Fetching the paper…
Reading the bibliography…
Humans learn about the world, and how to act in the world, in many ways: from individually conducting experiments to observing and reproducing others' behavior.
Does biology constrain culture?
A. R. Rogers · 1988
Earlier work this paper cites.
Cultural learning
M. Tomasello, A. C. Kruger, and H. H. Ratner · 1993
Earlier work this paper cites.
Why does culture increase human adaptability?
R. Boyd and P. J. Richerson · 1995
Earlier work this paper cites.
The scientist as child
A. Gopnik · 1996
Earlier work this paper cites.
Understanding and sharing intentions: The origins of cultural cognition
M. Tomasello, M. Carpenter, J. Call, T. Behne, and H. Moll · 2005
Earlier work this paper cites.
Complex systems: Network thinking
M. Mitchell · 2006
Earlier work this paper cites.
Critical social learning: a solution to rogers’s paradox of nonadaptive culture
M. Enquist, K. Eriksson, and S. Ghirlanda · 2007
Earlier work this paper cites.
Using “annotator rationales” to improve machine learning for text categorization
O. Zaidan, J. Eisner, and C. Piatko · 2007
Earlier work this paper cites.
Nudge: Improving decisions about health, wealth, and happiness
R. H. Thaler and C. R. Sunstein · 2009
Earlier work this paper cites.
Why copy others? insights from the social learning strategies tournament
L. Rendell, R. Boyd, D. Cownden, M. Enquist, K. Eriksson, M. W. Feldman, L. Fogarty, S. Ghirlanda, T. Lillicrap, and K. N. Laland · 2010
Earlier work this paper cites.
Cognitive culture: theoretical and empirical insights into social learning strategies
L. Rendell, L. Fogarty, W. J. Hoppitt, T. J. Morgan, M. M. Webster, and K. N. Laland · 2011
Earlier work this paper cites.
Algorithm aversion: People erroneously avoid algorithms after seeing them err
B. J. Dietvorst, J. P. Simmons, and C. Massey · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. e. a. Dean · 2015
Earlier work this paper cites.
The paradox of choice
B. Schwartz · 2015
Earlier work this paper cites.
Deep reinforcement learning from human preferences
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei · 2017
Earlier work this paper cites.
Towards a rigorous science of interpretable machine learning
F. Doshi-Velez and B. Kim · 2017
Earlier work this paper cites.
Grounded language learning in a simulated 3d world
K. M. Hermann, F. Hill, S. Green, F. Wang, R. Faulkner, H. Soyer, D. Szepesvari, W. M. Czarnecki, M. Jaderberg, D. Teplyashin, M. Wainwright, C. Apps, D. Hassabis, and P. Blunsom · 2017
Earlier work this paper cites.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, et al · 2017
Earlier work this paper cites.
Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them
B. J. Dietvorst, J. P. Simmons, and C. Massey · 2018
Earlier work this paper cites.
Social learning strategies: Bridge-building between fields
R. L. Kendal, N. J. Boogert, L. Rendell, K. N. Laland, M. Webster, and P. L. Jones · 2018
Earlier work this paper cites.
Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff
G. Bansal, B. Nushi, E. Kamar, D. S. Weld, W. S. Lasecki, and E. Horvitz · 2019
Earlier work this paper cites.
Darpa’s explainable artificial intelligence (xai) program
D. Gunning and D. Aha · 2019
Earlier work this paper cites.
Ai extenders: the ethical and societal implications of humans cognitively extended by ai
J. Hernández-Orallo and K. Vold · 2019
Earlier work this paper cites.
Algorithm appreciation: People prefer algorithmic to human judgment
J. M. Logg, J. A. Minson, and D. A. Moore · 2019
Earlier work this paper cites.
Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
K. R. Allen, K. A. Smith, and J. B. Tenenbaum · 2020
Earlier work this paper cites.
Dynamic social learning in temporally and spatially variable environments
D. Deffner, V. Kleinow, and R. McElreath · 2020
Earlier work this paper cites.
Understanding human intelligence through human limitations
T. L. Griffiths · 2020
Earlier work this paper cites.
Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources
F. Lieder and T. L. Griffiths · 2020
Earlier work this paper cites.
