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The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML.
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CVAP: validation for cluster analyses
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Learning hmm-based cognitive load models for supporting human-agent teamwork
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Matching networks for one shot learning, in: Advances in neural information processing systems, pp. 3630–3638
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Humans, but not deep neural networks, often miss giant targets in scenes
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An end-to-end process model for supervised machine learning classification: from problem to deployment in information systems, in: Designing the Digital Transformation: DESRIST 2017 Research in Progress Proceedings of the 12th International Conference on Design Science Research in Information Systems and Technology. Karlsruhe, Germany. 30 May-1 Jun., Karlsruher Institut für Technologie (KIT). pp. 55–63
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Celebration of twenty years promoting cognitive science
Kogler, J.E., Pessoa, O.F., 2017 · 2017
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Building machines that learn and think like people
Lake, B.M., Ullman, T.D., Tenenbaum, J.B., Gershman, S.J., 2017 · 2017
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The forthcoming artificial intelligence (ai) revolution: its impact on society and firms
Makridakis, S., 2017 · 2017
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Active and semi-supervised learning for object detection with imperfect data
Rhee, P.K., Erdenee, E., Kyun, S.D., Ahmed, M.U., Jin, S., 2017 · 2017
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Cognitive load and issue engagement in congressional discourse
Shaffer, R., 2017 · 2017
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Computational models of ethical decision-making: A coherence-driven reflective equilibrium model
Yilmaz, L., Franco-Watkins, A., Kroecker, T.S., 2017 · 2017
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An analysis of the interaction between intelligent software agents and human users
Burr, C., Cristianini, N., Ladyman, J., 2018 · 2018
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Investigating human priors for playing video games, in: International Conference on Machine Learning, pp. 1348–1356
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Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner
Dupoux, E., 2018 · 2018
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Adversarial examples that fool both computer vision and time-limited humans, in: Advances in Neural Information Processing Systems, pp. 3910–3920
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When will ai exceed human performance? evidence from ai experts
Grace, K., Salvatier, J., Dafoe, A., Zhang, B., Evans, O., 2018 · 2018
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The knowledge level in cognitive architectures: Current limitations and possible developments
Lieto, A., Lebiere, C., Oltramari, A., 2018 · 2018
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The algebraic mind: Integrating connectionism and cognitive science
Marcus, G.F., 2018 · 2018
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Evaluating (and improving) the correspondence between deep neural networks and human representations
Peterson, J.C., Abbott, J.T., Griffiths, T.L., 2018 · 2018
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Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
Rajalingham, R., Issa, E.B., Bashivan, P., Kar, K., Schmidt, K., DiCarlo, J.J., 2018 · 2018
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Human-extended machine cognition
Smart, P.R., 2018 · 2018
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Challenges in the deployment and operation of machine learning in practice
Baier, L., Jöhren, F., Seebacher, S., 2019 · 2019
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Hybrid intelligence
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On simulating one-trial learning using morphological neural networks
Feng, N., Sun, B., 2019 · 2019
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The minimum intelligent signal test (mist) as an alternative to the turing test
Łupkowski, P., Jurowska, P., 2019 · 2019
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Machines as teammates: a research agenda on ai in team collaboration
Seeber, I., Bittner, E., Briggs, R.O., de Vreede, T., de Vreede, G.J., Elkins, A., Maier, R., Merz, A., Oeste-Reiß, S., Randrup, N., et al., 2019 · 2019
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Development of computational models of emotions: A software engineering perspective
Osuna, E., Rodríguez, L.F., Gutierrez-Garcia, J.O., Castro, L.A., 2020 · 2020
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