The incredible possibilities artificial intelligence (AI) presents - and also its potential challenges - have made it an important talking point. Of course, there are a lot of myths out there about AI. Some like Alibaba founder Jack Ma said AI is not only a massive threat to jobs but could also spark World War III. Not only Ma, opinions are divided in the industry on whether AI is a job killer or a job creator. According to a recent report published by MIT Technology Review, Artificial Intelligence will not exactly eliminate jobs, but it will deconstruct them in the near future.

People have a weird obsession with games likes Chess and Go. Achievement in them has long been seen as a marker of human intellect, and yet they're among the least human test you could devise; putting players in simplified situations where everything is known, every possible course of action is laid out for them, and the test is one of concentration and logic. We pass far greater tests daily, when we recognise a face in a crowd, when we dynamically balance in motion, when we predict the response our words and expressions will have on another sentient being, or when we do all of the above, effortlessly, at the same time. We don't think of these as challenging because they're so innately human, while playing Chess or Go seems far more impressive precisely because they're more rigid and computational in nature. There's an irony in making a board game one of the'grand challenges' of AI, and it surprises me that more people don't see it.

Forty years ago, NASA launched twin robotic explorers on a mission to travel farther out than any spacecraft had gone before, and today, they continue to be our most distant emissaries. The story of those probes, and of the people behind them, is the focus of the aptly-titled documentary, "The Farthest," airing Wednesday (Aug. The Voyager probes, referred to by numerical designators "1" and "2," revealed the outer planets of our solar system and then continued to sail beyond. Voyager 2, which was the first to launch on Aug. 20, 1977, visited Jupiter, Saturn, Uranus and Neptune. Voyager 1 departed Earth on Sep. 5, 1977, overtook its counterpart, and was the first to arrive at Jupiter and Saturn.

Along with autonomous cars, virtual reality, and drones, artificial intelligence (AI) has been one of the key areas of tech that is generating huge amounts of excitement. From the much hyped triumphs of Google's DeepMind, to those very handy recommendations on Skype and Netflix, us regular human folk are only seeing the beginnings of what AI holds in store in the next few years. AI is also taking huge strides in helping people manage their finances, which is particularly important when looking at the state of savings in the UK. The figures are pretty grim. This year the savings ratio, which is the amount a household saves as a proportion of its income, reached its lowest level since records began.

At last, a fully operational urban delivery drone system is here... only you probably won't get to use it. Drone logistics startup Flytrex has teamed up with Iceland's main online retailer, AHA, to launch a courier drone service in Reykjavik. Specifically, it's serving one part of Reykjavik -- robotic fliers carry food across a river in the city, cutting the delivery time from 25 minutes to 4. That doesn't sound like a whole lot, but it could make a big difference both in terms of getting your food quickly and cutting back on delivery costs. This undoubtedly comes across as a publicity grab. The delivery drones aren't carrying large packages, and it's relatively easy to handle a modestly-sized city with few big buildings.

Protecting people rather than property or animals will be the priority under pioneering new German legal guidelines for the operation of driverless cars, the transport ministry said on Wednesday. Germany is home to some of the world's largest car companies, including Volkswagen, Daimler and BMW, all of which are investing heavily in self-driving technology. German regulators have been working on rules for how such vehicles should be programmed to deal with a dilemma, such as choosing between hitting a cyclist or accelerating beyond legal speeds to avoid an accident. Mercedes EQ electric car concept: German regulators have been working on rules for how such vehicles should be programmed to deal with a dilemma, such as choosing between hitting a cyclist or accelerating beyond legal speeds to avoid an accident. When an accident is unavoidable, the software must choose whichever action will hurt people the least, even if that means destroying property or hitting animals in the road, a transport ministry statement showed.

