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

Perceptual multistability as Markov Chain Monte Carlo inference

While many perceptual and cognitive phenomena are well described in terms of Bayesian inference, the necessary computations are intractable at the scale of real-world tasks, and it remains unclear how the human mind approximates Bayesian computations algorithmically. We explore the proposal that for some tasks, humans use a form of Markov Chain Monte Carlo to approximate the posterior distribution over hidden variables. As a case study, we show how several phenomena of perceptual multistability can be explained as MCMC inference in simple graphical models for low-level vision

Compositional Policy Priors

This paper describes a probabilistic framework for incorporating structured inductive biases into reinforcement learning. These inductive biases arise from policy priors, probability distributions over optimal policies. Borrowing recent ideas from computational linguistics and Bayesian nonparametrics, we define several families of policy priors that express compositional, abstract structure in a domain. Compositionality is expressed using probabilistic context-free grammars, enabling a compact representation of hierarchically organized sub-tasks. Useful sequences of sub-tasks can be cached and reused by extending the grammars nonparametrically using Fragment Grammars. We present Monte Carlo methods for performing inference, and show how structured policy priors lead to substantially faster learning in complex domains compared to methods without inductive biases.This work was supported by AFOSR FA9550-07-1-0075 and ONR N00014-07-1-0937. SJG was supported by a Graduate Research Fellowship from the NSF

Probing the compositionality of intuitive functions

How do people learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is accomplished by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels. We show that participants prefer compositional over non-compositional function extrapolations, that samples from the human prior over functions are best described by a compositional model, and that people perceive compositional functions as more predictable than their non-compositional but otherwise similar counterparts. We argue that the compositional nature of intuitive functions is consistent with broad principles of human cognition.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF – 1231216

Human learning in Atari

Atari games are an excellent testbed for studying intelligent behavior, as they offer a range of tasks that differ widely in their visual representation, game dynamics, and goals presented to an agent. The last two years have seen a spate of research into artificial agents that use a single algorithm to learn to play these games. The best of these artificial agents perform at better-than-human levels on most games, but require hundreds of hours of game-play experience to produce such behavior. Humans, on the other hand, can learn to perform well on these tasks in a matter of minutes. In this paper we present data on human learning trajectories for several Atari games, and test several hypotheses about the mechanisms that lead to such rapid learning.National Science Foundation (U.S.) (Award CCF-1231216

The effects of the 2020–2021 Coronavirus pandemic change-event on football refereeing: evidence from the Israeli and Portuguese leagues

Upon the emergence of the 2020 Coronavirus pandemic (CP), football referees were forced to cope with the interruption of leagues and experience quarantines, with a potential threat to their physical and psychological well-being. This study examined referees’ perceptions of the CP change-event, particularly the effects on refereeing engagement and performance, in part to facilitate more effective support. For this study, an online inventory was circulated during January–February 2021 to 198 referees and assistants from Israel and Portugal, asking them to reflect on the CP in the context of their careers, and the potential effects on theirrefereeing engagement and performance. The results indicated that the CP was perceived as a moderate change-event in terms of significance, severity, and coping, although many participants didconsider it as significant. The participants indicated just a minor reduction in their refereeing quality between the 2019–2020 and the 2020–2021 seasons. The main areas affected were training on agrass field, decision-making training, and financial status. Still, refereeing instruction (conducted mainly online) improved. The behavioural effects were more forceful among the nonprofessional referees, suggesting that Referee Associations must pay closer attention to support these populations. The participants’ motivation, refereeing identity, and self-efficacy were actually improved. Finally, the absence of the crowd in matches allowed the referees to be more aware of their actions and better communicate with players and coaches, which related to better performance. These findings further emphasize the social aspect of football refereeing and the importance of having upright management and communication skills.The authors would like to thank the Israel Referee Union in the Israel Football Association, the Portuguese Referees Committee and Portugal Football School for supporting this studyinfo:eu-repo/semantics/publishedVersio

Toward the neural implementation of structure learning

Despite significant advances in neuroscience, the neural bases of intelligence remain poorly understood. Arguably the most elusive aspect of intelligence is the ability to make robust inferences that go far beyond one's experience. Animals categorize objects, learn to vocalize and may even estimate causal relationships -all in the face of data that is often ambiguous and sparse. Such inductive leaps are thought to result from the brain's ability to infer latent structure that governs the environment. However, we know little about the neural computations that underlie this ability. Recent advances in developing computational frameworks that can support efficient structure learning and inductive inference may provide insight into the underlying component processes and help pave the path for uncovering their neural implementation

Kepler-20: A Sun-like Star with Three Sub-Neptune Exoplanets and Two Earth-size Candidates

We present the discovery of the Kepler-20 planetary system, which we initially identified through the detection of five distinct periodic transit signals in the Kepler light curve of the host star 2MASSJ19104752+4220194. We find a stellar effective temperature Teff=5455+-100K, a metallicity of [Fe/H]=0.01+-0.04, and a surface gravity of log(g)=4.4+-0.1. Combined with an estimate of the stellar density from the transit light curves we deduce a stellar mass of Mstar=0.912+-0.034 Msun and a stellar radius of Rstar=0.944^{+0.060}_{-0.095} Rsun. For three of the transit signals, our results strongly disfavor the possibility that these result from astrophysical false positives. We conclude that the planetary scenario is more likely than that of an astrophysical false positive by a factor of 2e5 (Kepler-20b), 1e5 (Kepler-20c), and 1.1e3 (Kepler-20d), sufficient to validate these objects as planetary companions. For Kepler-20c and Kepler-20d, the blend scenario is independently disfavored by the achromaticity of the transit: From Spitzer data gathered at 4.5um, we infer a ratio of the planetary to stellar radii of 0.075+-0.015 (Kepler-20c) and 0.065+-0.011 (Kepler-20d), consistent with each of the depths measured in the Kepler optical bandpass. We determine the orbital periods and physical radii of the three confirmed planets to be 3.70d and 1.91^{+0.12}_{-0.21} Rearth for Kepler-20b, 10.85 d and 3.07^{+0.20}_{-0.31} Rearth for Kepelr-20c, and 77.61 d and 2.75^{+0.17}_{-0.30} Rearth for Kepler-20d. From multi-epoch radial velocities, we determine the masses of Kepler-20b and Kepler-20c to be 8.7\+-2.2 Mearth and 16.1+-3.5 Mearth, respectively, and we place an upper limit on the mass of Kepler-20d of 20.1 Mearth (2 sigma).Comment: accepted by ApJ, 58 pages, 12 figures revised Jan 2012 to correct table 2 and clarify planet parameter extractio