The UKRI Centre for Doctoral Training (CDT) in the'Application of Artificial Intelligence to the study of Environmental Risks' (AI4ER) will, through several multi-disciplinary cohorts, train researchers uniquely equipped to develop and apply leading edge computational approaches to address critical global environmental challenges by exploiting vast, diverse and often currently untapped environmental data sets. Embedded in the outstanding research environments of the University of Cambridge and the British Antarctic Survey (BAS), the AI4ER CDT will address problems that are relevant to building resilience to environmental hazards and managing environmental change. The primary application areas will be: a) Weather, Climate and Air Quality, b) Natural Hazards, c) Natural Resources (food, water & resource security and biodiversity). The AI4ER CDT is offering at least 10 fully-funded PhD studentships to start in October 2020. Students in the CDT cohorts engage in a one-year Master of Research (MRes) course with a taught component and a major research element, followed by a three-year PhD research project.
The deployment of artificial intelligence (AI) and other advanced technologies could trigger a four per cent decline in the number of lawyers in England and Wales by 2027, according to a new report. The study warns AI could halt the historic year-on-year growth in the number of lawyers in its tracks, with the profession shrinking by 7,000 lawyers to 169,200 when compared to 2017. However, the decline would only happen if AI take-up was even faster than predicted. The report's baseline findings are that the number of lawyers is likely to rise by a modest two per cent over the period, although overall employment in the sector will fall by four per cent thanks to a sharp decline in the number of legal secretaries and other office support staff as their roles are taken over by technology. The report notes: "In 1998 there were two legal professionals to one legal secretary, and the ratio was one to one when adding in other office support staff, but by 2017 the ratios had increased to five legal professionals per legal secretary, and two legal professionals for every secretary or other office support worker. "In 2027, there are projected to be around 20 legal professionals per legal secretary, and five legal professionals for every secretary or other office support worker.
The adoption of new technologies such as artificial intelligence could lead to the UK legal sector shedding 13,000 jobs, according to a report by the Law Society of England and Wales. The report on the future shape of the legal workforce projected a 13,000 fall in the number of jobs by 2027, equivalent to a four per cent drop. The body said the number of legal secretaries is projected to fall by nearly two thirds and other office support staff by a quarter. Many major law firms have already axed support staff, particularly in expensive locations such as London. Magic Circle firm Freshfields Bruckhaus Deringer offered voluntary redundancy to all 180 of its secretaries in London in 2017, while both Ashurst and Baker McKenzie have made staff cuts in the City this year.
On 11 April 2019, Daniel Fiott was invited by the EU's Political and Security Committee (PSC) to participate in a lunch debate on Artificial intelligence (AI). The event was part of the PSC's initiative to enhance dialogue with think tanks, NGOS and academia on key challenges for EU foreign, security and defence policy. The event brought together PSC Ambassadors, as well as representatives from the European Commission and the European External Action Service. Daniel joined experts from the Centre for the Study of Existential Risk (CSAR) at the University of Cambridge and Tilburg University, and he outlined recent AI developments and implications for the defence sector, with a particular focus on the EU and AI developments in Russia, China and the United States. The legal challenges and ethical dilemmas of AI were also discussed.
Artificial intelligence is poised to transform the way we work, learn and live. Across the globe, businesses, governments and the public at large are already having to adapt to the rapid development of these technologies. The Global AI Index analyses how 54 countries are driving and adapting to AI's accelerating development through three pillars; investment, innovation and implementation. Here is the Index in full. Use the toggle to switch between our Index's ranks – where countries stand – or score – how far or close they are to each other.
Consistent in their quest to spearhead innovative, groundbreaking events, Eventus International is hosting the first ever AI In Gaming 2020 summit in Dubai on 26 and 27 February at Crowne Plaza Dubai. Joining a lineup of top international industry experts, is Andrew Pearson, founder and MD of Intelligencia Limited, who will be speaking at AI In Gaming 2020. Andrew Pearson was born in Pakistan, grew up in Singapore and was educated in England and America. With a degree in psychology from UCLA, Pearson has had a varied career in IT, marketing, mobile technology, social media and entertainment.In 2011, Pearson relocated to Hong Kong to open Qualex Asia Limited, bringing its parent company's experience into the ASEAN region. Pearson is the Managing Director of Intelligencia Limited, a leading implementer of BI, CI, data warehousing, data modeling, predictive analytics, data visualisation, digital marketing, mobile, social media and cloud solutions for the gaming, finance, telco, hospitality and retail industries.
