"In the long term, harnessing AI and technology will determine the split between winners and losers," said Jeroen van Oerle, Portfolio Manager Robeco Fintech Equities, Robeco. "In order to keep relevant for the future, you need efficient back-office operations. On top of that, you need to be able to tailor make products. If you cannot provide those kinds of services in the future, a competitor will and you will lose." Similar to the largest US institutions, European banks have a strong interest in exploring the impact of AI on business functions.

Democracy is a solid form of government, and that's why it isn't particularly exciting. It's based on laws, on an independent judiciary and the separation of powers, all of which makes the system predictable. There is broad consensus in democracies that it is in everyone's best interest to agree on a set of rules that all must live by. There isn't much room left over for personal escapades and individual acts of heroism. But cults of personality are generally incompatible. Democracy always allows room for doubt, for divergent opinions and for criticism.

When Angelica Lim bakes macaroons, she has her own kitchen helper, Naoki. Her assistant is only good at the repetitive tasks, like sifting flour, but he makes the job more fun. Naoki is very cute, just under two feet tall. He's white, mostly, with blue highlights, and has speakers where his ears should be. The little round circle of a mouth that gives him a surprised expression is actually a camera, and his eyes are infrared receivers and transmitters. "I just love robots," said Lim in 2013, at the time a Ph.D. student in the Department of Intelligent Science and Technology at Kyoto University in Japan.

On April 19th, members of the Effect.AI team visited the Zero-In Conference in Amsterdam. They attended presentations on the subject of blockchain technology and met with several influencers in the blockchain space to discuss the Effect Network and possible collaborations. If you are interested in the speeches of the conference you can find the videos here. Switcheo, the first Multi-Chain Decentralized Exchange for NEP-5 tokens, added the EFX token to their platform just last week. Users are now able to to trade EFX with NEO, GAS and SWH instantly -- and from their own wallet files -- greatly reducing delays and fees associated with deposits and withdrawals.

One of the ways that intelligent computers and Artificial Intelligence (AI) platforms "think" is by analysing the relationships between and within large sets of data. Now, using a new type of Quantum Machine Learning (QML) algorithm, an international team have demonstrated that quantum computers can analyse a far wider array of data types than was previously expected. The details of the team's new "Quantum Linear System Algorithm," or QLSA, was published in Arvix, and in the future it could help crunch numbers on problems as varied as commodities pricing, social networks and chemical structures, and usher in a new era of Quantum AI. "Previous quantum algorithms only worked on very specific types of problem. We needed an upgrade if we want to achieve a quantum speed up for other data," said Zhikuan Zhao, who co-authored the paper, and that's exactly what he, and his colleagues, Anupam Prakash at the Centre for Quantum Technologies in Singapore, and Leonard Wossnig from ETH Zurich and the University of Oxford, have done. QLSA's were first proposed in 2009 by a different group of researchers and since then the idea's helped kick start research into new exotic forms of AI such as Quantum Artificial Intelligence (QAI), which gradually I'm seeing more and more research papers reference.

WASHINGTON – The European Union's executive branch, the European Commission, has announced that it will increase its investment in artificial intelligence (AI) research and development by €1.5 billion and called on member states to invest €20 billion as well. Congressman John K. Delaney (MD-6), the founder of the House AI Caucus, says that the European Union's action should get the attention of U.S. policymakers. "Our economic competitors in Europe and Asia are moving forward on AI, while we stand still. I sincerely hope that today's announcement from the EU gets the attention of Washington and serves as a wake-up call. If we want artificial intelligence technology to benefit our society, our economy and our workers, we've got to make sure that the United States remains the global leader – but make no mistake, we will have competition," said Congressman Delaney.

We propose a generic, Bayesian, information geometric approach to the exploration--exploitation trade-off in multi-armed bandit problems. Our approach, BelMan, uniformly supports pure exploration, exploration--exploitation, and two-phase bandit problems. The knowledge on bandit arms and their reward distributions is summarised by the barycentre of the joint distributions of beliefs and rewards of the arms, the \emph{pseudobelief-reward}, within the beliefs-rewards manifold. BelMan alternates \emph{information projection} and \emph{reverse information projection}, i.e., projection of the pseudobelief-reward onto beliefs-rewards to choose the arm to play, and projection of the resulting beliefs-rewards onto the pseudobelief-reward. It introduces a mechanism that infuses an exploitative bias by means of a \emph{focal distribution}, i.e., a reward distribution that gradually concentrates on higher rewards. Comparative performance evaluation with state-of-the-art algorithms shows that BelMan is not only competitive but can also outperform other approaches in specific setups, for instance involving many arms and continuous rewards.

Flying in dynamic, urban, highly-populated environments represents an open problem in robotics. State-of-the-art (SoA) autonomous Unmanned Aerial Vehicles (UAVs) employ advanced computer vision techniques based on computationally expensive algorithms, such as Simultaneous Localization and Mapping (SLAM) or Convolutional Neural Networks (CNNs) to navigate in such environments. In the Internet-of-Things (IoT) era, nano-size UAVs capable of autonomous navigation would be extremely desirable as self-aware mobile IoT nodes. However, autonomous flight is considered unaffordable in the context of nano-scale UAVs, where the ultra-constrained power envelopes of tiny rotor-crafts limit the on-board computational capabilities to low-power microcontrollers. In this work, we present the first vertically integrated system for fully autonomous deep neural network-based navigation on nano-size UAVs. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and deployed on a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. We discuss a methodology and software mapping tools that enable the SoA CNN presented in [1] to be fully executed on-board within a strict 12 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 94 mW on average - 1% of the power envelope of the deployed nano-aircraft.

Several recently proposed methods aim to learn conceptual space representations from large text collections. These learned representations asso- ciate each object from a given domain of interest with a point in a high-dimensional Euclidean space, but they do not model the concepts from this do- main, and can thus not directly be used for catego- rization and related cognitive tasks. A natural solu- tion is to represent concepts as Gaussians, learned from the representations of their instances, but this can only be reliably done if sufficiently many in- stances are given, which is often not the case. In this paper, we introduce a Bayesian model which addresses this problem by constructing informative priors from background knowledge about how the concepts of interest are interrelated with each other. We show that this leads to substantially better pre- dictions in a knowledge base completion task.

Electroencephalogram, an influential equipment for analyzing humans activities and recognition of seizure attacks can play a crucial role in designing accurate systems which can distinguish ictal seizures from regular brain alertness, since it is the first step towards accomplishing a high accuracy computer aided diagnosis system (CAD). In this article a novel approach for classification of ictal signals with wavelet based cross frequency coupling (CFC) is suggested. After extracting features by wavelet based CFC, optimal features have been selected by t-test and quadratic discriminant analysis (QDA) have completed the Classification.