We present ease.ml, a declarative machine learning service platform we built to support more than ten research groups outside the computer science departments at ETH Zurich for their machine learning needs. With ease.ml, a user defines the high-level schema of a machine learning application and submits the task via a Web interface. The system automatically deals with the rest, such as model selection and data movement. In this paper, we describe the ease.ml architecture and focus on a novel technical problem introduced by ease.ml regarding resource allocation. We ask, as a "service provider" that manages a shared cluster of machines among all our users running machine learning workloads, what is the resource allocation strategy that maximizes the global satisfaction of all our users? Resource allocation is a critical yet subtle issue in this multi-tenant scenario, as we have to balance between efficiency and fairness. We first formalize the problem that we call multi-tenant model selection, aiming for minimizing the total regret of all users running automatic model selection tasks. We then develop a novel algorithm that combines multi-armed bandits with Bayesian optimization and prove a regret bound under the multi-tenant setting. Finally, we report our evaluation of ease.ml on synthetic data and on one service we are providing to our users, namely, image classification with deep neural networks. Our experimental evaluation results show that our proposed solution can be up to 9.8x faster in achieving the same global quality for all users as the two popular heuristics used by our users before ease.ml.

Research on interference has provided evidence that the formation of dependencies between non-adjacent words relies on a cue-based retrieval mechanism. Two different models can account for one of the main predictions of interference, i.e., a slowdown at a retrieval site, when several items share a feature associated with a retrieval cue: Lewis and Vasishth's (2005) activation-based model and McElree's (2000) direct access model. Even though these two models have been used almost interchangeably, they are based on different assumptions and predict differences in the relationship between reading times and response accuracy. The activation-based model follows the assumptions of ACT-R, and its retrieval process behaves as a lognormal race between accumulators of evidence with a single variance. Under this model, accuracy of the retrieval is determined by the winner of the race and retrieval time by its rate of accumulation. In contrast, the direct access model assumes a model of memory where only the probability of retrieval varies between items; in this model, differences in latencies are a by-product of the possibility and repairing incorrect retrievals. We implemented both models in a Bayesian hierarchical framework in order to evaluate them and compare them. We show that some aspects of the data are better fit under the direct access model than under the activation-based model. We suggest that this finding does not rule out the possibility that retrieval may be behaving as a race model with assumptions that follow less closely the ones from the ACT-R framework. We show that by introducing a modification of the activation model, i.e, by assuming that the accumulation of evidence for retrieval of incorrect items is not only slower but noisier (i.e., different variances for the correct and incorrect items), the model can provide a fit as good as the one of the direct access model.

We propose a new PAC-Bayesian bound and a way of constructing a hypothesis space, so that the bound is convex in the posterior distribution and also convex in a trade-off parameter between empirical performance of the posterior distribution and its complexity. The complexity is measured by the Kullback-Leibler divergence to a prior. We derive an alternating procedure for minimizing the bound. We show that the bound can be rewritten as a one-dimensional function of the trade-off parameter and provide sufficient conditions under which the function has a single global minimum. When the conditions are satisfied the alternating minimization is guaranteed to converge to the global minimum of the bound. We provide experimental results demonstrating that rigorous minimization of the bound is competitive with cross-validation in tuning the trade-off between complexity and empirical performance. In all our experiments the trade-off turned to be quasiconvex even when the sufficient conditions were violated.

Despite the enormous changes in recent years, including the emergence of the plethora of significant new market players – including fintech start-ups, established payment, technology, and information firms, telecoms, and other providers, the financial services market is indeed in the middle of a revolution and this is down to both technology and regulations. This begs the question of how financial services incumbents will fare is far from settled and offers a scenario wherein these incumbents looking to grow shareholder value will need to build and sustain new competitive advantages. The European Commission's revised Payment Service Directive (PSD2) represents a broad sweep of financial services sector regulations that will come into force next year. In summary, PSD2 creates the opportunity for digital actors to link directly into payment systems via API's. The regulation will require that banks provide these API's so that third-party service providers will be able to directly access customers' accounts.