Transforming Artificial Intelligence to Emotional Intelligence: What does the new evolution of AI mean for the financial services industry? Speakers: Mr. Greg Cross, Chief Business Officer, Soul Machines, presents ground breaking work followed by Q&A from the audience.

Just hours after the leaked image of a keychain accessory revealed the game, Ubisoft has posted a quick teaser for Assassin's Creed: Odyssey. With only five seconds of video, the teaser serves as a quick callback to the movie 300 as a Spartan-looking warrior boots his enemy off of a cliff, before flashing the game's logo with a familiar helmet in the middle. Along with the leak, an insider told JeuxvideoLive that this game follows Assassin's Creed: Origins with a trip to ancient Greece, where there's plenty of heroic tales, battles and mythology to mine. Kotaku reports other sources claiming that it should arrive during Ubisoft's fiscal 2019 period, and that it will include even more RPG-style elements than the last game. We'll find out more during E3

Artificial intelligence (AI) can more accurately identify cancerous skin cells than experienced doctors, research from the European Society for Medical Oncology (ESMO) shows. Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address.

Today's rapid pace of technological change has fundamentally transformed the global outsourcing scene, making outsourcing part of every successful company's strategy. Properly developed, strategic outsourcing substantially lowers costs, risks, and fixed investments while greatly expanding flexibility, innovative capabilities, and opportunities for creating higher value-added and shareholder returns. Traditionally, the main driving factor behind IT & BPO outsourcing was cost-reduction. But lately, apart from cost-reduction, global companies outsource to access knowledge, talent, innovation and expertise that is available and ready to be put into use. It's a knowledge economy, and in knowledge economies global companies gain access to global capabilities and access global knowledge with the aim to stay current, innovate or transform their companies.

Waymo, Alphabet's self-driving car company (formerly under Google), has been using modified vans for years to privately test its autonomous tech. In late 2016, Waymo announced that its first production car would be a modified Chrysler Pacifica. Now the company is looking to buy a few more minivans - 62,000, to be exact. Alphabet announced today that it struck a deal with Chrysler to buy 62,000 additional Pacifica hybrid vans for Waymo, increasing the company's number of vehicles by 100 times. Waymo is already testing a small fleet of completely autonomous taxis in Arizona.

On 25 April 2018, a new Communication was published that sets out the European Commission's (EC's) new strategy to boost Europe's artificial intelligence (AI) capabilities and related industries, while at the same time preparing for socioeconomic changes emanating from these emerging technologies. The Communication also poses questions as to whether – and, if so, where and how – the European legal and ethical framework needs to be adapted due to the advent of AI. The EC refers to AI as "systems that show intelligent behaviour by analysing their environment, and performing various tasks with some degree of autonomy to achieve specific goals."1 European leaders are considering AI as a top priority. On 10 April, 24 member states2 and Norway co-signed a Declaration which commits them to working together on AI.

Stochastic differential equations (SDEs) and the Kolmogorov partial differential equations (PDEs) associated to them have been widely used in models from engineering, finance, and the natural sciences. In particular, SDEs and Kolmogorov PDEs, respectively, are highly employed in models for the approximative pricing of financial derivatives. Kolmogorov PDEs and SDEs, respectively, can typically not be solved explicitly and it has been and still is an active topic of research to design and analyze numerical methods which are able to approximately solve Kolmogorov PDEs and SDEs, respectively. Nearly all approximation methods for Kolmogorov PDEs in the literature suffer under the curse of dimensionality or only provide approximations of the solution of the PDE at a single fixed space-time point. In this paper we derive and propose a numerical approximation method which aims to overcome both of the above mentioned drawbacks and intends to deliver a numerical approximation of the Kolmogorov PDE on an entire region $[a,b]^d$ without suffering from the curse of dimensionality. Numerical results on examples including the heat equation, the Black-Scholes model, the stochastic Lorenz equation, and the Heston model suggest that the proposed approximation algorithm is quite effective in high dimensions in terms of both accuracy and speed.

Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domain-specific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for NMT.

Musical performance requires prediction to operate instruments, to perform in groups and to improvise. We argue, with reference to a number of digital music instruments (DMIs), including two of our own, that predictive machine learning models can help interactive systems to understand their temporal context and ensemble behaviour. We also discuss how recent advances in deep learning highlight the role of prediction in DMIs, by allowing data-driven predictive models with a long memory of past states. We advocate for predictive musical interaction, where a predictive model is embedded in a musical interface, assisting users by predicting unknown states of musical processes. We propose a framework for characterising prediction as relating to the instrumental sound, ongoing musical process, or between members of an ensemble. Our framework shows that different musical interface design configurations lead to different types of prediction. We show that our framework accommodates deep generative models, as well as models for predicting gestural states, or other high-level musical information. We apply our framework to examples from our recent work and the literature, and discuss the benefits and challenges revealed by these systems as well as musical use-cases where prediction is a necessary component.

Learning a generative model is a key component of model-based reinforcement learning. Though learning a good model in the tabular setting is a simple task, learning a useful model in the approximate setting is challenging. Recently Farahmand et al. (2017) proposed a value-aware (VAML) objective that captures the structure of value function during model learning. Using tools from Lipschitz continuity, we show that minimizing the VAML objective is in fact equivalent to minimizing the Wasserstein metric.