Abstract
The chapter looks into the possible developments of Artificial Intelligence (AI) in the near future and identifies two likely trends: (a) a shift from historical to synthetic data; and (b) a translation of difficult tasks (in terms of abilities) into complex ones (in terms of computation). It is argued that (a) and (b) will be pursued as development strategies of AI solutions whenever and as far as they are feasible.
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Notes
- 1.
For a reassuringly converging review based not on the nature of data or the nature of problems, but rather on the nature of technological solutions, based on a large scale review of the fortcoming literture on AI, see “We analyzed 16,625 papers to figure out where AI is headed next” https://www.technologyreview.com/s/612768/we-analyzed-16625-papers-to-figure-out-where-ai-is-headed-next/ (accessed 16 June 2019).
- 2.
https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ (accessed 16 June 2019).
- 3.
Masking is any information process used to conceal (hide or “mask”) sensititive or private information, e.g. by replacing or making unavailable part of the relevant data https://www.tcs.com/blogs/the-masking-vs-synthetic-data-debate (accessed 16 June 2019).
- 4.
https://deepmind.com/blog/alphazero-shedding-new-light-grand-games-chess-shogi-and-go/ (accessed 16 June 2019).
- 5.
One may argue that the data generated by AlphaZero are as synthetic as the data that would be generated by a human player playing against herself. This is correct—this is the idea behing the very process of synthesising anything from anything else—but also irrelevant here. Syntehtic data is used to stress the fact that the data avaialbel to the AI system are generated (mind, not collected, because the colleciotn could be of data generated by humans, for example) entirely by the AI system.
- 6.
Put more epistemologically, with synthetic data AI enjoys the privileged position of a maker’s knowledge, who knows the intrinsic nature and working of something because it made that something (Floridi 2018).
- 7.
https://securityintelligence.com/generative-adversarial-networks-and-cybersecurity-part-1/ (accessed 16 June 2019).
- 8.
- 9.
See for example Microsoft’s “Project InnerEye—Medical Imaging AI to Empower Clinicians”, https://www.microsoft.com/en-us/research/project/medical-image-analysis/ (accessed 16 June 2019).
- 10.
https://qz.com/966882/robots-cant-lace-shoes-so-sneaker-production-cant-be-fully-automated-just-yet/ (accessed 16 June 2019).
- 11.
I am not the first to make this point, see for example: https://www.campaignlive.co.uk/article/hard-things-easy-easy-things-hard/1498154 (accessed 16 June 2019).
- 12.
http://www.moley.com/ (accessed 16 June 2019).
- 13.
https://misorobotics.com/ (accessed 16 June 2019).
- 14.
Strange things happen when the software does not work properly: https://www.bbc.co.uk/news/business-47336684 (accessed 16 June 2019).
- 15.
https://helloeffie.com/ (accessed 16 June 2019).
- 16.
https://foldimate.com/ (accessed 16 June 2019).
- 17.
I would like to thank all members of the Digital Ethics Lab, OII, Univeristy of Oxford, for many discussions about some of the topics covered in this article, Nikita Aggarwal, Josh Cowls, Jessica Morley, David Sutcliffe, and Mariarosaria Taddeo for their hugely helpful coments on several drafts, and the editors of the volume, Christoper Burr and Silvia Milano, for their constructive feedback on the last version.
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Funding
This research was partly supported, at different stages, by Privacy and Trust Stream—Social lead of the PETRAS Internet of Things research hub (PETRAS is funded by the Engineering and Physical Sciences Research Council (EPSRC), grant agreement no. EP/N023013/1), Facebook, Google, and Microsoft.
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Floridi, L. (2020). What the Near Future of Artificial Intelligence Could Be. In: Burr, C., Milano, S. (eds) The 2019 Yearbook of the Digital Ethics Lab. Digital Ethics Lab Yearbook. Springer, Cham. https://doi.org/10.1007/978-3-030-29145-7_9
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