England


Responses to a Critique of Artificial Moral Agents

arXiv.org Artificial Intelligence

The field of machine ethics is concerned with the question of how to embed ethical behaviors, or a means to determine ethical behaviors, into artificial intelligence (AI) systems. The goal is to produce artificial moral agents (AMAs) that are either implicitly ethical (designed to avoid unethical consequences) or explicitly ethical (designed to behave ethically). Van Wynsberghe and Robbins' (2018) paper Critiquing the Reasons for Making Artificial Moral Agents critically addresses the reasons offered by machine ethicists for pursuing AMA research; this paper, co-authored by machine ethicists and commentators, aims to contribute to the machine ethics conversation by responding to that critique. The reasons for developing AMAs discussed in van Wynsberghe and Robbins (2018) are: it is inevitable that they will be developed; the prevention of harm; the necessity for public trust; the prevention of immoral use; such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. In this paper, each co-author addresses those reasons in turn. In so doing, this paper demonstrates that the reasons critiqued are not shared by all co-authors; each machine ethicist has their own reasons for researching AMAs. But while we express a diverse range of views on each of the six reasons in van Wynsberghe and Robbins' critique, we nevertheless share the opinion that the scientific study of AMAs has considerable value.


How machine learning, drones, and robotics will transform the NHS and healthcare

ZDNet

The UK's National Health Service continues to suffer the longest funding squeeze since it was established 71 years ago. That financial pressure has resulted in the service missing targets for how soon cancer patients should be referred for treatment for the past three years and waiting times in Accident and Emergency departments being at record levels. Such is the financial and staffing pressure on the service, that talking about how recent advances in artificial intelligence (AI) could be applied to the NHS might seem fanciful. Yet Professor Tony Young, national clinical director for innovation at NHS England, believes healthcare is at an inflection point, where machine-learning technology could fuel huge advances in what's possible. "I think that healthcare is heading for one of those giant-leap moments in the next five to 10 years and AI is going to be a key tool in enabling us to take that giant leap," he said, speaking at an event in London organized by The King's Fund and IBM Watson Health.


New Artificial Intelligence Advisory Body in England and Wales – Bringing the Modern World to the Judiciary

#artificialintelligence

Lord Burnett of Maldon, the current Lord Chief Justice, has set up a new Advisory Body with the aim of ensuring that the Judiciary of England and Wales is fully informed about developments in artificial intelligence (AI). Professor Richard Susskind, President of the Society for Computers & Law, has been named chair of the body, and in a recent interview stated that AI has taken off in the last six or seven years, to the point where it has become "affordable and practical". Professor Susskind believes that the new group will start a dialogue among the judiciary about "one of the most influential technologies that there is", and recognises the importance of judges being open to the opportunities that AI technology could offer to the court system (with "practical tasks" cited as an example). The 10-person team will be made up of both senior judges (including Lord Neuberger, past President of the UK Supreme Court, and Lady Justice Sharp, Vice-President of the Queen's Bench Division), as well as leading experts on AI and law (such as Professor Katie Atkinson, past President of the International Association for AI and Law). There is little doubt that automation already plays an essential role for the legal profession, for example, in large disclosure exercises.


Machine learning used to identify high-performing solar materials

#artificialintelligence

Finding the best light-harvesting chemicals for use in solar cells can feel like searching for a needle in a haystack. Over the years, researchers have developed and tested thousands of different dyes and pigments to see how they absorb sunlight and convert it to electricity. Sorting through all of them requires an innovative approach. Now, thanks to a study that combines the power of supercomputing with data science and experimental methods, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory and the University of Cambridge in England have developed a novel "design to device" approach to identify promising materials for dye-sensitized solar cells (DSSCs). DSSCs can be manufactured with low-cost, scalable techniques, allowing them to reach competitive performance-to-price ratios.


