Clever crows can assemble tools from two or more components without any help, a feat previously seen only in humans and great apes. The birds were filmed slotting together rod pieces to create a tool long enough to extract a morsel of food which scientists had hidden away. In one experiment, they were presented with disassembled syringes, and created the right length of tool without any prompt or demonstration. The birds' ability to anticipate what an unseen object will be able to do matches the intelligence of a human toddler, Oxford University researchers said. The animals in the experiment were New Caledonian crows - a species native to a large Pacific island east of Australia of the same name.

Try to fool an artificial intelligence lie detector, discover innovative fashion made using rare pictures of the Antarctic and find out why 7,000 socks will be hanging in Central Library, as the UK's Manchester Metropolitan University joins the Manchester Science Festival 2018. The festival, which is in its 12th year, is organised by the Science and Industry Museum – with Manchester Met showcasing some of its most exciting projects and researchers in locations across the city. RELATED: Smart cities need to be more human, so we're creating Sims-style virtual worlds Scientists from Manchester Metropolitan will reveal their cutting-edge research, answer any burning questions about how science and technology will shape our futures, and give you the opportunity to take part in live hands-on science experiments. Interest in and focus on smart cities has skyrocketed in 2018, with a very large number of vendors from across the value chain repositioning and optimizing their IoT portfolios to take advantage of this beckoning opportunity. Members of the public are invited to attempt to get past iBorderCtrl – a virtual border guard that uses artificial intelligence to work out whether people are lying about who they are and why they are travelling.

Last week, a Pepper model robot was hailed as the first non-human to give evidence in Parliament, after it issued pre-recorded answers to members of the UK Education Select Committee during a session on the "Fourth Industrial Revolution". Rather than shedding light on the advance of robotics, this stunt further obscures it. It is not the first time I have seen this phenomenon. I began researching AI ethics at the Massachusetts Institute of Technology in 1993.

"AI has become a major sight of focus of interest, in the media and elsewhere," said Allan Dafoe, director of the governance of AI programme at the University of Oxford, at a hearing in the European Parliament. "Much of this interest is driven by hype and misunderstanding, but it is my belief that the critical insight that AI will be an important technology of this century is correct. In fact I would argue that how we build advanced AI will be the defining development of the 21st century," he added. It is clear that Europe is looking for a way to benefit from the advantages that AI can bring, but also that it will try to shape the development of AI to mitigate some of its possible negative effects. So why is AI such a prominently discussed topic all of a sudden?

As part of Lufthansa's ongoing global #SayYesToTheWorld brand campaign, it has become the first airline to launch a Watson Ads campaign, joining other brands such as Lego, Behr Paint, State Farm, Best Western, and TruGreen Lufthansa's AI-powered ad helps consumers plan their next international adventure in Europe, showcasing a specific location and helping a consumer explore the city by providing local travel facts, and tailored image galleries or videos. The campaign, which will run from 10/1 - 11/25, will help Lufthansa to discover new insights about consumers and their travel planning needs, which can inform future marketing efforts. The AI-powered ad is available to US consumers on weather.com, Featured cities include: Athens, Barcelona, Berlin, Budapest, Copenhagen, Florence, Frankfurt, Krakow, Milan, Munich, Oslo, Paris, Prague, Rome, and Stockholm. The campaign focuses on exploring a world of new possibilities and saying yes to the unknown. The interactive ads, powered by IBM Watson, provides consumers with the opportunity to interact with Lufthansa for general airline questions and for travel content across 15 European destinations.

One of the oldest Greek myths, the story of Pandora was first recorded more than 2,500 years ago, in the time of Homer. In the original telling, Pandora was not some innocent girl who succumbed to the temptation to open a forbidden jar. Rather, as the poet Hesiod tells us, Pandora was "made, not born." Having been commissioned by all-powerful Zeus and designed to his cruel specifications by Hephaestus, the god of invention, Pandora was a lifelike android created to look like a bewitching maiden. Her purpose was to entrap mortals as a manifestation of kalos kakon: "evil hidden in beauty."

The real opportunity for businesses is to blend AI with human effort,

Floating Content (FC) is a communication paradigm for the local dissemination of contextualized information through D2D connectivity, in a way which minimizes the use of resources while achieving some specified performance target. Existing approaches to FC dimensioning are based on unrealistic system assumptions that make them, highly inaccurate and overly conservative when applied in realistic settings. In this paper, we present a first step towards the development of a cognitive approach to efficient dynamic management of FC. We propose a deep learning strategy for FC dimensioning, which exploits a Convolutional Neural Network(CNN) to efficiently modulate over time the resources employed by FC in a QoS-aware manner. Numerical evaluations show that our approach achieves a maximum rejection rate of3%, and resource savings of 37.5% with respect to the benchmark strategy

Kernel methods on discrete domains have shown great promise for many challenging tasks, e.g., on biological sequence data as well as on molecular structures. Scalable kernel methods like support vector machines offer good predictive performances but they often do not provide uncertainty estimates. In contrast, probabilistic kernel methods like Gaussian Processes offer uncertainty estimates in addition to good predictive performance but fall short in terms of scalability. We present the first sparse Gaussian Process approximation framework on discrete input domains. Our framework achieves good predictive performance as well as uncertainty estimates using different discrete optimization techniques. We present competitive results comparing our framework to support vector machine and full Gaussian Process baselines on synthetic data as well as on challenging real-world DNA sequence data.

In open-ended and changing environments, agents face a wide range of potential tasks that may or may not come with associated reward functions. Such autonomous learning agents must be able to generate their own tasks through a process of intrinsically motivated exploration, some of which might prove easy, others impossible. For this reason, they should be able to actively select which task to practice at any given moment, to maximize their overall mastery on the set of learnable tasks. This paper proposes CURIOUS, an extension of Universal Value Function Approximators that enables intrinsically motivated agents to learn to achieve both multiple tasks and multiple goals within a unique policy, leveraging hindsight learning. Agents focus on achievable tasks first, using an automated curriculum learning mechanism that biases their attention towards tasks maximizing the absolute learning progress. This mechanism provides robustness to catastrophic forgetting (by refocusing on tasks where performance decreases) and distracting tasks (by avoiding tasks with no absolute learning progress). Furthermore, we show that having two levels of parameterization (tasks and goals within tasks) enables more efficient learning of skills in an environment with a modular physical structure (e.g. multiple objects) as compared to flat, goal-parameterized RL with hindsight experience replay.