| EPSRC Reference: |
EP/W002981/1 |
| Title: |
Turing AI Fellowship: Robust, Efficient and Trustworthy Deep Learning |
| Principal Investigator: |
Torr, Professor PH |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Engineering Science |
| Organisation: |
University of Oxford |
| Scheme: |
EPSRC Fellowship - NHFP |
| Starts: |
01 October 2021 |
Ends: |
30 September 2026 |
Value (£): |
3,087,056
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Image & Vision Computing |
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| EPSRC Industrial Sector Classifications: |
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| Panel History: |
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Summary on Grant Application Form |
The world is currently experiencing an unprecedented era of booming proliferation of machine learning (ML) and artificial intelligence (AI). Undoubtedly, the determining reason behind this rapidly evolving adoption of ML/AI is the embrace of deep neural networks (DNNs). Neural networks had been around for decades, but the advent of faster processing in the form of GPUs and storage enabling huge amounts of "big data" allowed for the training of deeper networks which showed startling performance increases on a variety of tasks in a variety of disciplines. However, the limitations of deep learning are becoming increasingly evident.
Despite deep neural networks performing exceptionally well on a range of metrics, they have also been shown to be vulnerable to adversarial examples. This was first demonstrated in the field of computer vision-certain images are classified incorrectly (often with high confidence), despite there being a minimal perceptual difference with correctly classified inputs.
Adversarial examples have been found in many other applications of deep learning, such as speech understanding, models of code etc. The ease with which these adversarial examples can be found raises doubts about deep neural networks being used in safety-critical applications such as autonomous vehicles or medical diagnosis since the networks could inexplicably classify a natural input incorrectly although it is almost identical to examples it has classified correctly before.
Moreover, it allows for the possibility of malicious agents attacking systems that use neural networks, strikingly, Tencent Keen Security Lab recently demonstrated that the neural network underlying Tesla Autopilot can be fooled by an
adversarially crafted marker on the ground into swerving into the opposite lane.
The Fellowship will create a new Centre of Excellence at Oxford aiming to make deep learning reliable, robust and deployable, creating a new capability within the UK's AI/ML research landscape. The solution will involve developing
fundamental algorithms to make the training more robust, together with algorithms to give an accurate uncertainty calculation for the deep networks estimates. However it is important that the solution also takes into account efficiency. As systems become deployed in the real world in some cases they will be exposed to an ever changing data stream. For instance, as of May 2019, 500 hours of video data is uploaded to YouTube every minute [2], 2.5 quintillion bytes of data are produced by humans every day. In the last two years alone, and most astonishingly, 90 percent of the world's data has been created. The challenge now, however, is to train the (almost) trillion parameters networks with quintillion bytes of data being produced continuously. As we might not want to store all this data and even if we could store it, it might not be computationally possible to train with this amount of data in a single go. Hence this proposal would be incomplete unless we proposed research on uncertainty estimation and robustness in the context of both continual learning and sparsification.
The overarching objective for this Fellowship is to retain Prof Phil Torr within the UK and within academia. His research area of Computer Vision, and in particular deep learning, is of increasing interest to companies, as well as overseas
academic institutions. The prestige and long-term funding of a Turing AI World Leader Researcher Fellowship would not only secure Prof Torr's continued commitment to UK academic research, but would also enable Oxford to build a Centre of Excellence for Robust and Trustworthy Deep Learning around him, and enable him to take a greater leadership role within Oxford, the UK and internationally.
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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.ox.ac.uk |