Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Learning a theory of causality

The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned—an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion.James S. McDonnell Foundation (Causal Learning Collaborative Initiative)United States. Office of Naval Research (Grant N00014-09-0124)United States. Air Force Office of Scientific Research (Grant FA9550-07-1-0075)United States. Army Research Office (Grant W911NF-08-1-0242

Theory Acquisition as Stochastic Search

We present an algorithmic model for the development of children’s intuitive theories within a hierarchical Bayesian framework, where theories are described as sets of logical laws generated by a probabilistic context-free grammar. Our algorithm performs stochastic search at two levels of abstraction – an outer loop in the space of theories, and an inner loop in the space of explanations or models generated by each theory given a particular dataset – in order to discover the theory that best explains the observed data. We show that this model is capable of learning correct theories in several everyday domains, and discuss the dynamics of learning in the context of children’s cognitive development.United States. Air Force Office of Scientific Research (AFOSR (FA9550-07-1-0075)United States. Office of Naval Research (ONR (N00014-09-0124)James S. McDonnell Foundation (Causal Learning Collaborative Initiative

Help or hinder: Bayesian models of social goal inference

Everyday social interactions are heavily influenced by our snap judgments about others’ goals. Even young infants can infer the goals of intentional agents from observing how they interact with objects and other agents in their environment: e.g., that one agent is ‘helping’ or ‘hindering’ another’s attempt to get up a hill or open a box. We propose a model for how people can infer these social goals from actions, based on inverse planning in multiagent Markov decision problems (MDPs). The model infers the goal most likely to be driving an agent’s behavior by assuming the agent acts approximately rationally given environmental constraints and its model of other agents present. We also present behavioral evidence in support of this model over a simpler, perceptual cue-based alternative.United States. Army Research Office (ARO MURI grant W911NF-08-1-0242)United States. Air Force Office of Scientific Research (MURI grant FA9550-07-1-0075)National Science Foundation (U.S.) (Graduate Research Fellowship)James S. McDonnell Foundation (Collaborative Interdisciplinary Grant on Causal Reasoning

People’s evaluation of programs that drive agents' behavior

We examine whether people evaluate the performance of other agents using only behavioral metrics (intuitive behaviorism) or by also taking into account the program that is driving an agent’s performance (intuitive cognitivism). In an online study, 200 participants, most without programming experience, learned to use a simple block programming language that controls a maze-solving robot. Participants then evaluated which of two programs was “better”, for 18 pairs of robots and programs. The programs varied in 3 metrics. Two of these, action efficiency and representation efficiency, were motivated by work in Reinforcement Learning and philosophy of mind, and the third, semantic generalization, is novel. We found that people’s judgements are in line with intuitive cognitivism, that people are sensitive to the program features, and that people intuitively evaluate and care about which other problems a program solves beyond the given task

Causal implicatures from correlational statements.

Correlation does not imply causation, but this does not necessarily stop people from drawing causal inferences from correlational statements. We show that people do in fact infer causality from statements of association, under minimal conditions. In Study 1, participants interpreted statements of the form "X is associated with Y" to imply that Y causes X. In Studies 2 and 3, participants interpreted statements of the form "X is associated with an increased risk of Y" to imply that X causes Y. Thus, even the most orthodox correlational language can give rise to causal inferences

Bayesian Models of Conceptual Development: Learning as Building Models of the World

A Bayesian framework helps address, in computational terms, what knowledge children start with and how they construct and adapt models of the world during childhood. Within this framework, inference over hierarchies of probabilistic generative programs in particular offers a normative and descriptive account of children's model building. We consider two classic settings in which cognitive development has been framed as model building: ( a) core knowledge in infancy and ( b) the child as scientist. We interpret learning in both of these settings as resource-constrained, hierarchical Bayesian program induction with different primitives and constraints. We examine what mechanisms children could use to meet the algorithmic challenges of navigating large spaces of potential models, in particular the proposal of the child as hacker and how it might be realized by drawing on recent computational advances. We also discuss prospects for a unifying account of model building across scientific theories and intuitive theories, and in biological and cultural evolution more generally.NSF (Award CCF-1231216

Adults' Evaluations of Rote and Reflective Teachers

Decision-making can be automatic (“rote”) or on-the-fly (“reflective”). Are people sensitive to whether others are behaving rotely or reflectively? The rote-versus-reflective inference may be particularly relevant when learning from others: good teachers should be actively considering the learner’s needs. When teachers rely on rote systems, this may “break” the mental state recursion that facilitates learning from pedagogy. This study takes a first step in investigating learning and inference when teachers use rote-versus-reflective reasoning, using uniqueness of feedback as a cue to reflectivity. Adult participants viewed videos of teachers providing either identical, similar, or unique feedback to three different groups of students; participants evaluated that teacher along several metrics. Consistent with our predictions, rote teachers were evaluated as poorer informants than reflective teachers, and students paired with rote teachers were expected to learn less. These results are the first to demonstrate sensitivity to, and impact from, inferences about others’ rote-versus-reflective behaviors

Loopholes, a Window into Value Alignment and the Learning of Meaning

Finding and exploiting loopholes is a familiar facet of fable, law, and everyday life. But cognitive, computational, and empirical work on this behavior remains scarce. Engaging with loopholes requires a nuanced understanding of goals, social ambiguity, and value alignment. We trace loophole behavior to early childhood, and we propose that exploiting loopholes results from a conflict in actors' goals combined with a pressure to cooperate. A survey of 260 parents reporting on 425 children reveals that loophole behavior is prevalent, frequent, and diverse in daily parent-child interactions, emerging around ages five to six and tapering off from around ages nine to ten into adolescence. A further experiment shows that adults consider loophole behavior in children as less costly than non-compliance, and children increasingly differentiate loophole behavior from non-compliance from ages four to ten. We discuss limitations of the current work together with a proposal for a formal framework for loophole behavior