"The connectivity that is the heart of globalisation can be exploited by states with hostile intent to further their aims.[…] The risks at stake are profound and represent a fundamental threat to our sovereignty." "It's not MI6's job to warn of internal threats. It was a very strange speech. Was it one branch of the intelligence services sending a shot across the bows of another? Or was it pointed at Theresa May's government? Does she know something she's not telling us?" Senior intelligence analyst, April 2017 In June 2013, a young American postgraduate called Sophie was passing through London when she called up the boss of a firm where she'd previously interned. The company, SCL Elections, went on to be bought by Robert Mercer, a secretive hedge fund billionaire, renamed Cambridge Analytica, and achieved a certain notoriety as the data analytics firm that played a role in both Trump and Brexit campaigns. But all of this was still to come. London in 2013 was still basking in the afterglow of the Olympics. Britain had not yet Brexited. The world had not yet turned. "That was before we became this dark, dystopian data company that gave the world Trump," a former Cambridge Analytica employee who I'll call Paul tells me. "It was back when we were still just a psychological warfare firm." Was that really what you called it, I ask him. Psychological operations – the same methods the military use to effect mass sentiment change.

Among the various products offered by Mitsubishi UFJ Kokusai Asset Management is AI Japan Equity Open, an investment trust launched at the beginning of February. As of March 31 it had attracted total investor assets of 10.7 billion yen ($98 million). Asked why it is selling so well, director Hideo Shirota said, "Many investors want their assets handled based on objective judgment rather than the professional sense of a fund manager." The company receives advice about the selection and timing of equity trades from Mitsubishi UFJ Trust Banking, where chief fund manager Noriyuki Okamoto, a man of over 20 years experience, and moreover, an artificial intelligence system are the brains behind the product. Arriving at the company's Tokyo head office at 8 every morning, he turns on his computer to read an AI-generated report on which equity prices will likely rise and the best timing to sell index futures.

You may wonder why a company would want to use supercomputers, or high-performance computing (HPC), to make shampoos. But that's exactly what a European cosmetics company did to get the right mix of materials that would make the shampoo smooth. "Nobody wants to buy shampoo that is not stable--one in which ingredients get segregated like in a salad dressing so you have to shake it before you can use it," said Dave Turek, who leads HPC (high performance computing) strategy at International Business Machines (IBM) Corp., which worked with the shampoo maker on devising a cost-efficient HPC solution. According to Turek, ordinarily, one would start the shampoo-making process by getting a laboratory to mix the ingredients (water, detergent, thickeners, etc.), testing them and checking the mixture's properties like how it "behaves, sitting on the shelf" of a bathroom. "This will teach you something about the ratio of materials, and then you do another set of experiments with another ratio and so on."

Elon Musk is attempting to combat the rise of artificial intelligence with the launch of his latest venture, brain-computer interface company Neuralink. In an interview with Wait Buy Why, Musk has revealed some of the details about his latest venture. He has confirmed that he will be the CEO of Neuralink – alongside the Tesla, and SpaceX leadership positions he already holds. Musk explained that the firm's goal is to turn cloud-based AI into an extension of the human brain. The company will aim to create (and bring to market) a product that can help those with severe brain injuries.

Data scientists are using machine learning to tackle lung cancer detection. Beginning in January, nearly 10,000 data scientists around the world competed in the Data Science Bowl to develop the most effective algorithm to help medical professionals detect lung cancer earlier and with better accuracy. In 2010, the National Lung Screening Trial showed that annual screening with low-dose computed tomography (CT) -- a scanner that uses computer-processed combinations of many X-ray images from different angles to generate high-contrast 3D images -- could reduce lung cancer deaths by 20 percent. While a breakthrough for early detection, the technology has also resulted in a relatively high rate of false positives compared with more traditional X-rays. An anonymized high-res lung scan from the NCI, which Data Science Bowls participants used when developing algorithms.

Technically Incorrect offers a slightly twisted take on the tech that's taken over our lives. Nothing good to say about Google Home and Amazon Echo. Perhaps you like the idea of a box in your living room that talks to you, turns the lights on for you, yet still can't make you breakfast. It seems, though, that Apple's SVP of worldwide marketing, Phil Schiller, isn't impressed with the ones he's seen so far. "Well, I won't talk to either one specifically, [I] don't want to. My mother used to have a saying that if you don't have something nice to say, say nothing at all," he said.

