For more than 30 years, Gibbs has advised on and developed product and service marketing for many businesses and he has consulted, lectured, and authored numerous articles and books. So the UK has just given itself a national headache. Whether you think the Brexit was the right decision or a dangerous and unmitigated screw-up (as I do), the consequences of the referendum will be non-trivial and take years to complete. But the mechanics of the UK exiting the European Union aside, the question of how people now feel about the Brexit is interesting. Are they awash in jubilation or has buyer's remorse set in?

Google is starting to piece together a cloud platform strategy beyond just being a lower-cost option than the competition, but it's a considerable risk, banking on a set of services many enterprises likely won't use for years to come. Machine learning and deep analytics are the latest trend to gain attention in the cloud market, and Google has latched on wholeheartedly -- it sees these tools a way to differentiate in the market, by externalizing what has driven it to be one of the largest corporations in the world. The most full-throated endorsement of this strategy emerged at the GCP Next user conference back in March. Eric Schmidt, chairman of Google parent company Alphabet Inc., talked in broad strokes about creating an internet operating system, adding that in five years, every major IPO will be for companies using machine learning. "The platform is not the end; it's the bottom, and above it is machine learning," Schmidt said.

Making Apple services even smarter and more personalized entails processing troves of information because intelligence is driven by big data. The fact that iOS 9's proactive features don't tap into the cloud has served Apple well thus far. But since Google Assistant came to light, people have been wondering if Apple can compete without resorting to raw data collection Google is infamous for. An en vogue statistical method, Differential Privacy helps Apple deliver smarter services without compromising privacy of their users. It's a relatively unproven technique with lots of potential which hasn't been used to boost Apple's services before iOS 10 and macOS Sierra.

ACCORDING to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Elon Musk and Nick Bostrom about "the singularity" -- when machines become smarter than humans -- have attracted millions of dollars and spawned a multitude of conferences. But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorized and advertised to. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologized; it was unintentional.

For the second time in a week, a robot in Russia that is programmed with advanced artificial intelligence as well as an ability to learn from experiences and about its surroundings has escaped the facility that it is housed in. A robot in Russia caused an unusual traffic jam last week after it "escaped" from a research lab, and now, the artificially intelligent bot is making headlines again after it reportedly tried to flee a second time, according to news reports. Engineers at the Russian lab reprogrammed the intelligent machine, dubbed Promobot IR77, after last week's incident, but the robot recently made a second escape attempt, The Mirror reported. Last week, the robot made it approximately 160 feet (50 meters) to the street, before it lost power and "partially paralyzed" traffic. The first time the robot escaped it was due to an improperly latched gate.

With the rise of social media, people can now form relationships and communities easily regardless of location, race, ethnicity, or gender. However, the power of social media simultaneously enables harmful online behavior such as harassment and bullying. Cyberbullying is a serious social problem, making it an important topic in social network analysis. Machine learning methods can potentially help provide better understanding of this phenomenon, but they must address several key challenges: the rapidly changing vocabulary involved in cyber- bullying, the role of social network structure, and the scale of the data. In this study, we propose a model that simultaneously discovers instigators and victims of bullying as well as new bullying vocabulary by starting with a corpus of social interactions and a seed dictionary of bullying indicators. We formulate an objective function based on participant-vocabulary consistency. We evaluate this approach on Twitter and Ask.fm data sets and show that the proposed method can detect new bullying vocabulary as well as victims and bullies.

High-dimensional linear classifiers, such as the support vector machine (SVM) and distance weighted discrimination (DWD), are commonly used in biomedical research to distinguish groups of subjects based on a large number of features. However, their use is limited to applications where a single vector of features is measured for each subject. In practice data are often multi-way, or measured over multiple dimensions. For example, metabolite abundance may be measured over multiple regions or tissues, or gene expression may be measured over multiple time points, for the same subjects. We propose a framework for linear classification of high-dimensional multi-way data, in which coefficients can be factorized into weights that are specific to each dimension. More generally, the coefficients for each measurement in a multi-way dataset are assumed to have low-rank structure. This framework extends existing classification techniques, and we have implemented multi-way versions of SVM and DWD. We describe informative simulation results, and apply multi-way DWD to data for two very different clinical research studies. The first study uses metabolite magnetic resonance spectroscopy data over multiple brain regions to compare patients with and without spinocerebellar ataxia, the second uses publicly available gene expression time-course data to compare treatment responses for patients with multiple sclerosis. Our method improves performance and simplifies interpretation over naive applications of full rank linear classification to multi-way data. An R package is available at https://github.com/lockEF/MultiwayClassification .

It is the future of shipping - and there's not a single sailor on board. Rolls Royce has revealed planed for fleets of'drone ships' to ferry carry around the world - all controlled from a central'holodeck'. It believes an entirely unmanned ship could take to the seas by 2020. Rolls Royce said it has already begun testing the technology needed to make the ships a reality, and expected them to take to the sea by the end of the decade. Cameras would beam 360-degree views from the drone ship back to operators based in a virtual bridge.

Should a self-driving car kill its passengers for the greater good – for instance, by swerving into a wall to avoid hitting a large number of pedestrians? Surveys of nearly 2,000 US residents revealed that, while we strongly agree that autonomous vehicles should strive to save as many lives as possible, we are not willing to buy such a car for ourselves, preferring instead one that tries to preserve the lives of its passengers at all costs. Driving our own cars might be a enjoyable pursuit, but it's also responsible for a tremendous amount of misery: it locks out the elderly and physically challenged and is the primary cause of death, worldwide, for people aged 15 to 29. Every year, over 30,000 traffic-related deaths and millions of injuries, costing close to a trillion dollars, take place in the US alone (worldwide, the numbers approach 1.25 million fatalities and 20 to 50 million injuries a year). And, according to numerous studies, human error has been responsible for at least a staggering 90 percent or more of these accidents.

Mrs. Gates made that statement at a recent ReCode conference, according to Bloomberg Technology. "The thing I want to say to everybody in the room is: We ought to care about women being in computer science," she said. "You want women participating in all of these things because you want a diverse environment creating AI and tech tools and everything we're going to use." Melinda Gates' comment came after her husband Bill Gates extolled the virtues of artificial intelligence. "Certainly, it's the most exciting thing going on," he said. It's the big dream that anybody who's ever been in computer science has been thinking about."