Top UK-Based AI Fellowships That Indian Students Can Apply For

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In an attempt to attract and retain the best of the talent, the UK government has rolled out a number of scholarships for Indian and EU students. As part of the programme, 1,000 students from India and the European Union will get an opportunity to enhance their skills in artificial intelligence through their 16 dedicated centres for Research and Innovation AI Centres for Doctoral Training (CDTs) in universities across England, Scotland and Wales. The £110 million packages by the UK government will offer 200 AI Masters places at UK universities which is also funded by the likes of Infosys, Deepmind, QuantumBlack, Cisco and BAE Systems. Types of scholarship: Turing Senior AI Fellowships: For existing leaders in the field and applicants should already be demonstrating leadership equivalent to a full professorial position. Location: Fellows will be based in the UK and hosted by a UK organisation with significant ability to carry out research.


Engineers of the future create Lego robots

BBC News

Over 70 teams of schoolchildren from the UK and Ireland took part in the Institution of Engineering and Technology event in Bristol.


The Topol Review – NHS Health Education England

#artificialintelligence

The Secretary of State for Health and Social Care commissioned The Topol Review: Preparing the healthcare workforce to deliver the digital future, as part of the draft health and care Workforce Strategy for England to 2027 – Facing the Facts, Shaping the Future. The Topol Review, led by cardiologist, geneticist, and digital medicine researcher Dr Eric Topol, explores how to prepare the healthcare workforce, through education and training, to deliver the digital future. Dr Topol appointed a Review Board and three Expert Advisory Panels. HEE provided the secretariat team to facilitate the Review. The Topol Review is now published and it makes recommendations that will enable NHS staff to make the most of innovative technologies such as genomics, digital medicine, artificial intelligence and robotics to improve services.


Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders

arXiv.org Machine Learning

The estimation of treatment effects is a pervasive problem in medicine. Existing methods for estimating treatment effects from longitudinal observational data assume that there are no hidden confounders. This assumption is not testable in practice and, if it does not hold, leads to biased estimates. In this paper, we develop the Time Series Deconfounder, a method that leverages the assignment of multiple treatments over time to enable the estimation of treatment effects even in the presence of hidden confounders. The Time Series Deconfounder uses a novel recurrent neural network architecture with multitask output to build a factor model over time and infer substitute confounders that render the assigned treatments conditionally independent. Then it performs causal inference using the substitute confounders. We provide a theoretical analysis for obtaining unbiased causal effects of time-varying exposures using the Time Series Deconfounder. Using simulations we show the effectiveness of our method in deconfounding the estimation of treatment responses in longitudinal data.


Value Investing with Machine Learning – Towards Data Science

#artificialintelligence

After a few more iterations, AuDaS was able to find a model with a 92% classification accuracy. This means that AuDaS was able to learn a relationship in the data that is able to predict Infosys' score 30 days ahead with a 92%! This accuracy can be increased by infusing the Analysts' awareness of context which could affect the score. That is why Machine Learning should augment Analysts, not automate them! Please don't hesitate to reach out with your feedback if you are an Analyst or wish to see a live demo of AuDaS!


Game Changer: AlphaZero's Groundbreaking Chess Strategies and the Promise of AI

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Matthew Sadler (1974) is a Grandmaster who twice won the British Championship and was awarded an individual Gold Medal at the 1996 Olympiad. He has authored several highly acclaimed books on chess and has been writing the famous'Sadler on Books' column for New In Chess magazine for many years. Natasha Regan is a Women's International Master from England who achieved a degree in mathematics from Cambridge University. Matthew Sadler and Natasha Regan won the English Chess Federation 2016 Book of the Award for their book Chess for Life. Garry Kasparov: "Chess has been shaken to its roots by AlphaZero." Steven Strogatz (professor of mathematics at Cornell), New York Times, December 26, 2018: "Most unnerving was that AlphaZero seemed to express insight. It played like no computer ever has, intuitively and beautifully, with a romantic, attacking style."