Flexible learning, rather than inveterate innovation or copying, drives cumulative knowledge gain
E. Miu, N. Gulley, K. N. Laland, and L. Rendell · 2020
Earlier work this paper cites.
Consistent estimators for learning to defer to an expert
H. Mozannar and D. Sontag · 2020
Earlier work this paper cites.
The child as hacker
J. S. Rule, J. B. Tenenbaum, and S. T. Piantadosi · 2020
Earlier work this paper cites.
Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
U. Bhatt, J. Antorán, Y. Zhang, Q. V. Liao, and P. e. a. Sattigeri · 2021
Earlier work this paper cites.
To trust or to think: cognitive forcing functions can reduce overreliance on ai in ai-assisted decision-making
Z. Buçinca, M. B. Malaya, and K. Z. Gajos · 2021
Cited alongside, same era.
Expanding explainability: Towards social transparency in ai systems
U. Ehsan, Q. V. Liao, M. Muller, M. O. Riedl, and J. D. Weisz · 2021
Cited alongside, same era.
Algorithmic monoculture and social welfare
J. Kleinberg and M. Raghavan · 2021
Cited alongside, same era.
Ai standardisation landscape: state of play and link to the ec proposal for an ai regulatory framework
N. S and D. N. S · 2021
Cited alongside, same era.
Language models as agent models
J. Andreas · 2022
Cited alongside, same era.
Eliciting and learning with soft labels from every annotator
K. M. Collins, U. Bhatt, and A. Weller · 2022
Cited alongside, same era.
The unequal opportunities of large language models: Examining demographic biases in job recommendations by chatgpt and llama
A. Salinas, P. Shah, Y. Huang, R. McCormack, and F. Morstatter · 2023
Later among the works it cites.
L. Wong, G. Grand, A. K. Lew, N. D. Goodman, and V. K. e. a. Mansinghka · 2023
Later among the works it cites.
Data-centric artificial intelligence: A survey
D. Zha, Z. P. Bhat, K.-H. Lai, F. Yang, Z. Jiang, S. Zhong, and X. Hu · 2023
Later among the works it cites.
Voluntary AI Safety Standard: The 10 guardrails, 2024
S. Australia’s Department of Industry and Resources · 2024
Later among the works it cites.
When Should Algorithms Resign? A Proposal for AI Governance
U. Bhatt and H. Sargeant · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
When does selection favor learning from the old? social learning in age-structured populations
D. Deffner and R. McElreath · 2022
Cited alongside, same era.
Towards continual reinforcement learning: A review and perspectives
K. Khetarpal, M. Riemer, I. Rish, and D. Precup · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, and C. e. a. Wainwright · 2022
Cited alongside, same era.
What makes inventions become traditions?
S. E. Perry, A. Carter, J. G. Foster, S. Nöbel, and M. Smolla · 2022
Cited alongside, same era.
Deskilling, upskilling, and reskilling: a case for hybrid intelligence
J. Rafner, D. Dellermann, A. Hjorth, D. Verasztó, C. Kampf, W. Mackay, and J. Sherson · 2022
Cited alongside, same era.
Bayesian modeling of human–ai complementarity
M. Steyvers, H. Tejeda, G. Kerrigan, and P. Smyth · 2022
Cited alongside, same era.
A hierarchical bayesian model of adaptive teaching
A. M. Chen, A. Palacci, N. Vélez, R. D. Hawkins, and S. J. Gershman · 2024
Later among the works it cites.
Modulating language model experiences through frictions
K. M. Collins, V. Chen, I. Sucholutsky, H. R. Kirk, M. Sadek, H. Sargeant, A. Talwalkar, A. Weller, and U. Bhatt · 2024
Later among the works it cites.
Towards guaranteed safe ai: A framework for ensuring robust and reliable ai systems
D. Dalrymple, J. Skalse, Y. Bengio, S. Russell, M. Tegmark, S. Seshia, S. Omohundro, C. Szegedy, B. Goldhaber, N. Ammann, et al · 2024
Later among the works it cites.
The ethics of advanced ai assistants
I. Gabriel, A. Manzini, G. Keeling, L. A. Hendricks, and V. e. a. Rieser · 2024
Later among the works it cites.
How human–ai feedback loops alter human perceptual, emotional and social judgements
M. Glickman and T. Sharot · 2024
Later among the works it cites.
