A maximum of 50 participants are onsite at the Digital Futures hub. The deadline for registration is 10 September! If you cannot participate on-site, you are welcome to join us via Zoom. Zoom-link will be sent upon registration. The main objective of this PhD course, by Dr Pei Wang, Dr Patrick Hammer and Dr Robert Johansson, is to introduce the audience to Artificial General Intelligence.

Learn involves how to extract information from data that makes systems smart and adaptive or even autonomous. Since the generation and storage of data will often be distributed, there is a strong need for efficient distributed data analytics. A fundamental understanding of how machine learning algorithms extract information from data is still missing, and the impact of the data on the learning process and the resulting bias is hardly understood. This gives rise to questions concerning legal safeguards and the rule of law. If you are interested in joining the working group, please feel free to contact the chair.

Digital Futures at Oxford is a series of thought leadership sessions organised by IT Services. Leaders from industry and external organisations have been invited to share their views on the future direction of digital in higher education. The Digital Futures series launched during December 2021 with three talks. If you missed these talks, recordings are available in the'Related links' section on the right of this page. The series continues during January with more fascinating talks giving us food for thought about Oxford's digital future. These online lunchtime sessions will run on Microsoft Teams and are open to all members of Oxford University.

Hi Katie Winkle, describe your role as a Postdoc Fellow at Digital Futures and why you applied for this fellowship mobility program? I applied for the Digital Futures fellowship because of the fantastic freedom it gives you to pursue your own research project, and the idea of working with researchers from a range of universities and departments really appealed to me. The title of your postdoc project is "On The Feminist Design of Social Robots and Designing Robots For Young People, With Young People". It's a long title – tell us a bit about it? Have you already some results from your work?

'Insider Threat' is a formidable risk to business because it threatens both customer and employee trust. Accidental or malicious misuse of a firm's most sensitive and valuable data can result in customer identity theft, financial fraud, intellectual property theft, or damage to infrastructure. Because insiders have privileged access to data in order to do their jobs, it's usually quite difficult for security professionals to detect suspicious activity; a process even more challenging to automate (and deploy at scale across the large organisations that most need it) as – so I will suggest – computers fundamentally lack semantic understanding of the meaning of the'bits' they so adroitly process. Conversely, in this talk I will outline a new approach to'Insider Threat' detection that draws inspiration from the Traffic Analysis' of encrypted Axis signal traffic' undertaken at Bletchley Park in WW2. A novel approach that (i) conceives companies as complex autonomous autopoietic entities and (ii) deploys state of art computational analysis of the communication flows that so define the company to flag potentially aberrant employee behaviour; intelligence that can be leveraged to help detect HR problematics before they arise.

The phrase "artificial intelligence" can stir up a lot of panic at some federal agencies, and can give rise to the idea of intelligent machines putting some employees out of work. However, some federal agencies are embracing the idea of artificial intelligence, and in those test cases, adopting machine learning comes down to a few key strategies like starting small and managing expectations. While AI isn't a panacea for every big-data problem in government, agency leaders say they see value in using machine learning to handle the most tedious aspects of handling data, which frees up human operators to address more mission-critical issues. Insight by Red Hat: Agency experts examine the DevSecOps mindset in government. "Artificial intelligence is an imperative.

The phrase "artificial intelligence" can stir up a lot of panic at some federal agencies, and can give rise to the idea of intelligent machines putting some employees out of work. However, some federal agencies are embracing the idea of artificial intelligence, and in those test cases, adopting machine learning comes down to a few key strategies like starting small and managing expectations. While AI isn't a panacea for every big-data problem in government, agency leaders say they see value in using machine learning to handle the most tedious aspects of handling data, which frees up human operators to address more mission-critical issues. "Artificial intelligence is an imperative. It's not something that's nice to have, or something that we should consider at some point," Teresa Smetzer, the director of digital futures at the Central Intelligence Agency said Tuesday during an event sponsored by Partnership for Public Service and the IBM Center for the Business of Government.