| EPSRC Reference: |
EP/R030952/1 |
| Title: |
Sleep cycling for Probabilistic Generative Models |
| Principal Investigator: |
van Rossum, Professor M |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Sch of Psychology |
| Organisation: |
University of Nottingham |
| Scheme: |
Standard Research - NR1 |
| Starts: |
01 March 2018 |
Ends: |
31 January 2020 |
Value (£): |
142,948
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| EPSRC Research Topic Classifications: |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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31 Oct 2017
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Human-like Computing Interviews
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Announced
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Summary on Grant Application Form |
The media have lately been full of excitement about progress in Artificial Intelligence. Not only can computers now beat humans at Go, and detect cats in Youtube videos, but soon we will have robots in the house, self-driving cars, and many jobs might become automated. Apart from the societal challenges that this revolution will bring, many hurdles are still to be overcome before Artificial Intelligence will obtain truly human-like capabilities.
In particular, current artificial systems might be very good at specific tasks, they cannot easily apply their processing power to other problems. Moreover, in order to become experts in a certain problem these machines often need millions of training examples. Current AI systems follow a strategy very different from humans and obtain their strength from brute compute power and massive amounts of data rather than by cleverness. This is also the reason why it is hard to communicate with these machines, understand their decisions and instruct them. The fact that computers use an approach that is so different from that used by humans seriously hinders application of AI to real world applications. The research community is well aware of these issues, and it generally believed that the problem arises because machines don't construct higher level understanding of the problems that they are solving. How this should be addressed is however not clear.
In humans and animals sleep plays an important role in creating high level representations. During sleep, the brain consolidates information, rearranges it, finds links between different types of knowledge, reformulates problems, and comes up with creative solutions. Most people have experienced this at some point - as they feel better able to solve a problem after a good night of sleep. It is only very recently that researchers have become able to manipulate the processes that go on during sleep, and thereby pick apart the roles of the various sleep phases play and the reason why the sleep phases are ordered in a particular way.
Here we propose to research how to processes occurring during sleep can be mimicked in computational models, and thereby open the possibility to build more human-like artificial systems.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.nottingham.ac.uk |