Computational constructivism: The algorithmic basis of discovery
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Philosophy Psychology & Language
Abstract
One of the defining aspects of being human is an ability to flexibly generate new ideas and hypotheses. For example, we readily come up with possible faults if our car breaks down, or plausible maladies when we feel unwell. We can brainstorm anything from party ideas, to corporate strategies, to magical creatures, and frequently hypothesise hidden motivations and beliefs in our peers to explain why they act the way they do. Our ideas often combine familiar objects, concepts, and relations, making them symbolic, easy to communicate, and a ready guide for follow up queries or evidence seeking. For example, suppose you came home from work to find your house in disarray. You might quickly suspect you have been burgled and investigate by checking whether valuables are missing. If you then discover feathers on the floor this might inspire other possibilities. Perhaps a bird got in through an open window and ran amok. These kinds of inventive inferences come quickly and easily for us, but are surprisingly difficult for artificial intelligence systems. Part of this difficulty is that, for the kinds of natural domains mentioned above, there is typically an infinite number of possibilities one could generate, but few good ones. Our best ideas have the character of "ah ha" moments, immediately providing a better explanation than preceding candidates and potentially becoming a lasting addition to one's beliefs or knowledge base.
The key aim of this project is to develop algorithms that emulate the way humans generate, adapt and actively investigate such hypotheses in everyday life. The basic idea is that we combine our more primitive concepts to form more complex ideas, essentially "trying out" different combinations of primitives and connectives when searching for a better explanation, or adapting one that does not fit the latest evidence. Such a search process is governed by overarching principles of simplicity and fit to the evidence, but constrained by our finite thinking time and capacity. For example, in the above example you might rapidly generate, refine or overturn several hypotheses as you investigate the mess, discovering a feather duster, cleaning products, and finally your partner in the midst of a spring clean.
"Program induction" is a powerful new mathematical framework for constructing symbolic models or programs that can explain or reproduce observations. Induced programs can grow in structure and complexity as evidence is encountered, reusing past solutions as and composing them to solve new problems. We propose to use this as a framework to capture and ultimately synthesise humanlike hypothesis generation. To closely examine human hypothesis generation, we will combine theoretical work in the program induction framework with experiments with human adults. In our inductive learning tasks, participants and our algorithms will both observe and create their own physical scenes made up of simple geometric blocks and test them to discover and generate hypotheses regarding under what conditions they will produce a novel causal effect (i.e. in our pilot, produce a "newly discovered form of radiation"). This setup allows us to explore arbitrarily complex hidden causal effects that can involve combinations of features and relations, meaning the participants (and our algorithms) must use hypothesis generation, reasoning and active testing to identify the ground truth in each case.
Through our modelling and our experiments we expect to deepen understanding of the mechanisms that underpin the uniquely human ability to make explanatory inferences. We expect our findings to influence robotics, and AI communities providing insight into how to build artificial systems that can better emulate, understand and be understood by humans. The goal of this project thus to develop a precise algorithmic account of idea generation in human learning that we call "computational constructivism".
The key aim of this project is to develop algorithms that emulate the way humans generate, adapt and actively investigate such hypotheses in everyday life. The basic idea is that we combine our more primitive concepts to form more complex ideas, essentially "trying out" different combinations of primitives and connectives when searching for a better explanation, or adapting one that does not fit the latest evidence. Such a search process is governed by overarching principles of simplicity and fit to the evidence, but constrained by our finite thinking time and capacity. For example, in the above example you might rapidly generate, refine or overturn several hypotheses as you investigate the mess, discovering a feather duster, cleaning products, and finally your partner in the midst of a spring clean.
"Program induction" is a powerful new mathematical framework for constructing symbolic models or programs that can explain or reproduce observations. Induced programs can grow in structure and complexity as evidence is encountered, reusing past solutions as and composing them to solve new problems. We propose to use this as a framework to capture and ultimately synthesise humanlike hypothesis generation. To closely examine human hypothesis generation, we will combine theoretical work in the program induction framework with experiments with human adults. In our inductive learning tasks, participants and our algorithms will both observe and create their own physical scenes made up of simple geometric blocks and test them to discover and generate hypotheses regarding under what conditions they will produce a novel causal effect (i.e. in our pilot, produce a "newly discovered form of radiation"). This setup allows us to explore arbitrarily complex hidden causal effects that can involve combinations of features and relations, meaning the participants (and our algorithms) must use hypothesis generation, reasoning and active testing to identify the ground truth in each case.
