Tessler, M.H., Goodman, N.D., & Frank, M.C. (2017). Avoiding frostbite: It helps to learn fromothers. Commentary on Lake et al., Building machines that learn and think like people.
Behavioral and Brain Sciences, 40, E279. doi:10.1017/S0140525X17000280
Abstract
Machines that learn and think like people must be able to learn from others. Social learning
speeds up the learning process and – in combination with language – is a gateway to abstractand unobservable information. Social learning also facilitates the accumulation of knowledge
across generations, helping people and AIs learn things that no individual could learn in alifetime.
Main text
Causality, compositionality, and learning-to-learn – the future goals for artificial intelligencearticulated by Lake et al. – are central for human learning. But these abilities alone would not beenough to avoid frostbite on King William Island in the Arctic Archipelago. You need to know
how to hunt seals, make skin clothing, and manage dog sleds, and these skills are not easy toacquire from the environment alone. But if the Netsilik Inuit people taught them to you, yourchances of surviving a winter would be dramatically improved (Lambert, 2011). Similar to ahuman explorer, an AI learning to play videogames like Frostbite should take advantage of therich knowledge available from other people. Access to this knowledge requires the capacity forsocial learning, both a critical prerequisite for language use and a gateway in itself to cumulative
cultural knowledge.
Learning from other people helps you learn with less data. In particular, humans learn effectively
even from “small data” because the social context surrounding the data is itself informative.
Dramatically different inferences can result from what is ostensibly the same data in distinctsocial contexts or even with alternative assumptions about the same context (Shafto, Goodman,and Frank, 2012). The flexibility of the social inference machinery in humans turns small signals
into weighty observations: even for young children, ambiguous word-learning events become
informative through social reasoning (Frank & Goodman, 2014), non-obvious causal action
sequences become “the way you do it” when presented pedagogically (Buchsbaum et al.,2011); and complex machines can become single-function tools when a learner is taught justone function (Bonawitz et al., 2011).
Learning from others comes in many forms. An expert may tolerate onlookers, a demonstrator
may slow down when completing a particularly challenging part of the task, and a teacher may
actively provide pedagogical examples and describe them with language (Kline, 2014; Csibra &Gergely, 2009). Informative demonstrations may be particularly useful for procedural learning(e.g., hunting seals, learning to play Frostbite). Language, however, is uniquely powerful in its
ability to convey information that is abstract or difficult to observe, or information that otherwise
does not have a way of being safely acquired (e.g., learning that certain plants are poisonous,
how to avoid frostbite; Gelman, 2009). Studying social learning is an important part of studying
language learning (Goodman & Frank, 2016); both should be top priorities for making AIs learn
like people.

Figure 1. Score trajectories for players in the game Frostbite over time. The two panels depict
results with and without instructions about the abstract structure of the game.
Focusing on Lake et al.’s key example, you can even learn the game Frostbite with less datawhen you learn it from other people. We recruited 20 participants from Amazon’s Mechanical
Turk to play Frostbite for 5 minutes. Half of the participants were given written instructions about
the abstract content of the game, adapted directly from the caption of Figure 2 in the target
article. The other half were not given this information. (Everybody was told that you move theagent using the arrow keys.) Learners who were told about the abstract structure of the gamelearned to play the game more quickly and achieved higher overall scores (M=2440) than thegroup without written instructions (M=1333; Figure 1). The highest score for those without
linguistic instructions was 3530 points, achieved after about 4 minutes of play. By comparison,
the highest score achieved with linguistic instructions was 7720 points, achieved after 2 minutesof play. Indeed, another group (including some authors of the target article) recently found asimilar pattern of increased performance in Frostbite due to social guidance (Tsividis et al.,2017).
Learning from others also does more than simply “speed up” learning about the world. Humanknowledge seems to accumulate across generations, thus permitting progeny to learn in one
lifetime what no generation before them could learn (Tomasello, 1999; Boyd et al., 2011). We
hypothesize that language – and its flexibility to refer to abstract concepts – is key to faithfultransmission of knowledge, between individuals and through generations. Human intelligence isso difficult to match because we stand on the shoulders of giants. AIs need to “ratchet” up theirown learning, by communicating knowledge efficiently within and across generations. Rather
than be subject to a top-down hive mind, intelligent agents should retain their individual
intellectual autonomy and innovate new solutions to problems based on their own experienceand what they’ve learned from others. The important discoveries of a single AI could then beshared, and we believe language is the key to this kind of cultural transmission. Cultural
knowledge could then accumulate within both AI and human networks.

In sum, learning from other people should be a high priority for AI researchers. Lake et al. hope
to set priorities for future research in AI but fail to acknowledge the importance of learning fromlanguage and social cognition. This is a mistake: The more complex the task is, the morelearning to perform like a human involves learning from other people.

References
Bonawitz, E., Shafto, P., Gweon, H., Goodman, N. D., Spelke, E., & Schulz, L. (2011). The
double-edged sword of pedagogy: Instruction limits spontaneous exploration anddiscovery. Cognition, 120(3), 322–330. http://doi.org/10.1016/j.cognition.2010.10.001
Boyd, R., Richerson, P. J., & Henrich, J. (2011). The cultural niche: why social learning isessential for human adaptation. Proceedings of the National Academy of Sciences of theUnited States of America, 10918–10925.
Buchsbaum, D., Gopnik, A., Griffiths, T. L., & Shafto, P. (2011). Children’s imitation of causal
action sequences is influenced by statistical and pedagogical evidence. Cognition,
120(3), 331–340. http://doi.org/10.1016/j.cognition.2010.12.001
Csibra, G., & Gergely, G. (2009). Natural pedagogy. Trends in Cognitive Sciences, 13(4), 148–153.
Frank, M. C., & Goodman, N. D. (2014). Inferring word meanings by assuming that speakers
are informative. Cognitive Psychology, 75, 80–96.
Gelman, S. A. (2009). Learning from others: Children’s construction of concepts. Annual Reviewof Psychology, 60, 115–140.
Goodman, N. D., & Frank, M. C. (2016). Pragmatic language interpretation as probabilisticinference. Trends in Cognitive Sciences.
Kline, M. A. (2014). How to learn about teaching: An evolutionary framework for the study ofteaching behavior in humans and other animals. Behavioral and Brain Sciences, 1–70.
Lambert A (2011) The Gates of Hell: Sir John Franklin’s Tragic Quest for the North West
Passage (Yale Univ Press, New Haven, CT).
Shafto, P., Goodman, N. D., & Frank, M. C. (2012). Learning From Others: The Consequencesof Psychological Reasoning for Human Learning. Perspectives on Psychological
Science, 7(4), 341–351.
Tomasello, M. (1999). The cultural origins of human cognition. Cambridge, MA: Harvard
University Press.
Tsividis, P.A., Pouncy, T., Xu, J.L., Tenenbaum, J.B., & Gershman, S.J. (2017). Human learningin Atari. AAAI Spring Symposium on Science of Intelligence: Computational Principles ofNatural and Artificial Intelligence.