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.

The descendants of Alexa and Siri might come with a surprisingly good sales pitch. I met an early version of such a persuasive chatbot at a tech conference in Pittsburgh recently. After some small talk and jokes, the bot, called Sara, recommended some other people for me to meet. The suggestions were in fact excellent, and if I hadn't just met with them already, I would've followed her lead. Sara was developed by Justine Cassell, director of human-computer interaction at Carnegie Mellon University, who is studying ways for virtual agents to use subtle cues in conversation to build rapport with people and become more effective at conveying information or persuading them to do something.

Times Higher Education recently asked whether universities needed to rethink what they do and how they do it, given that artificial intelligence is beginning to take over many post-university careers. With that in mind, here are four examples of how AI can benefit universities. First, there is a new role for higher education, which is to equip graduates to work effectively alongside artificially intelligent systems. The onslaught of AI on white-collar jobs is likely to lead to the AI augmentation of human intelligence, rather than the total replacement of human workers with machine workers. We need workers who understand how to make the best use of the power that AI automation can bring to industry and commerce.

It seems to me that the hype about AI makes really difficult for experienced investors to understand where the real value and innovation are. I would like then to humbly try to bring some clarity to what is happening on the investment side of the artificial intelligence industry. We have seen as in the past the development of AI has been stopped by the absence of funding, and thus studying the current investment market is crucial to identify where AI is going. First of all, it should be clear that investing in AI is extremely cumbersome: the level of technical complexity goes out of the pure commercial scope, and not all the venture capitalists are able to fully comprehend the functional details of machine learning. This is why the figures of the "Advisors" and "Scientist-in-Residence" are becoming extremely important nowadays.

Late last month it was revealed that Apple is quietly working on the iPhone 8 in its Herzliya, Israel offices. Today, it's been revealed that the Cupertino giant is working on its car operating software in its Kanata, Canada facility. The location is somehow strategic, since the tech company hired ex-employees of its former rival, BlackBerry, to work on this project. According to MacRumors, Apple's R&D facility in Canada is focused on the "software core" of its upcoming self-driving car program that is currently being developed by a separate Project Titan team. The car operating system is said to come with many features, such as a heads-up display that would be very useful to drivers who want to access Maps via Apple's digital assistant, Siri.

World chess champion Magnes Carlsen (right) won't play his computer or play the game like a computer. Instead, he chooses his strategy based on what he knows about his opponent. World chess champion Magnes Carlsen (right) won't play his computer or play the game like a computer. Instead, he chooses his strategy based on what he knows about his opponent. Next month, there's a world chess championship match in New York City, and the two competitors, the assembled grandmasters, the budding chess prodigies, the older chess fans -- everyone paying attention -- will know this indisputable fact: A computer could win the match hands down. They've known as much for almost 20 years -- ever since May 11, 1997.

While transportation, hospitality, and financial services are all industries being disrupted by technology, the next big area poised for massive, tech-driven change may be the human workforce. "We are going to move from people to things," explained Jane Fraser, CEO of Citigroup's Latin America business, speaking Monday at Fortune's Most Powerful Women Summit in Laguna Niguel, Calif. "We are expecting 500 billion objects to become connected to the internet and this automation is going to hollow out middle and working class jobs," explained Fraser. "Technology is replacing these jobs." The technology Fraser is referring to is artificial intelligence--the machine learning that powers driverless cars and other intelligent machines that are slowly taking over human tasks.

Matt Zeiler grew up in a Canadian farming community -- but fast forward a few decades and he's now running a startup that's looking to bring the same kinds of visual search tools that Pinterest and Google have to other companies and developers. That company is Clarifai, a New York-based startup that offers developers the ability to tag metadata to photos in such a way that the company algorithmically learns what kinds of objects are in photos. With that, Clarifai developers can train algorithms to be able to search for those objects, or input their own photos in order to find similar objects. The company said today that it has raised $30 million. The round was led by Menlo Ventures, with Union Square Ventures, Lux Capital and others participating.

The AI'nightmare machine': Spooky Google algorithm transforms famous sights into horror scenes The DeepDream algorithm transfers a photograph of the Eiffel Tower in Paris to a horror scene, in a style called'Fright Night', according to the website. 'We use state-of-the-art deep learning algorithms to learn how haunted houses, or toxic cities look like,' the researchers said Interested viewers can help MIT find the essence of horror on the website, or look at more of the pictures the Nightmare Machine has generated on Instagram. A normal photograph of St Basil's Cathedral in Moscow is pictured left. The Nightmare Machine team is making photographs of famous landmarks appear scary. In creating a network that works against itself, researchers believe it will eventually learn to be more precise in its output.

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = (V;E) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W and letting G(W) be the induced subgraph in G of the vertices in W. In addition to G(W), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that correctly estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhaustive set of experiments to study the effects of sample size, K, and SBM model parameters on the accuracy of the estimates. The experimental results also demonstrate that PULSE significantly outperforms a widely-used method called the network scale-up estimator in a wide variety of scenarios. We conclude with extensions and directions for future work.