Causal inference goes beyond prediction by modeling the outcome of interventions and formalizing counterfactual reasoning. In this blog post, I provide an introduction to the graphical approach to causal inference in the tradition of Sewell Wright, Judea Pearl, and others. We first rehash the common adage that correlation is not causation. We then move on to climb what Pearl calls the "ladder of causal inference", from association (seeing) to intervention (doing) to counterfactuals (imagining). We will discover how directed acyclic graphs describe conditional (in)dependencies; how the do-calculus describes interventions; and how Structural Causal Models allow us to imagine what could have been. This blog post is by no means exhaustive, but should give you a first appreciation of the concepts that surround causal inference; references to further readings are provided below. Messerli (2012) published a paper entitled "Chocolate Consumption, Cognitive Function, and Nobel Laureates" in The New England Journal of Medicine showing a strong positive relationship between chocolate consumption and the number of Nobel Laureates. I have found an even stronger relationship using updated data2, as visualized in the figure below. Now, except for people in the chocolate business, it would be quite a stretch to suggest that increasing chocolate consumption would increase the number Nobel Laureates. Correlation does not imply causation because it does not constrain the possible causal relations enough. If two random variables $X$ and $Y$ are statistically dependent ($X \perp Y$), then either (a) $X$ causes $Y$, (b) $Y$ causes $X$, or (c) there exists a third variable $Z$ that causes both $X$ and $Y$. Further, $X$ and $Y$ become independent given $Z$, i.e., $X \perp Y \mid Z$. An in principle straightforward way to break this uncertainty is to conduct an experiment: we could, for example, force the citizens of Austria to consume more chocolate, and study whether this increases the number of Nobel laureates in the following years.
It is said to have the potential to save the industry countless hours and millions of pounds a year. The strength prediction engine was developed in collaboration with BAM Nuttall, using funding from an Innovate UK grant awarded last year. The system is already being used on BAM Nuttall's London City Airport expansion project. Development of the system was made possible by Converge's access to a huge data set on concrete performance, paving the way for the commercial application of machine learning to monitor and predict material performance in a live project. BAM Nuttall head of innovation Colin Evison said: "This advancement in construction technology is a game changer. The Converge prediction engine gives us insight into material performance we didn't think possible. We are delighted to be Converge's industry partner in bringing this exciting new tool to market."
Ironically, algorithms are telling us that machines will soon be able to do most of our jobs, but those conclusions perfectly illustrate what's non-scientific about turning human reasoning over to computers. Barely a month passes without a news story about how robots and artificial intelligence (AI) are going to devastate a significant number of the jobs in the workplace, sweeping away solid salary paying employment for factory workers and white-collar clerks alike. Just this month, the UK Parliament reported on the future of work in the face of automation, declaring that over 70% of jobs were at medium to high risk of displacement. While the report draws on a "more optimistic" study by the ONS to arrive at this prediction, the ONS used methods inspired by the even-more-frightening results in a 2013 paper by Oxford economist Carl Frey and machine learning expert Michael Osbourne, which found that almost half of jobs were at high risk, and two-thirds at medium risk. The paper was so important that in addition to shaping the methodologies of later reports (like those of the ONS, the OECD, and others), it informed a speech of the Bank of England's Chief Economist to the Trades Union Congress in 2015, prompted the "Fourth Industrial Revolution" theme of the 2016 Davos Forum, provided the basis of the WEF "Future of Jobs" report, and generated a subsequent sea of articles by journalists who rarely questioned the numbers.
Captivating images capture by custom-built drones have revealed the damage to the Greenland Ice Sheet that is being caused by rising global temperatures. The images, which have been taken as part of an EU-funded project to track changes in the world's second-largest ice sheet, are the first drone-based observations of how fractures form and expand under meltwater lakes. The expanding fractures cause catastrophic lake drainages, during which huge quantities of water are transferred to below the surface of the ice. Changes in ice flow occur on a much shorter timescales than were previously considered possible, said the research team, which was led by the University of Cambridge. 'It's possible we've under-estimated the effects of these glaciers on the overall instability of the Greenland Ice Sheet,' said drone pilot Tom Chudley, a PhD student at the University of Cambridge's Scott Polar Research Institute.