Through our modelling and our experiments we expect to deepen understanding of the mechanisms that underpin the uniquely human ability to make explanatory inferences. We expect our findings to influence robotics, and AI communities providing insight into how to build artificial systems that can better emulate, understand and be understood by humans. The goal of this project thus to develop a precise algorithmic account of idea generation in human learning that we call "computational constructivism".
Planned Impact
1. Who might benefit from this research? And How?
By modelling the mechanisms by which people discover patterns and constraints in data, our project will lead to systems that can emulate, understand, and be understood by humans and will inform the design of teaching resources to convey scientific and mathematical principles and facilitate learning. The stakeholders who stand to benefit from such systems thus include:
a) Developers of systems that perform well in low-resource or "small data" settings
b) Manufacturers of robots and other systems where counter-intuitive or surprising behaviour can be costly and "programming by example" may open the door to novel applications.
c) Educators who will benefit from a better understanding of how examples can be tailored to help a child
discover a scientific or mathematical principle.
To reach the tech community including developers and roboticists we have planned a number of dissemination activities including:
a) Connecting with businesses though AIMday collider events and the Data Science Research Days organised by the University Edinburgh; running a training session with a London-based AI consultancy
b) Training our PhD and MSc students many of whom have already gone on to work with major tech industry players including Google, Facebook, Amazon and Netflix
c) Publishing generalist articles in popular tech publications like Wired and the MIT Tech Review
To reach educators we plan to:
a) Publish generalist articles on the Gender Teaching Council for Scotland's blog
b) Host a focus group with teachers to discuss practical implications and limitations
c) Offer public facing lectures at the Scottish Learning Festival and Edinburgh International Science Festival
For both the tech community and education community we plan to leverage social media to network promote our findings. We also plan to take the first steps toward converting our learning task into a gamified learning app to leverage the value integrating cognitive research and technology.
2. How will we evaluate impact?
For blogs, articles, lectures, and tweets, we will judge impact by the size of the audience or readership, number of retweets. For our data science resources, we will judge impact by the number of people forking and downloading our tasks and datasets. We will use our interactions with the business and tech community at our collider and research days and the education community at our focus group to seek direct feedback on our practical impact and will interact with our KE officer at Edinburgh and our Advisory panel internationally on a rolling basis to get assessments of our impact success and will potentially adjust our impact strategy where necessary as a result.
By modelling the mechanisms by which people discover patterns and constraints in data, our project will lead to systems that can emulate, understand, and be understood by humans and will inform the design of teaching resources to convey scientific and mathematical principles and facilitate learning. The stakeholders who stand to benefit from such systems thus include:
a) Developers of systems that perform well in low-resource or "small data" settings
b) Manufacturers of robots and other systems where counter-intuitive or surprising behaviour can be costly and "programming by example" may open the door to novel applications.
c) Educators who will benefit from a better understanding of how examples can be tailored to help a child
discover a scientific or mathematical principle.
To reach the tech community including developers and roboticists we have planned a number of dissemination activities including:
a) Connecting with businesses though AIMday collider events and the Data Science Research Days organised by the University Edinburgh; running a training session with a London-based AI consultancy
b) Training our PhD and MSc students many of whom have already gone on to work with major tech industry players including Google, Facebook, Amazon and Netflix
c) Publishing generalist articles in popular tech publications like Wired and the MIT Tech Review
To reach educators we plan to:
a) Publish generalist articles on the Gender Teaching Council for Scotland's blog
b) Host a focus group with teachers to discuss practical implications and limitations
c) Offer public facing lectures at the Scottish Learning Festival and Edinburgh International Science Festival
For both the tech community and education community we plan to leverage social media to network promote our findings. We also plan to take the first steps toward converting our learning task into a gamified learning app to leverage the value integrating cognitive research and technology.
