Abstract
New types of artificial intelligence (AI), from cognitive assistants to social robots, are challenging meaningful comparison with other kinds of intelligence. How can such intelligent systems be catalogued, evaluated, and contrasted, with representations and projections that offer meaningful insights? To catalyse the research in AI and the future of cognition, we present the motivation, requirements and possibilities for an atlas of intelligence: an integrated framework and collaborative open repository for collecting and exhibiting information of all kinds of intelligence, including humans, non-human animals, AI systems, hybrids and collectives thereof. After presenting this initiative, we review related efforts and present the requirements of such a framework. We survey existing visualisations and representations, and discuss which criteria of inclusion should be used to configure an atlas of intelligence.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
A detailed analysis of the questionnaire can be found in (Bhatnagar et al. 2017).
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
- 13.
- 14.
- 15.
- 16.
- 17.
- 18.
References
Aarts, A., Anderson, J., Anderson, C., Attridge, P., Attwood, A., Fedor, A.: Estimating the reproducibility of psychological science. Science 349(6251), 1–8 (2015)
Allen Institute for Brain Science: Allen brain observatory (2016). http://observatory.brain-map.org/visualcoding
Arsiwalla, X.D., Moulin-Frier, C., Herreros, I., Sanchez-Fibla, M., Verschure, P.: The morphospace of consciousness. arXiv preprint arXiv:1705.11190 (2017)
Balakhonov, D., Rose, J.: Crows rival monkeys in cognitive capacity. Sci. Rep. 7(1), 8809 (2017)
Bhatnagar, S., et al.: A First Survey on an Atlas of Intelligence (2017). http://www.dsic.upv.es/~flip/papers/Bhatnagar18_SurveyAtlas.pdf
Boero, F., Bernardi, G.: Phenotypic vs genotypic approaches to biodiversity, from conict to alliance. Mar. Genomics 17, 63–64 (2014)
Brooks, R.A.: From earwigs to humans. Robot. Auton. Syst. 20(2–4), 291–304 (1997)
Cadman, E.: AI not just a game for DeepMind’s Demis Hassabis. Financial Times (2014). https://www.ft.com/content/1c9d5410-8739
Castelvecchi, D.: Tech giants open virtual worlds to bevy of AI programs. Nature 540(7633), 323–324 (2016)
Cattell, R.B., Coulter, M.A.: Principles of behavioural taxonomy and the mathematical basis of the taxonome computer program. Br. J. Math. Stat. Psychol. 19(2), 237–269 (1966)
Dennett, D.C.: Darwin’s dangerous idea. Sciences 35(3), 34–40 (1995)
de Valadés, D.: Rhetorica christiana (1579)
Eckersley, P., Nasser, Y., et al.: EFF AI Progress Measurement Project (2017). https://www.eff.org/es/ai/metrics. Accessed 10 Jan 2017
Einav, L., Levin, J.: Economics in the age of big data. Science 346(6210), 1243089 (2014)
Frey, C.B., Osborne, M.A.: The future of employment: how susceptible are jobs to computerisation? Technol. Forecast. Soc. Chang. 114, 254–280 (2017)
Gentner, D.: Psychology in cognitive science: 1978–2038. Top. Cogn. Sci. 2(3), 328–344 (2010)
Gewin, V.: Taxonomy: all living things, online. Nature 418(6896), 362–363 (2002)
Godfrey-Smith, P.: Towers and trees in cognitive evolution. In: Huebner, B. (ed.) The Philosophy of Daniel Dennett (Chap. 8.1). Oxford University Press (2015)
Gray, H.M., Gray, K., Wegner, D.M.: Dimensions of mind perception. Science 315(5812), 619–619 (2007)
Hayles, N.K.: Narratives of artificial life. In: Future Natural: Nature, Science, Culture, pp. 146–164. Routledge, London (1996)
Hernández-Orallo, J., et al.: A new AI evaluation cosmos: ready to play the game? AI Mag. 38(3), 66–69 (2017). https://www.aaai.org/ojs/index.php/aimagazine/article/view/2748
Hernández-Orallo, J.: The Measure of All Minds: Evaluating Natural and Artificial Intelligence. Cambridge University Press, Cambridge (2017)
Herrmann, E., Call, J., Hernández-Lloreda, M.V., Hare, B., Tomasello, M.: Humans have evolved specialized skills of social cognition: the cultural intelligence hypothesis. Science 317(5843), 1360–1366 (2007)
Hessel, M., et al.: Rainbow: combining improvements in deep reinforcement learning. arXiv preprint arXiv:1710.02298 (2017)
Kirsh, D.: Today the earwig, tomorrow man? Artif. Intell. 47(1–3), 161–184 (1991)
Landhuis, E.: Neuroscience: big brain, big data. Nature 541(7638), 559–561 (2017)
Leslie, M.: Calling all taxonomists. Science 307(5712), 1021–1022 (2005)
Lowndes, J.S.S., Best, B.D., Scarborough, C., Afflerbach, J.C., Frazier, M.R., O’Hara, C.C., Halpern, B.S.: Our path to better science in less time using open data science tools. Nat. Ecol. Evol. 1, 0160 (2017)
