The TERESA project adds a vital dimension: social intelligence. The researchers have developed methods that enable the robots to perform social functions automatically, so that the human controller never needs to make decisions about how the robot should move around or what postures it should adopt. TERESA robots are able to navigate semi-autonomously among groups, maintaining face-to-face contact during conversations, and displaying appropriate body-pose behaviour – in a similar way to human beings. Algorithms that can interpret social behaviour are able to detect facial emotions such as the intensity of a smile, for example, and respond accordingly. Thanks to the robot's social intelligence, the human controller is free to focus on interactions with other people, instead of worrying about manually navigating the robot or adjusting its position and orientation.

Computational modelling has been brought under the spotlight during the COVID-19 pandemic, with scientists trying to predict how the SARS-CoV-2 virus will spread. On 23 March 2020 UK prime minister Boris Johnson announced a lockdown to tackle the spread of coronavirus, following the example of other countries around the world who chose this strategy to halt the virus' progression. This decision came days after Johnson's government toyed with the idea of letting the virus spread and infect up to 70% of the population, in order to develop so-called "herd immunity". The stark policy shift left people wondering what had changed. They predicted that should no action be taken, the death toll in the UK could reach 500,000, and may exceed 2 million in the US.

An opportunity to present the results of your research to other students, industrial partners, and invited researchers from other universities. As part of this we invite our industrial collaborators to share the latest problems and market trends, and discuss opportunities for future collaboration with our students. We plan for a two day outreach event in the training programme, during which you will be encouraged to demonstrate the systems built during your group project, individual short research project, or later on as part of your PhD research. You will show them to beneficiaries such as companies and government departments, as well as schools and local communities. In Year 4, your cohort will be asked to help organise the Annual CDT Workshop, inviting keynote speakers, participating in the program committee, reviewing papers submitted by 2nd and 3rd year students, and publicising the workshop to other universities and industrial partners beyond those directly involved in the CDT.

Prof. Gavin Lowe Excellent student feedback for teaching Concurrent Programming and Concurrency (with the latter moved to remote teaching during TT 2020).

Computational methods can increase productivity of drug discovery pipelines, through overcoming challenges such as cardiotoxicity identification. We demonstrate prediction and preservation of cardiotoxic relationships for six drug-induced cardiotoxicity types using a machine learning approach on a large collected and curated dataset of transcriptional and molecular profiles (1,131 drugs, 35% with known cardiotoxicities, and 9,933 samples). The algorithm generality is demonstrated through validation in an independent drug dataset, in addition to cross-validation. Individual cardiotoxicities for specific drug types are also predicted with high accuracy, including cardiac disorder signs and symptoms for a previously unseen set of anti-inflammatory agents (AUC 80%) and heart failures for an unseen set of anti-neoplastic agents (AUC 76%). Besides, independent testing on transcriptional data from the Drug Toxicity Signature Generation Center (DToxS) produces similar results in terms of accuracy and shows an average AUC of 72% for previously seen drugs and 60% for unseen respectively.

You may like to look at our GeomLab website which will introduce you to some of the most important ideas in computer programming in an interactive, visual way through a guided activity. The Turtle system is a graphics programming environment designed to provide an enjoyable introduction to programming in Java syntax, together with a practical insight into fundamental concepts of computer science such as compilation and machine code. The Alice system from Carnegie Mellon University provides a point-and-click environment for designing 3-D animations and is a useful introduction to object-oriented programming. Elizabeth is an automated conversation and natural language processing program that provides an enjoyable introduction to natural language processing, and that can give insights into some of the fundamental methods and issues of artificial intelligence within an entertaining context. CodeAcademy provides a fun introduction to programming.

Alice is an innovative block-based programming environment that makes it easy to create animations, build interactive narratives, or program simple games in 3D. Alice is designed to teach logical and computational thinking skills, fundamental principles of programming and to be a first exposure to object-oriented programming. The Alice Project provides supplemental tools and materials for teaching using Alice across a spectrum of ages and subject matter with proven benefits in engaging and retaining diverse and underserved groups in computer science education.

Oxford University spin-out Navenio has announced £9m in Series A funding for its efficiency-boosting location technology. The funding round was led by QBN Capital and includes G.K. Goh, Hostplus, Big Pi Ventures, Oxford Investment Consultants, as well as existing investors like Oxford Sciences Innovation (OSI), IP Group plc and the University of Oxford. Navenio provides infrastructure-free indoor location solutions to power a range of apps and platforms in sectors including healthcare. Hospitals, for example, can use Navenio's artificial intelligence (AI) led'intelligent workforce solution' to assign tasks to healthcare teams based on their location. This helps prioritise workload in real-time.

Ironically or not, the best way to understand science is often through history. As Wooldridge relates early on, he planned this book as'the story of AI through failed ideas' - and in this he succeeds brilliantly in showing the fascinating and compelling slog towards Artificial Intelligence. This is an outstanding read. It is passionate about the technology, yet sceptical about its achievements. Wooldridge - Head of the Computer Science department at Oxford University - is humane in his judgements, yet clear and logical in his assessments. There's enough to get your teeth into, but little to scare away the general reader.

Open Philanthropy recommended a total of approximately $2,300,000 over five years in PhD fellowship support to 10 promising machine learning researchers that together represent the 2020 class of the Open Phil AI Fellowship.1 These fellows were selected from more than 380 applicants for their academic excellence, technical knowledge, careful reasoning, and interest in making the long-term, large-scale impacts of AI a central focus of their research. This falls within our focus area of potential risks from advanced artificial intelligence. We believe that progress in artificial intelligence may eventually lead to changes in human civilization that are as large as the agricultural or industrial revolutions; while we think it's most likely that this would lead to significant improvements in human well-being, we also see significant risks. Open Phil AI Fellows have a broad mandate to think through which kinds of research are likely to be most valuable, to share ideas and form a community with like-minded students and professors, and ultimately to act in the way that they think is most likely to improve outcomes from progress in AI. The intent of the Open Phil AI Fellowship is both to support a small group of promising researchers and to foster a community with a culture of trust, debate, excitement, and intellectual excellence.