The Vector Institute in Toronto has only been around for a few weeks, and it is already attracting top machine learning talent from Silicon Valley. "Geopolitics is playing a part, as people contacting us have said'I don't want to live in the U.S.,'" Jordan Jacobs, co-founder of the Vector Institute, told CNBC. The Trump administration's recent battles over immigration have made some people feel unwelcome, and others more hesitant to move to the U.S. to do research in artificial intelligence. "People have been emailing and calling to ask: When can I send my application?" Raquel Urtasun, co-founder of the Vector Institute, told CNBC.

It is of fundamental importance to find algorithms obtaining optimal performance for learning of statistical models in distributed and communication limited systems. Aiming at characterizing the optimal strategies, we consider learning of Gaussian Processes (GPs) in distributed systems as a pivotal example. We first address a very basic problem: how many bits are required to estimate the inner-products of Gaussian vectors across distributed machines? Using information theoretic bounds, we obtain an optimal solution for the problem which is based on vector quantization. Two suboptimal and more practical schemes are also presented as substitute for the vector quantization scheme. In particular, it is shown that the performance of one of the practical schemes which is called per-symbol quantization is very close to the optimal one. Schemes provided for the inner-product calculations are incorporated into our proposed distributed learning methods for GPs. Experimental results show that with spending few bits per symbol in our communication scheme, our proposed methods outperform previous zero rate distributed GP learning schemes such as Bayesian Committee Model (BCM) and Product of experts (PoE).

Google has challenged China's top Go player to a series of games against its artificial intelligence technology. It said the software would play a best-of-three match against Ke Jie, among other games against humans in the eastern Chinese city of Wuzhen from 23-27 May. Last year, the Google program recorded a 4-1 victory against one of South Korea's top Go players. One expert said that result had come as a surprise. "A lot of AI researchers have been working on Go because it's the most challenging board game we have," said Calum Chace, author of Surviving AI. "The conventional wisdom was that machines would ultimately triumph but it would take 10 years or so. "The win was a big wake-up call for a lot of people, including many outside the AI community." Google's AlphaGo software was developed by British computer company DeepMind, which was bought by the US search firm in 2014. Its defeat of Lee Se-dol in March 2016 is seen as a landmark moment, similar to that of IBM's Deep Blue AI beating Garry Kasparov at chess in 1997. Several of the moves AlphaGo made defied conventional wisdom but ended up paying off. However, many Go aficionados did not recognise Mr Lee as the world's top player at the time of the contest. So, the new competition against 19-year-old Mr Ke - who is the current number one according to a popular but unofficial player-ranking system - has the potential to bring additional prestige to Google. "We've been hard at work improving AlphaGo to become even more creative, and since playing Lee Se-dol, the program has continued to learn through self-play training," a spokeswoman for DeepMind told the BBC. "We intend to publish more scientific papers in the future, which will include further details of AlphaGo's progress." Google added that Mr Lee would also be invited, but was not sure if he would attend. Over the past year, DeepMind's technology has also been used to find ways to reduce energy bills at Google's data centres as well as to try to improve care in British hospitals. A fresh wave of positive publicity could help Google find further uses for its tech. "If it loses this match, a lot of people will be delighted to claim that Google and DeepMind has overpromised and that this is the kind of hype we always get with AI," commented Mr Chace. "But I wouldn't have thought Google is taking a huge risk.

To provide you with this exclusive report, MyPrivateBanking has partnered with BI Intelligence, Business Insider's premium research service, to create The Complete Robo Advisor Research Collection. If you're involved in the financial services industry at any level, you simply must understand the paradigm shift caused by robo advisors. Investors frustrated by mediocre investment performance, high wealth manager fees and deceptive sales techniques are signing up for automated investment accounts at a record pace. And the robo advisor field is evolving right before our eyes. Firms are figuring out on the fly how to best attract, service and upsell their customers. What lessons are they learning?