Tata Steel Defect Detection Project

The project will explore the use of active learning frameworks in the training of a defect classification system for identifying surface defects observed from imaging systems. The questions to explore will include whether a human-in-the-loop system can be used to utilise domain expert knowledge and iterative machine learning to produce a robust and accurate system for recognising a wide range of complex classes. The project will also look into underlying methods of supervised and unsupervised machine learning, in order to identify which approaches provide reliable but efficient approaches for the labelling problem. One key question to be answered is whether the relationships between the observations can be utilised to guide and inform the user during their labelling of the data, and whether this is then reflected in the system's performance.



The approaches used:

The project will first implement a labelling system with input from a group of end users and previous literature. The labelling system will allow them to take in images and provide an effective method for labelling a small selection of the observed data, before then training supervised machine learning models to predict labels for the remaining images. The user will then enter into an active learning loop where they update the labels, retrain the model and review the predictions. This then repeats until the user is satisfied, or another criterion is met. The project will then look to implement an underlying data-structure which allows relationships between the observed samples to be represented. This data-structure will then be used to allow a machine learning model to be trained which takes not only the appearance of the image into consideration, but also the underlying relationships between the samples. Graph-based deep learning approaches will be used to then generalise the underlying domain of the problem. This graph-based approach can then be used for both predictive and generative models, and also in the visual presentation of the problem back to the domain user for further inspection.



Novel content:

The project novelty comes from understanding the underlying relationships between the images, and using them to guide and strengthen the active learning framework. It should identify key approaches in the active learning community, and should also provide methods for utilising the graph-based information for visualisation and interpretation of model activity.

The Centre will nurture 55 new PhD researchers who will be highly sought after in technology companies and application sectors where data and intelligence based systems are being developed and deployed. We expect that our graduates will be nationally in demand for two reasons: firstly, their training occurs in a vibrant and unique environment exposing them to challenging domains and contexts (that provide stretch, ambition and adventure to their projects and capabilities); and, secondly, because of the particular emphasis the Centre will put on people-first approaches. As one of the Google AI leads, Fei-Fei Li, recently put it, "We also want to make technology that makes humans' lives better, our world safer, our lives more productive and better. All this requires a layer of human-level communication and collaboration" [1]. We also expect substantial and attractive opportunities for the CDT's graduates to establish their careers in the Internet Coast region (Swansea Bay City Deal) and Wales. This demand will dovetail well with the lifetime of the Centre and provide momentum for its continuation after the initial EPSRC investment.

With the skills being honed in the Centre, the UK will gain a important competitive advantage which will be a strong talent based-pull, drawing in industrial investment to the UK as the recognition of and demand for human-centred interactions and collaborations with data and intelligence multiplies. Further, those graduates who wish to develop their careers in the academy will be a distinct and needed complement to the likely increased UK community of researchers in AI and big data, bringing both an ability to lead insights and innovation in core computer science (e.g., in HCI or formal methods) allied to talents to shape and challenge their research agenda through a lens that is human-centred and that involves cross-disciplinarity and co-creation.

The PhD training will be the responsibility of a team which includes research leaders in the application of big data and AI in important UK growth sectors - from health and well being to smart manufacturing - that will help the nation achieve a positive and productive economy. Our graduates will tackle impactful challenges during their training and be ready to contribute to nationally important areas from the moment they begin the next steps of their careers. Impact will be further embedded in the training programme with cohorts involved in projects that directly involve communities and stakeholders within our rich innovation ecology in Swansea and the Bay region who will co-create research and participate in deployments, trials and evaluations.

The Centre will also impact by providing evidence of and methods for integrating human-centred approaches within areas of computational science and engineering that have yet to fully exploit their value: for example, while process modelling and verification might seem much removed from the human interface, we will adapt and apply methods from human-computer interaction, one of our Centre's strengths, to develop research questions, prototyping apparatus and evaluations for such specialisms. These valuable new methodologies, embodied in our graduates, will impact on the processes adopted by a wide range of organisations we engage with and who our graduates join.

Finally, as our work is fully focused on putting the human first in big data and intelligent systems contexts, we expect to make a positive contribution to society's understandings of and involvement with these keystone technologies. We hope to reassure, encourage and empower our fellow citizens, and those globally, that in a world of "smart" technology, the most important ingredient is the human experience in all its smartness, glory, despair, joy and even mundanity.

[1] https://www.technologyreview.com/s/609060/put-humans-at-the-center-of-ai/