Macphail, E.M.: The comparative psychology of intelligence. Behav. Brain Sci. 10(4), 645–656 (1987)
Marcus, G.: Deep learning: a critical appraisal. arXiv preprint arXiv:1801.00631 (2018)
Marx, V.: Biology: the big challenges of big data. Nature 498(7453), 255–260 (2013)
McKinlay, R.: Use or lose our navigation skills: automatic wayfinding is eroding natural abilities, warns roger mckinlay. Nature 531(7596), 573–576 (2016)
Midford, P.E.: Ontologies for behavior. Bioinformatics 20(18), 3700–3701 (2004)
Miller, R.: Task taxonomy: science or technology? Ergonomics 10(2), 167–176 (1967)
Milner, G.: Pinpoint: How GPS is Changing Technology, Culture, and Our Minds. WW Norton & Company (2016)
Moravec, H.: When will computer hardware match the human brain. J. Evol. Technol. 1(1), 10 (1998)
National Research Council: Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. National Academies Press (2011)
Ng, A.: What artificial intelligence can and can’t do right now. Harvard Business Review, November 2016
Parr, C.S., et al.: The encyclopedia of life v2: Providing global access to knowledge about life on earth, vol. 2 (2014). http://www.eol.org/
Pfeifer, R.: Embodied artificial intelligence 10 years back, 10 years forward. In: Informatics, pp. 294–310 (2001)
Pinker, S.: The Language Instinct: How the Mind Creates Language. William Morrow and Company (1994)
Roskov, Y., et al.: Species 2000 ITIS catalogue of life. Aeon (2018). http://catalogueoflife.org/col
Schaie, K.W.: Intellectual development in adulthood. In: Birren, J.E., Schaie, K.W. (eds.) Handbook of the Psychology of Aging, vol. 4, pp. 266–286. Academic Press Inc. (1996)
Shah, H., Warwick, K., Vallverdú, J., Wu, D.: Can machines talk? Comparison of eliza with modern dialogue systems. Comput. Hum. Behav. 58, 278–295 (2016)
Shanahan, M.: Conscious exotica. from algorithms to aliens, could humans ever understand minds that are radically unlike our own? Aeon (2016). https://aeon.co/essays/beyond-humans-what-other-kinds-of-minds-might-be-out-there
Shead, S.: Facebook’s AI boss: in terms of general intelligence, we’re not even close to a rat. Business Insider (2017). http://uk.businessinsider.com/facebooks-ai-boss-in-terms-of-general-intelligence-were-not-even-close-to-a-rat-2017-10
Silver, D., et al.: Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv preprint arXiv:1712.01815 (2017)
Sloman, A.: The Structure and Space of Possible Minds. University of Sussex, School of Cognitive Sciences (1984)
Solé, R.: Rise of the humanbot. arXiv preprint arXiv:1705.05935 (2017)
Solé, R.V., Macia, J.: Synthetic biocomputation: the possible and the actual. In: ECAL, pp. 29–36 (2011)
Sparrow, B., Liu, J., Wegner, D.M.: Google effects on memory: cognitive consequences of having information at our fingertips. Science, p. 1207745 (2011)
Stuart, S., Wilson, E., McNeely, J., Mittermeier, R., Rodríguez, J.: The barometer of life. Science 328(5975), 177–177 (2010)
Tegmark, M.: Life 3.0: Being human in the age of artificial intelligence. Knopf (2017)
Thagard, P.: Why cognitive science needs philosophy and vice versa. Top. Cogn. Sci. 1(2), 237–254 (2009)
Vanschoren, J., et al.: Towards a data science collaboratory. Lecture Notes in Computer Science (IDA 2015), vol. 9385 (2015)
Vanschoren, J., van Rijn, J.N., Bischl, B., Torgo, L.: OpenML: networked science in machine learning. SIGKDD Explor. 15(2), 49–60 (2013). https://doi.org/10.1145/2641190.2641198
Wegner, D.M., Gray, K.: The Mind Club: Who Thinks, What Feels, and Why It Matters. Penguin (2017)
Winfield, A.F.: Estimating the energy cost of (artificial) evolution. In: Sayama, H., Rieffel, J., Risi, S., Doursat, R., Lipson, H. (eds.) Proceedings of 14th International Conference on the Synthesis and Simulation of Living Systems (ALife), pp. 872–875. MIT Press (2014)
Winfield, A.F.: How intelligent is your intelligent robot? arXiv preprint arXiv:1712.08878 (2017)
Yampolskiy, R.V.: The universe of minds. arXiv preprint arXiv:1410.0369 (2014)
Acknowledgements
