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  • Artificial Intelligence

Artificial Intelligence

A glass pyramid sits on a colourful background
Computer systems performing tasks requiring human intelligence

Artificial Intelligence is the science and engineering of developing computer systems capable of performing tasks that typically require human intelligence, such as learning, reasoning, communicating, planning, acting and problem-solving.

  • Artificial Intelligence (AI) is the field of study that focuses on the creation of computer systems that can perform tasks that would typically require human intelligence. These tasks include learning from experience, reasoning, communicating in natural language, planning and acting, and solving complex problems. To achieve these capabilities, AI systems use a combination of algorithms, mathematical models, and data analysis to process and understand information in a way that mimics human thought processes. This includes techniques such as machine learning, deep learning, natural language processing, computer vision, and robotics. AI techniques are strongly addressed within the School.
  • A goal of AI is to create systems that can operate autonomously based on the information they have been given, without the need for human intervention. AI has the potential to revolutionise numerous industries and sectors, from healthcare and finance to transportation and customer service. The field of AI is constantly evolving, as researchers and practitioners work to advance the technology and develop new applications. The challenge of creating truly intelligent systems is complex, but the potential benefits to society are enormous, making AI a field of study that is both fascinating and important.
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MAIN RESEARCH AREAS

Within AI Theme, there is extensive expertise in machine learning and deep learning, natural language processing, computer vision, knowledge engineering, planning and scheduling, AI ethics, and computer systems for AI. Applications include:

  • Health: Biomarker/target discovery, e.g., virus detection, drug discovery; Human-centred medical imaging, e.g., explainable AI for age-related macular degeneration; Hypothesis generation, e.g., WHEEL intervention (in collaboration with Prof Nathan Congdon)
  • Fintech: Anomaly/outlier discovery, e.g., cyber security; Compliance audit, within Advanced Research and Engineering Centre with PwC;
  • Media: Semantic search (to search based on semantic embedding of content), e.g. multimodal video search
  • Others: Time tabling; AI for education e.g., question/answer generation

There is a growing body of work on Discovery AI, which is a combination of AI techniques to facilitate discovery research including causality discovery, model discovery and hypothesis generation. DAI aims to establish causal, correlational and logical relationships between entities based on different types of data including observational data, experimental data, ontologies, and scientific literature. 

  • Deep learning: to represent data semantically for purposes (e.g. classification) and to build models and to characterise interested classes of data.
  • Knowledge and reasoning: to extract and represent knowledge, to draw conclusions, and to generalise to generate new hypotheses.
  • Causality and Explainability: to discover causal relationships based on observational data and explain models and predictions on the basis of causal relationships

 

INDUSTRIAL COLLABORATIONS
anyvision
Anyvision

An industry leader in image processing techniques for face recognition.

Learn more


INNOVATION CENTRE
Momentum One Zero
Secure, Connected, Intelligence
Find out more

 

 

OUR IMPACT

Artificial Intelligence Research in Northern Ireland

ENVIRONMENT/SUPPORT
Critical Mass

ECIT Atlas Research Compute Solution can be used for computationally-intensive training of machine learning models. 


KEY STAFF CONTACT
Professor Hui Wang

Tel: 028 90972328

Email: h.wang@qub.ac.uk


SPINOUT COMPANIES

  • Anyvision 

  • EventMAP  

  • Brainwavebank

  • Analytics Engines

STAFF

ARTIFICAL INTELLIGENCE

Role

Interest Area

Hui Wang

Theme Lead

Machine learning, knowledge engineering

Ivor Spence

Reader

Heterogeneous systems, satisfiability solving

Barry Devereux

Lecturer

Natural language processing

Yang Hua

Lecturer

Machine learning (vision)

Anna Loughrey

Senior Lecturer

Machine learning (bioinformatics)

Barry McCollum

Senior Lecturer

Scheduling

Paul McMullan

Lecturer

Scheduling

Ji Ming

Professor (Emeritus)

Speech, audio and image processing

Deepak Padmanabhan

Senior Lecturer

Fairness in machine learning, ethical AI, unsupervised learning

Neil Robertson

Professor (Emeritus)

Machine learning (vision)

Mai Thai Son

Lecturer

Data mining, machine learning, high performance computing

Roger Woods

Professor

Data analytics, specialised computer hardware

Muhammad Fahim

Lecturer

Machine learning, MTA

Richard Gault

Lecturer

Computer vision, MTA

Note: MTA -- Multidisciplinary Topics and Applications

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School of Electronics, Electrical Engineering and Computer Science

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