Beware all the hype about artificial intelligence. In fact, experts say, the technology is still in its formative stages – and so Europe still has an opportunity to overtake an early American and Chinese lead in the field. Though news reports of artificial intelligence can sound space-age, in fact the underlying technology of "the current AI is very old," he says. "It is about 30 years old and it is really stupid," he told a workshop on AI at a conference on innovation in the EU organised by Science Business. "The next generation will be the big revolution. We haven't seen anything yet," Myllymäki added, explaining that the next wave of AI systems will help knowledge workers retrieve information, enabling them to focus on more creative tasks.

The world has a fake news problem. And with the speed and scale of false information being spread across the internet, it can seem impossible to stop. Artificial intelligence could be a way to slow its spread--and help stop companies profiting from intentional or accidental dissemination. The method will be complex but the idea is simple. AI can be trained to identify fake news and gather data on sites that are the most prolific in peddling it--or just sloppy in their fact checking, experts say.

Put "female football fan" into a search engine and the image results are a stream of attractive young women in tight shirts and, sometimes, no shirts. Tired of sexualisation and misrepresentation, some fans have decided it's time to level the playing field. "These images represent everything that's great about this game - how many different kinds of women go to matches and support. We need this realness," says Emma Townley, from online community This Fan Girl. She's talking about five images of female England fans taken ahead of this World Cup, which she hopes will start replacing photos of scantily-clad female fans in search engine results.

HANGZHOU, China/SHANGHAI, June 28 (Reuters) - In the eastern Chinese city of Hangzhou, an ambulance speeds through traffic on a wave of green lights, helped along by an artificial intelligence (AI) system and big data. The system, which involves sending information to a centralised computer linked to the city's transport networks, is part of a trial by Alibaba Group Holding Ltd. The Chinese tech giant is hoping to use its cloud and data systems to tackle issues hobbling China's healthcare system like snarled city traffic, long patient queues and a lack of doctors. Alibaba's push into healthcare reflects a wider trend in China, where technology firms are racing to shake up a creaking state-run health sector and take a slice of spending that McKinsey & Co estimates will hit $1 trillion by 2020. Tencent-backed WeDoctor, which offers online consultations and doctor appointments, raised $500 million in May at a valuation of $5.5 billion.

With AI technology on the rise, Generation Alpha children may never experience a doctor's appointment without the presence of medical artificial intelligence (AI). As an IEEE report revealed, their millennial parents are growing more comfortable with the technology. IEEE's study found that millennial parents across the globe are becoming increasingly comfortable with AI health technology for their Generation Alpha children. Most respondents noted that they would have "at least some trust" in AI tech. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) Globally, 56% of respondents noted that they had a "great deal of trust" in AI technologies for diagnosing and treating their sick children.

HANGZHOU, China/SHANGHAI (Reuters) - In the eastern Chinese city of Hangzhou, an ambulance speeds through traffic on a wave of green lights, helped along by an artificial intelligence (AI) system and big data. The system, which involves sending information to a centralized computer linked to the city's transport networks, is part of a trial by Alibaba Group Holding Ltd. The Chinese tech giant is hoping to use its cloud and data systems to tackle issues hobbling China's healthcare system like snarled city traffic, long patient queues and a lack of doctors. Alibaba's push into healthcare reflects a wider trend in China, where technology firms are racing to shake up a creaking state-run health sector and take a slice of spending that McKinsey & Co estimates will hit $1 trillion by 2020. Tencent-backed WeDoctor, which offers online consultations and doctor appointments, raised $500 million in May at a valuation of $5.5 billion.

UK-based healthcare startup Babylon Healthcare has claimed its artificial intelligence (AI) software, in tests, can assess common conditions more accurately than human doctors. The startup said its AI correctly answered 81% of diagnostic questions designed to become a qualified doctor in the UK. Founded in 2013, Babylon aims to offer health advice by using AI through a smartphone app.

For many of us, meetings are a boring waste of time but technology could soon help make them more interesting and productive. What do you do during a boring meeting? I canvassed some opinions on Twitter and the results were enlightening. Some people compose haikus, others play meeting bingo, seeing how many pre-agreed words they can chuck in to the conversation. Some secretly check out Grindr on their phones or watch catch-up TV, while others fiddle with their jewellery, doodle, or simply nod off.

Suppose you could merely imagine a computation, and a digital prostheses, an extension of your biological brain, would turn it into code that instantly realizes what you had in mind. Imagine looking at an image, dataset or set of equations and wanting to analyze and explore its meaning as an artistic whim or part of a scientific investigation. I don't mean you would use an existing software suite to produce a standard visualization, but rather you would make use of an extensive repository of existing code to assemble a new program analogous to how a composer draws upon a repertoire of musical motifs, themes and styles to construct new works, and tantamount to having a talented musical amanuensis who, in addition to copying your scores, takes liberties with your prior work, making small alterations here and there and occasionally adding new works of its own invention, novel but consistent with your taste and sensibilities. Perhaps the interaction would be wordless and you would express your objective by simply focusing your attention and guiding your imagination, the prostheses operating directly on patterns of activation arising in your primary sensory, proprioceptive and associative cortex that have become part of an extensive vocabulary that you now share with your personal digital amanuensis. Or perhaps it would involve a conversation conducted in subvocal, unarticulated speech in which you specify what it is you want to compute and your assistant asks questions to clarify your intention and the two of you share examples of input and output to ground your internal conversation in concrete terms. More than thirty years ago, Charles Rich and Richard Waters published an MIT AI Lab technical report [68] entitled The Programmer's Apprentice: A Research Overview. Whether they intended it or not, it would have been easy in those days for someone to misremember the title and inadvertently refer to it as "The Sorcerer's Apprentice" since computer programmers at the time were often characterized as wizards and most children were familiar with the Walt Disney movie Fantasia, featuring music written by Paul Dukas inspired by Goethe's poem of the same name

A main puzzle of deep neural networks (DNNs) revolves around the apparent absence of "overfitting", defined in this paper as follows: the expected error does not get worse when increasing the number of neurons or of iterations of gradient descent. This is surprising because of the large capacity demonstrated by DNNs to fit randomly labeled data and the absence of explicit regularization. Recent results by Srebro et al. provide a satisfying solution of the puzzle for linear networks used in binary classification. They prove that minimization of loss functions such as the logistic, the cross-entropy and the exp-loss yields asymptotic, "slow" convergence to the maximum margin solution for linearly separable datasets, independently of the initial conditions. Here we prove a similar result for nonlinear multilayer DNNs near zero minima of the empirical loss. The result holds for exponential-type losses but not for the square loss. In particular, we prove that the normalized weight matrix at each layer of a deep network converges to a minimum norm solution (in the separable case). Our analysis of the dynamical system corresponding to gradient descent of a multilayer network suggests a simple criterion for predicting the generalization performance of different zero minimizers of the empirical loss. This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216.