2. How will we evaluate impact?
For blogs, articles, lectures, and tweets, we will judge impact by the size of the audience or readership, number of retweets. For our data science resources, we will judge impact by the number of people forking and downloading our tasks and datasets. We will use our interactions with the business and tech community at our collider and research days and the education community at our focus group to seek direct feedback on our practical impact and will interact with our KE officer at Edinburgh and our Advisory panel internationally on a rolling basis to get assessments of our impact success and will potentially adjust our impact strategy where necessary as a result.
Organisations
Publications
Zhao B.
(2022)
Powering up causal generalization: A model of human conceptual bootstrapping with adaptor grammars
in Proceedings of the 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022
Quillien T
(2023)
Rational information search in welfare-tradeoff cognition.
in Cognition
Btesh VJ
(2022)
Redressing the emperor in causal clothing.
in The Behavioral and brain sciences
Yarkoni T
(2020)
The generalizability crisis.
in The Behavioral and brain sciences
Tadeg Quillien
(2022)
The logic of guesses: how people communicate probabilistic information
Quillien T.
(2022)
The logic of guesses: how people communicate probabilistic information
in Proceedings of the 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022
Description | Internal Research Support Grant Scheme |
Amount | £3,000 (GBP) |
Organisation | University of Edinburgh |
Sector | Academic/University |
Country | United Kingdom |
Start | 08/2022 |
End | 05/2023 |
Description | Turing Postdoc Awards |
Amount | £2,000 (GBP) |
Organisation | Alan Turing Institute |
Sector | Academic/University |
Country | United Kingdom |
Start | 01/2021 |
End | 01/2022 |
Description | Cognition, Communication and Computation Workshop |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | We brought together current researchers across PPLS and the ILCC in Informatics who work on computational models of cognition and communication. The goal of the workshop was to bring our existing experts in computational data-driven cognitive science together to exchange ideas, form new scientific collaborations and seed future funding applications. The topic aligns with the Computational Constructivism remit: Our invitees' research explores the principles and processes that give rise to intelligence in humans and how to synthesize these in AI systems. We variously work on computational creativity, learning, education and communication and social dynamics. This highly successful workshop provided an opportunity to meet and exchange ideas, leveraging our expertise toward the grant aims. |
Year(s) Of Engagement Activity | 2021 |
Description | Colloquium at Central European University |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Gave a talk on grant related topics |
Year(s) Of Engagement Activity | 2021 |
Description | Colloquium at U of New South Wales, Sydney |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Gave a talk on grant related topics at UNSW |
Year(s) Of Engagement Activity | 2021 |
Description | Colloquium at U of Surrey |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Gave a talk on grant related topics to Surrey Psych department |
Year(s) Of Engagement Activity | 2021 |
Description | Computational Cognitive Science Workshop |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | We ran a workshop on Comptutational Cognitive Science in Edinburgh inviting keynotes from Deep Mind and MIT and showcasing the lab's various projects, largely related to this grant. |
Year(s) Of Engagement Activity | 2022 |
Description | Symbolic and Subsymbolic Systems in People and Machines Workshop |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | We organised a large and extremely well attended workshop in Canada for CogSci 2021. We organised a line up of internationally respected speakers and also showcased much of the grant work. To what extent is symbolic processing required for intelligent behavior? Advances in both sub-symbolic deep learning systems and explicitly symbolic probabilistic program induction approaches have recently reinvigorated this long standing question about cognition. While sub-symbolic approaches have shown impressive results, they still lag far behind human cognition, e.g., in the compositional re-use of learned concepts or generalizing to new contexts. Symbolic systems have successfully addressed some of these shortcomings, but face other unsolved issues relating to feature selection, thorny search spaces and scalability. |
Year(s) Of Engagement Activity | 2021 |
URL | https://sassy-2021.github.io/ |
Description | Talk to Cognitive Science Society |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Undergraduate students |
Results and Impact | Just a talk on grant related things to an undergraduate society. |
Year(s) Of Engagement Activity | 2022 |