RotM Artificial Intelligence can predict the outcomes of European Court of Human Rights trials to a high accuracy, according to research published today. The use of AI has is slowly seeping into many industries including the legal sector. AI can trawl through vast amounts of information at a faster rate than humans without slowing down, making it easier for lawyers to prepare for hearings. The paper, published in PeerJ Computer Science, shows that the new software has gone one step further. It can judge the final result of legal trials based on the information in human rights cases to 79 per cent accuracy.

During this on-demand webinar, Chief Scientist Kris Hammond, will present a quantitative method for identifying areas within an organization ripe for the application of Artificial Intelligence (AI) technologies, looking at the metrics of data, task, and volume. After viewing, you will have learned a scalable three-step process for assessing their organization's artificial intelligence opportunity. To learn more about AI and Natural Language Generation's application in the enterprise, be sure to check out our Solutions page.

An artificial intelligence system designed to predict the outcomes of cases at the European Court of Human Rights would side with the human judges 79 percent of the time. Researchers at University College London and the University of Sheffield in the U.K., and the University of Pennsylvania in the U.S., described the system in a paper published Monday by the Peer Journal of Computer Science. "We formulated a binary classification task where the input of our classifiers is the textual content extracted from a case and the target output is the actual judgment as to whether there has been a violation of an article of the convention of human rights," wrote the paper's authors, Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preo?iuc-Pietro and Vasileios Lampos. The system examined public court documents relating to 584 cases of violations of articles 3 (prohibiting torture), 6 (right to a fair trial) and 8 (respect for private life) of the European Convention on Human Rights, which has been ratified by 47 European countries. The court documents have a distinctive structure, discussing first the procedure by which the case reached the court, the facts and circumstances of the case, relevant law, and the legal arguments applied.

A leading Antarctic researcher died on 22 October after his snowmobile plunged 30 metres into an unseen crevasse. Glaciologist Gordon Hamilton of the University of Maine Climate Change Institute in Orono was fatally injured as a result, according to a statement released yesterday by the National Science Foundation. At the time, Hamilton was working with a team from the US Antarctic Program (USAP) actively identifying and filling in newly formed crevasses along the McMurdo shear zone. This is a stretch of intensely crevassed Antarctic ice where the Ross and McMurdo ice shelves meet. Hamilton was using robots equipped with ground-penetrating radar to study the stability of the ice shelves.

The automatic classification of documents is an example of how Machine Learning (ML) and Natural Language Processing (NLP) can be leveraged to enable machines to better understand human language. By classifying text, we are aiming to assign one or more classes or categories to a document or piece of text, making it easier to manage and sort the documents. Manually categorizing and grouping text sources can be extremely laborious and time-consuming, especially for publishers, news sites, blogs or anyone who deals with a lot of content. Broadly speaking, there are two classes of ML techniques: supervised and unsupervised. In supervised methods, a model is created based on previous observations i.e. a training set.

When people ask me what I'm working on, I'm often confused about the depth I need to go to in my response. 'Artificial Intelligence' is way too broad for my personal satisfaction, and image understanding probably too specific. Nevertheless, every single time, I do get this completely unrelated follow-up question that infuriates me to my core. And I can't even blame the skeptic -- most people think artificial intelligence is some unknown, mysterious entity which is conspiring infinitesimally, and will eventually kill us all, since it can predict that Sausage Party is the next movie we'd want to watch after we've binge-watched Evan Goldberg flicks all night. That's what makes predicting your favourite music, or suggesting the correct phone app to use while you're taking a dump -- an easy task for machines.

The Microsoft Emotion API is based on state of the art research from Microsoft Research in computer vision and is based on a Deep Convolutional Neural Network model trained to classify the facial expressions of people in videos and images. This is an attempt to explain how to apply the API for data-driven reporting. Let's be honest, the last and final debate was depressing. The negativity, the personal allegations, and Trump's Belzebub-like facial expressions made it difficult to stay up to 3:30am and watch this combat with my American wife, which resembled an old feisty couple close to divorce. However, the debate was a gold mine for computer assisted reporting. One of the APIs I recently stumbled across when talking to the research lab from Microsoft is a neat emotion video API.

Temperament has been a key issue in the 2016 presidential election between Hillary Clinton and Donald Trump, and an issue highlighted in the series of three debates that concluded this week. Quantifying "temperament" isn't an easy task, but The Economist used the Microsoft Emotion API to chart the anger, contempt, sadness and surprised expressed in the faces of the candidates during key sequences of the debates, like this from the third debate: Economist Data Journalist Ben Heubl explains how you can analyze emotions in a video file using Python and R. The Emotion API provides scores for eight attributes of emotion as expressed by a face in a still image or video clip. For example, this expression by Donald Trump expresses mostly anger, with a touch of disgust and a soupçon of contempt. Ben provides Python code for passing a video clip into the Emotion API and retriving frame-by-frame emotion scores. He then uses R to analyze and chart the scores: mostly happiness for Clinton; mostly sadness for Trump.

Baidu has created a virtual version of "turn your head and cough." The Chinese search engine launched "Melody" on Tuesday, a chatbot that uses artificial intelligence to help doctors care for patients over text. Baidu (BIDU, Tech30) aims to make medical consults more accessible and help patients determine whether or not they should see a doctor in person. For instance, if you tell Melody your child is sick, it might ask whether she has a fever or is jaundiced and follow up with additional questions. Melody integrates with the Baidu Doctor app, which already lets patients ask doctors questions, make appointments and search for health information.

Artificial intelligence researchers have developed software that is capable of making complex decisions to accurately predict the outcome of human rights trials. The AI "judge" was developed by computer scientists at University College London (UCL), the University of Sheffield and the University of Pennsylvania using an algorithm that analyzed the text of cases at the European Court of Human Rights. Judicial decisions from the court were predicted with 79 percent accuracy by the machine learning algorithm. "Previous studies have predicted outcomes based on the nature of the crime, or the policy position of each judge, so this is the first time judgments have been predicted using analysis of text prepared by the court," said Vasileios Lampos, co-author of the research. The study follows warnings from several high-profile academics and entrepreneurs that AI could pose an existential risk to mankind. According to Tesla CEO Elon Musk, advanced AI could be "more dangerous than nukes," while in 2015 physicist Stephen Hawking suggested it could lead to the end of humanity.