The initiative was supported by the Leverhulme Trust via the Leverhulme Centre for the Future of Intelligence. J. H-Orallo and F. M-Plumed were supported by EU (FEDER) and the Spanish MINECO under grant TIN 2015-69175-C4-1-R and by GVA under grant PROMETEOII/ 2015/013 and by the Air Force Office of Scientific Research under award number FA9550-17-1-0287. J. H-Orallo also received a Salvador de Madariaga grant (PRX17/00467) from the Spanish MECD for a research stay at the CFI, Cambridge, and a BEST grant (BEST/2017/045) from the GVA for another research stay also at the CFI. F. M-Plumed was also supported by INCIBE (Ayudas para la excelencia de los equipos de investigación avanzada en ciberseguridad). A. Weller acknowledges support from the David MacKay Newton research fellowship at Darwin College, the Alan Turing Institute under EPSRC grant EP/N510129/1 & TU/B/000074, and the Leverhulme Trust via the CFI.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
A Appendix: Why is an atlas needed? Similar initiatives
A Appendix: Why is an atlas needed? Similar initiatives
While identifying the need for an atlas, we look at how it fits in cognitive science as a whole and also whether there are initiatives in other fields that could be inspirational.
Regarding cognitive science, it is true that its goal is to cover all possible cognitive systems, understand their behaviour and mechanisms, and establish meaningful comparisons. However, the field has not yet been able to portray a systematic representation covering both natural and artificial systems. But if we do not find this systematic representation in cognitive science, do we find it in related subdisciplines? The answer is that some similar initiatives in other disciplines do existFootnote 2:
-
Life forms: Examples are Wikispecies (Leslie 2005), the All Species Foundation (Gewin 2002), the Catalogue of Life and the Encyclopedia of Life (Roskov et al. 2018; Hayles 1996; Parr et al. 2014; Stuart et al. 2010).
-
Neuroscience: the Cognitive AtlasFootnote 3 and related repositories for neuroscienceFootnote 4 include an ontology of human cognitive functions and related tasks, and the pathologies affected. The Allen brain observatoryFootnote 5 (Allen Institute for Brain Science 2016) is a more visually-oriented platform that maps perception and cognition to parts of the human brain (National Research Council 2011).
-
Psychometrics: There are several initiatives bringing together test batteries and repositories: the mental Measurement yearbookFootnote 6, and with a more open character, the International Personality Item PoolFootnote 7 and the International Cognitive Ability ResourceFootnote 8.
-
Machine learning and data science research: KaggleFootnote 9, OpenMLFootnote 10 (Vanschoren et al. 2013) and many other platforms (e.g., gitxiv.com) provide benchmarks for ML. OpenML also includes experimental results that can be compared, aggregated and represented with powerful analytical packages.
-
Artificial intelligence: there are many collections of benchmarks and associated results, such as ALEFootnote 11, OpenAI universe/gymFootnote 12, Microsoft MalmoFootnote 13, Facebook’s CommAI-envFootnote 14, DeepMind LabFootnote 15 (see Hernández-Orallo et al. 2017 for a summary) and meta-views, such as a recent EFF analysisFootnote 16 and the AI index reportFootnote 17. This is a sign that AI is looking in this direction (Castelvecchi 2016; Hernández-Orallo 2017). The tasks are rarely arranged into abilities and the data usually compares specialised AI systems against average humans.
A partially overlapping initiative is the AI Roadmap InstituteFootnote 18, which encourages, compares and studies various AI and general AI roadmaps. It focuses on the future and on AI primarily, with representations that are usually flowcharts and pathway comparisons. Besides identifying where the field of AI stands as a whole, it also aims to identify dead-ends and open research problems on the path to the development of general AI systems.
The data and conceptual framing of the above projects can be used to inform an atlas of intelligence. Still, no repositories or taxonomies exist focusing mostly on behaviour, encompassing natural and artificial systems, as we are undertaking. Of course, the fact that something does not exist yet is not a sufficient reason that it should. The need for an atlas has to be supported by a series of motivations and applications, which we do in Sect. 2.
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Bhatnagar, S. et al. (2018). Mapping Intelligence: Requirements and Possibilities. In: Müller, V. (eds) Philosophy and Theory of Artificial Intelligence 2017. PT-AI 2017. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-319-96448-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-96448-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96447-8
Online ISBN: 978-3-319-96448-5
eBook Packages: Computer ScienceComputer Science (R0)