A decision theoretic framework for measuring ai reliance
Z. Guo, Y. Wu, J. D. Hartline, and J. Hullman · 2024
Later among the works it cites.
Simple and scalable strategies to continually pre-train large language models
A. Ibrahim, B. Thérien, K. Gupta, M. L. Richter, Q. G. Anthony, E. Belilovsky, T. Lesort, and I. Rish · 2024
Later among the works it cites.
Elements of world knowledge (ewok): A cognition-inspired framework for evaluating basic world knowledge in language models
A. A. Ivanova, A. Sathe, B. Lipkin, U. Kumar, S. Radkani, T. H. Clark, C. Kauf, J. Hu, R. Pramod, G. Grand, et al · 2024
Later among the works it cites.
A. Jaech, A. Kalai, A. Lerer, A. Richardson, A. El-Kishky, A. Low, A. Helyar, A. Madry, A. Beutel, A. Carney, et al · 2024
Later among the works it cites.
The PRISM alignment dataset: What participatory, representative and individualised human feedback reveals about the subjective and multicultural alignment of large language models
H. R. Kirk, A. Whitefield, P. Röttger, A. M. Bean, K. Margatina, R. Mosquera, J. M. Ciro, M. Bartolo, A. Williams, H. He, B. Vidgen, and S. A. Hale · 2024
Later among the works it cites.
The inversion problem: Why algorithms should infer mental state and not just predict behavior
J. Kleinberg, J. Ludwig, S. Mullainathan, and M. Raghavan · 2024
Later among the works it cites.
Decoding ai’s nudge: A unified framework to predict human behavior in ai-assisted decision making
Z. Li, Z. Lu, and M. Yin · 2024
Later among the works it cites.
Large language models assume people are more rational than we really are
R. Liu, J. Geng, J. C. Peterson, I. Sucholutsky, and T. L. Griffiths · 2024
Later among the works it cites.
Artificial intelligence and illusions of understanding in scientific research
L. Messeri and M. Crockett · 2024
Later among the works it cites.
Loafing in the era of generative ai
S. Saluja, S. Sinha, and S. Goel · 2024
Later among the works it cites.
Ai models collapse when trained on recursively generated data
I. Shumailov, Z. Shumaylov, Y. Zhao, N. Papernot, R. Anderson, and Y. Gal · 2024
Later among the works it cites.
Scaling llm test-time compute optimally can be more effective than scaling model parameters
C. Snell, J. Lee, K. Xu, and A. Kumar · 2024
Later among the works it cites.
Three challenges for ai-assisted decision-making
M. Steyvers and A. Kumar · 2024
Later among the works it cites.
Representational alignment supports effective machine teaching
I. Sucholutsky, K. M. Collins, M. Malaviya, N. Jacoby, W. Liu, T. R. Sumers, M. Korakakis, U. Bhatt, M. Ho, J. B. Tenenbaum, et al · 2024
Later among the works it cites.
Accuracy-time tradeoffs in ai-assisted decision making under time pressure
S. Swaroop, Z. Buçinca, K. Z. Gajos, and F. Doshi-Velez · 2024
Later among the works it cites.
Ai can help humans find common ground in democratic deliberation
M. H. Tessler, M. A. Bakker, D. Jarrett, H. Sheahan, M. J. Chadwick, R. Koster, G. Evans, L. Campbell-Gillingham, T. Collins, D. C. Parkes, et al · 2024
Later among the works it cites.
When combinations of humans and ai are useful: A systematic review and meta-analysis
M. Vaccaro, A. Almaatouq, and T. Malone · 2024
Later among the works it cites.
Group coordination catalyzes individual and cultural intelligence
C. M. Wu, R. Dale, and R. D. Hawkins · 2024
Later among the works it cites.
It takes two to think
I. Yanai and M. J. Lercher · 2024
Later among the works it cites.
From task structures to world models: what do llms know?
I. Yildirim and L. Paul · 2024
Later among the works it cites.
On the informativeness of supervision signals
I. Sucholutsky, R. M. Battleday, K. M. Collins, R. Marjieh, J. Peterson, P. Singh, U. Bhatt, N. Jacoby, A. Weller, and T. L. Griffiths · 2046
Closest in time.