Earlier this year, Thomson Reuters announced a partnership with Watson, IBM's artificial intelligence system. Thomson Reuters is committed to better serving its customers by providing cognitive solutions, and we are working on a number of initiatives in the legal and risk space. As part of our investment in artificial intelligence, Thomson Reuters is working with IBM Watson to accelerate the development of cognitive capabilities in our products. We have also recently established a Centre for Cognitive Computing which provides dedicated focus and resources to explore and develop in-house capabilities in the rapidly developing field of cognitive computing. I can't be too specific at the moment, but we are targeting to bring some capabilities into beta in Q4 2016, and launching a new product in the first half of 2017.

The World Intellectual Property Organization has developed a ground-breaking new "artificial intelligence"-based translation tool for patent documents, handing innovators around the world the highest-quality service yet available for accessing information on new technologies. WIPO Translate now incorporates cutting-edge neural machine translation technology to render highly technical patent documents into a second language in a style and syntax that more closely mirrors common usage, out-performing other translation tools built on previous technologies. WIPO has initially "trained" the new technology to translate Chinese, Japanese and Korean patent documents into English. Patent applications in those languages accounted for some 55% of worldwide filings in 20141. Users can already try out the Chinese-English translation facility on the public beta test platform.

Microsoft hosts its Future Decoded event on an annual basis at London's ExCeL center in the fast-regenerating'docklands' area. But was this year's event just another set of polished executives striding around talking about so-called'business transformation', or were there guts and substance of any kind? The firm in fact devoted much of its opening statements and arguments to discuss intelligent machines, neural networks and Artificial Intelligence (AI). By way of introduction, Microsoft UK CEO Cindy Rose leads the software firm's British operations. The New York Law School educated Rose explained some of the company's new business models and detailed the firm's approach to now operating datacenters in the UK itself -- and this is always important for so-called'data residency' and data sovereignty.

We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not impose unnecessary constraints in the hazard rate or in the survival function. Furthermore, our model handles left, right and interval censoring mechanisms common in survival analysis. We propose a MCMC algorithm to perform inference and an approximation scheme based on random Fourier features to make computations faster. We report experimental results on synthetic and real data, showing that our model performs better than competing models such as Cox proportional hazards, ANOVA-DDP and random survival forests.

An artificial intelligence (AI) judge has accurately predicted most verdicts of the European Court of Human Rights, and might soon be making important decisions about cases. Scientists built an artificial intelligence computer that was able to look at legal evidence as well as considering ethical questions to decide how a case should be decided. And it predicted those with 79 per cent accuracy, according to its creators. The algorithm looked at data sets made up 584 cases relating to torture and degrading treatment, fair trials and privacy. The computer was able to look through that information and make its own decision – which lined up with those made by Europe's most senior judges in almost every case.

One the other hand, the job loss of nearly 1.7 million Americans who are currently working as drivers with an average salary of $42,500, could be one of the most negative impacts of this disruption on the US Economy as nearly 1% of the total US workforce is represented by truckers. This would be the first such scenario in history that the middle class would take on such a direct hit by bringing in automation in a single sector. The concerns are found pervading the whole trucking industry as even an online marketplace Quip Sells it, published an infographic signifying the number of jobs that could be lost due to automation led by driverless trucks, while on the surface, this has nothing of value to a business which deals in heavy machinery but this phenomenon will end up affecting all direct and indirect stakeholders. This is where the Government and the Policy Makers come in, as not only will they have to make more laws like the ones released by the Obama Administration on Sept 20 which are the first of their kind federal laws pertaining to the rules for driving automated vehicles, but also make provisions and involve strategists to absorb this massive wave of unemployment back into the economy.

A team of researchers in the UK have developed an artificial intelligence (AI) program that can predict the outcome of human rights cases involving torture, degrading treatment, and privacy. The AI -- developed by researchers at University College London (UCL) and the University of Sheffield, alongside Dr Daniel Preoţiuc-Pietro from the University of Pennsylvania -- successfully predicted the verdicts for 79% of 584 cases at the European Court of Human Rights (ECtHR). In order to reach a decision, the AI analysed case text using a machine learning algorithm, the researchers said. The algorithm looked for patterns in the text and was able to classify each case either as a "violation" or a "non-violation". To prevent bias and mislearning, the team selected an equal number of violation and non-violation cases.

An artificial intelligence system has already successfully predicted the outcome of hundreds of cases at the European Court of Human Rights, according to rsearchers from the University College London and the universities of Sheffield and Pennsylvania who developed it. According to reports, the AI "judge" examined data sets for 584 cases, with all cases either relating to torture, degrading treatment and privacy. The algorithm analyzed the English language information for each case and then made a decision – a decision that proved to be 79 percent accurate. The vast majority of applications lodged with ECHR are deemed inadmissible, due to the fact the applications don't meet the court's required criteria. This means that each year the court receives thousands of applications it must read through to determine admissibility.

In May last year, emoji was named as the world's fastest growing language. In May last year, emoji was named as the world's fastest growing language. IS EMOJI THE FASTEST GROWING LANGUAGE? 'Most people have no idea they can just type a bunch of hearts in their address bar and go to a domain,' the company said. It has been possible to register domain names made up of emojis for a while now.

Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objective for a de-identification system is to have the highest possible recall. Recently, the first neural-network-based de-identification system has been proposed, yielding state-of-the-art results. Unlike other systems, it does not rely on human-engineered features, which allows it to be quickly deployed, but does not leverage knowledge from human experts or from electronic health records (EHRs). In this work, we explore a method to incorporate human-engineered features as well as features derived from EHRs to a neural-network-based de-identification system. Our results show that the addition of features, especially the EHR-derived features, further improves the state-of-the-art in patient note de-identification, including for some of the most sensitive PHI types such as patient names. Since in a real-life setting patient notes typically come with EHRs, we recommend developers of de-identification systems to leverage the information EHRs contain.