Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.

At Catch22, we are proud of our reputation as a modern and progressive employer. Our 1,300 colleagues and 300 volunteers work at every stage of the social welfare cycle, supporting over 60,000 individuals from cradle to career. In Social Justice, we work with young people and adults in custody and in the community, providing a range of services, including offender management and resettlement, mentoring, veterans in custody, victim services, gangs work, and youth justice. We believe that with effective support mechanisms, and the correct interventions, we can change the ideology of service users, helping them to desist from crime, and reach their true potential. This post presents an exciting opportunity to become an Admin & Data Analyst within our Personal Wellbeing services, in the Hampshire and Isle of Wight region.

Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include: heavy reliance on massive training data, limited generalizability and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.

Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow.

Hampshire, UK – 23rd May 2018: New findings from Juniper Research reveal that the annual sales of fully autonomous vehicles (AVs) in the US will reach 5 million by 2026. In comparison, the global market will account for 20 million new AVs sold during the same year.

The current resurgent interest in Artificial Intelligence (AI) has been driven by the availability of data (particularly labelled data), the democratisation of computing infrastructure and tooling, and the ability to combine these elements to create AI algorithms. Benefit is achieved once an algorithm is deployed into an operational system to achieve an operational advantage. The ability to exploit the opportunities offered by AI within UK Defence calls for an understanding of systemic issues required to achieve an effective operational capability. This paper provides the authors' views of issues which currently block UK Defence from fully benefitting from AI technology. These are situated within a reference model for the AI Value Train, so enabling the community to address the exploitation of such data and software intensive systems in a systematic, end to end manner. The paper sets out the conditions for success including: - Researching future solutions to known problems and clearly defined use cases; - Addressing achievable use cases to show benefit; - Enhancing the availability of Defence-relevant data; - Enhancing Defence'know how' in AI; - Operating Software Intensive supply chain ecosystems at required breadth and pace; - Governance and, the integration of software and platform supply chains and operating models.

The first things you notice are the chill in the air, the vast grid on the floor which makes you feel like you're on the film set of the movie Tron, and the whooshing sound of wheels skimming across aluminium. The last thing on your mind is buying groceries. I'm standing upstairs in an Ocado warehouse in Hampshire, England, where grocery orders are assembled and dispatched, watching hundreds of cuboid robots whizz around on a vast metal grid that stretches out as far as I can see. The grocery giant doesn't often let journalists in to its 18 acre (784,080 sq ft) Andover site. With the exception of two or three maintenance engineers on standby, my guide and I are the only humans in the vast space.

Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor of computer science, director of the Center for Intelligent Systems, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was cowinner of the Computers and Thought Award. He was a 1996 Miller Professor of the University of California and was appointed to a Chancellor's Professorship in 2000. In 1998, he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence. He has published over 100 papers on a wide range of topics in artificial intelligence. His other books include The Use of Knowledge in Analogy and Induction and (with Eric Wefald) Do the Right Thing: Studies in Limited Rationality.

London: Artificially intelligent (AI) nano-machines will be injected into humans in 20 years to be used to repair and enhance muscles, cells and bones, as well as enable us to control our environment with thought and gestures, says an IT specialist at IBM. "We may see nano-machines being injected into our bodies," John McNamara, senior inventor and IT specialist at IBM's Hursley Innovation Centre in Hampshire, UK, was quoted as saying to the Telegraph.co.uk. "These will provide huge medical benefits, such as being able to repair damage to cells, muscles and bones -- perhaps even augment them," McNamara added. McNamara said that within two decades, technology may have advanced to such a level that humans and machines are effectively "melded" together, allowing for huge leaps forward in human consciousness and cognition, raising the economic, ethical and social implications of AI. "Beyond this, utilising technology which is already being explored today, we see the creation of technology that can meld the biological with the technological and so be able to enhance human cognitive capability directly, as well as being able to utilise vast quantities of computing power to augment our own thought processes. "Using this technology, embedded in ourselves and in our surroundings, we will begin to be able to control our environment with thought and gestures alone," McNamara noted. However, in a report submitted to the House of Lords Artificial Intelligence Committee, McNamara warned that the rise of AI could bring "huge disruption" in the retail and service sectors which could spike widespread unemployment.

Hampshire, UK: 9th May 2017: A new study from Juniper Research has found that chatbots will redefine the customer service industry, with healthcare and banking sectors set to benefit the most. The new research, Chatbots: Retail, eCommerce, Banking & Healthcare 2017-2022, forecasts that chatbots will be responsible for cost savings of over $8 billion per annum by 2022, up from $20 million this year. Juniper expects dramatic cost savings to be made in the healthcare and banking sectors, as enquiry resolution times are reduced and cost savings boosted. Research author Lauren Foye explained: "We believe that healthcare and banking providers using bots can expect average time savings of just over 4 minutes per enquiry, equating to average cost savings in the range of $0.50-$0.70 per interaction. As Artificial Intelligence advances, reducing reliance on human representatives undoubtedly spells job losses."