, a specific form of Human-AI intelligence observing that a human in combination with GPT-3 can perform an HTML code generation task better than either humans or computers alone (Chapter 4).

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Campero Nuñez, Andres

Advisors dc:contributor.advisor

• Malone, Thomas W.
• Tenenbaum, Joshua B.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/145602

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/145602