From Machine Learning to Machine Psychology
Date and time: 11 September 2024, 16:00-18:00 CEST (UTC +2)
Speaker: Robert Johansson, Stockholm University
Title: From Machine Learning to Machine Psychology
Where: Digital Futures hub, Osquars Backe 5, floor 2 at KTH main campus OR Zoom
Directions: https://www.digitalfutures.kth.se/contact/how-to-get-here/
OR
Zoom: https://stockholmuniversity.zoom.us/j/67385156109
Abstract: In the quest for Artificial General Intelligence (AGI), this work advocates for incorporating operant conditioning — a fundamental principle from behavioural psychology extensively studied within the context of learning and adaptive behaviours with animals and humans — into AI research.
We introduce Machine Psychology, a novel framework that merges behavioural psychology principles with the Non-Axiomatic Reasoning System (NARS), an AI model known for its real-time learning and adaptation capabilities under constraints of limited knowledge and computational resources.
This framework presents a promising path for AGI advancement by leveraging the profound understanding of learning, adaptation, and complex decision-making processes provided by behavioural psychology. Specifically, the design of NARS aligns well with the application of operant conditioning paradigms, offering a method to explore the dynamics of intelligence evolution and adaptability with computer systems.
We propose a methodology that simultaneously manipulates NARS’ experiential inputs and operational dynamics to investigate the conditions necessary and sufficient for achieving behavioural changes aligned with operant conditioning principles. This methodology enables a systematic exploration of adaptive behaviours with NARS, following a behavioural psychology-informed approach to progress towards AGI.
Bio: The main area for Robert’s research is artificial general intelligence (AGI). The research is based on the premise that general-purpose intelligence can be seen as an instance of an abstract response pattern called arbitrarily applicable relational responding (AARR).
For a longer definition of AARR and how it relates to AGI, please see the 2019 paper linked below:
- Johansson, R. (2019). Arbitrarily applicable relational responding. In International Conference on Artificial General Intelligence (pp. 101-110). Springer, Cham. Link to paper.
For a longer description of what we mean by Machine Psychology and our approach to AGI, please see the recent paper linked below:
- Johansson, R. (2024). Machine Psychology: Integrating Operant Conditioning with the Non-Axiomatic Reasoning System for Advancing Artificial General Intelligence Research. Frontiers in Robotics and AI, 11, 1440631. Link to paper.
Robert also researches clinical psychology, where he has a broad range of interests, particularly in emotion-focused psychotherapy models. Please see his Google Scholar page for representative publications.