In Pyrrho Investments v MWB Business Exchange, Master Paul Matthews of the Chancery division supported the use of software in scoring documents for relevance. He found there was no evidence that software would be less accurate than manual review and keyword searches. He added that software could provide greater consistency in searching more than 3 million documents that could be involved in the disclosure. A final reason was that both sides had agreed to the use of the software, which would be much cheaper than a manual search – they just wanted the court's approval. However, in May, the High Court went further when two undisclosed parties disagreed on whether predictive coding software should be used.

According to the GMI Research report, "Global Artificial Intelligence Market By Solution, By Technology, By End-Use, and By Geography -- Opportunities & Forecast 2016–2025", the global artificial intelligence market was estimated to be USD 4,157.6 million in 2016 and is projected to reach USD 153,389.0 million by the end of 2025, exhibiting a CAGR of 49.3% during the forecast period. The key factors boosting the growth of the market are increasing adoption of artificial intelligence solutions across a broad range of industries, the rise in demand for analyzing and interpreting large amounts of data, as well as development and acceptance of technologically advanced artificial intelligence solutions for upgrading consumer services. On the Basis of Solution, Hardware Is Expected to Exhibit the Highest Cagr Through 2025 Surging demand from several industries such as retail, BFSI, healthcare, automotive and transportation, media and advertising, and manufacturing worldwide has led to a burgeoning market growth of hardware solution. This segment typically includes chipsets such as central processing units, graphics processing units, application specific integrated circuits, as well as the field-programmable gate array. Apart from this, the technological advancements, such as superior computing power, parallel computation capabilities, and more storage capacity in AI-enabled hardware products are further likely to drive the growth of this segment.

Zeger Vercouteren, VP Government Affairs and Policy for Europe, Middle East, and Africa, Johnson & Johnson, explains how artificial intelligence and machine learning could bring better and faster treatments to more patients. What if artificial intelligence (AI) could one day diagnose your illness before you even had symptoms? Or dramatically speed up drug discovery through machine learning? What if robots could learn to support surgeons to make surgical procedures more precise and effective? All of this is what we are already working to develop, and more.

Artificial intelligence will significantly change the ways we run our businesses and perform our work in the coming years. AI is also seen to carry a huge economic and growth potential. To capture that, Finland and Finnish organizations need to act now. This study explores how 20 leading Finnish organizations, both commercial and public, are embracing AI opportunities in practice to capture this potential. Additionally, we discuss the role AI is currently playing in Finnish business and public scenes and dig deeper into the practical use cases and current best practices identified to set the pace for the AI journey.

How can a drone be a life-saving object? Alfonso Zamarro, EENA's Drone Activities Manager, mentioned that the first phase brought great benefits for firefighters and search and rescue teams. Collaborating and making use of EENA efforts and emergency service, and DJI technical knowledge, they were able to notice how drones can help in such emergencies, and what all improvements it requires. "Phase II will go one step further and we need to study and understand its working," says Zamarro. What are the further steps of the program?

Data ScienceTech Institute is excited to launch 2 new programmes for the October 2018 entry! Discover our infographic "Which Applied MSc to choose"?

According to a press statement [insert link] published by the university, the four-year project will draw on a fleet of autonomous vehicles of sizes ranging from four to 12 places, and aims gather data reflecting the economic, logistic, and social implications of a self-driving network. Run in collaboration with local authorities and the Geneva public transport services (TPG), the trial hopes to collect information around three major axes: "autonomous driving" (the security and adaptability of vehicles), "optimization of itineraries" (the user experience), and "in- and out-of-vehicle services" (catering to reduced mobility passengers, for example). It will be conducted in suburban areas less frequently or conveniently serviced by existing routes. "Autonomous vehicles will not go downtown," said Denis Berdoz, CEO of TPG. Geneva, Switzerland's second-largest city, is particularly suited to such a trial because of the complex traffic situations it offers (a combination of jams, pedestrianized zones and bicycle lanes), as well as the well-mapped and GPS-ready nature of the region. The project has a budget of some €22 million (CHF26.28 million), €16 million of which are funded by the European Union's Horizon 2020 research financing mechanism. The following content is sourced from external partners. We cannot guarantee that it is suitable for the visually or hearing impaired.

How can we forecast, prevent, and (when necessary) mitigate the harmful effects of malicious uses of AI? This is the question posed by a 100-page report released last week, written by 26 authors from 14 institutions. The report, which is the result of a two-day workshop in Oxford, UK followed by months of research, provides a sweeping landscape of the security implications of artificial intelligence. The authors, who include representatives from the Future of Humanity Institute, the Center for the Study of Existential Risk, OpenAI, and the Center for a New American Security, argue that AI is not only changing the nature and scope of existing threats, but also expanding the range of threats we will face. They are excited about many beneficial applications of AI, including the ways in which it will assist defensive capabilities.

A parameterized skill is a mapping from multiple goals/task parameters to the policy parameters to accomplish them. Existing works in the literature show how a parameterized skill can be learned given a task space that defines all the possible achievable goals. In this work, we focus on tasks defined in terms of final states (goals), and we face on the challenge where the agent aims to autonomously acquire a parameterized skill to manipulate an initially unknown environment. In this case, the task space is not known a priori and the agent has to autonomously discover it. The agent may posit as a task space its whole sensory space (i.e. the space of all possible sensor readings) as the achievable goals will certainly be a subset of this space. However, the space of achievable goals may be a very tiny subspace in relation to the whole sensory space, thus directly using the sensor space as task space exposes the agent to the curse of dimensionality and makes existing autonomous skill acquisition algorithms inefficient. In this work we present an algorithm that actively discovers the manifold of the achievable goals within the sensor space. We validate the algorithm by employing it in multiple different simulated scenarios where the agent actions achieve different types of goals: moving a redundant arm, pushing an object, and changing the color of an object.

We develop a general method for estimating a finite mixture of non-normalized models. Here, a non-normalized model is defined to be a parametric distribution with an intractable normalization constant. Existing methods for estimating non-normalized models without computing the normalization constant are not applicable to mixture models because they contain more than one intractable normalization constant. The proposed method is derived by extending noise contrastive estimation (NCE), which estimates non-normalized models by discriminating between the observed data and some artificially generated noise. We also propose an extension of NCE with multiple noise distributions. Then, based on the observation that conventional classification learning with neural networks is implicitly assuming an exponential family as a generative model, we introduce a method for clustering unlabeled data by estimating a finite mixture of distributions in an exponential family. Estimation of this mixture model is attained by the proposed extensions of NCE where the training data of neural networks are used as noise. Thus, the proposed method provides a probabilistically principled clustering method that is able to utilize a deep representation. Application to image clustering using a deep neural network gives promising results.