Unless you've been living under a rock (which will likely be affected by climate change soon, by the way), you know that between forest fires, illegal deforestation, poaching, and other crimes, an enormity of environmental issues puts our ecosystems in danger. According to the National Science Foundation, a century ago, more than 60,000 tigers roamed in the wild. Now, there are as few as 3,000 remaining. While human patrols can directly protect endangered animals, many protection agencies lack the resources necessary to cover the appropriate amount of ground, especially in large national parks where many of these illicit activities might occur. In 2011, Eve McDonald-Madden and her colleagues at the University of Queensland in Australia lamented that a lack of money limits the impact that management strategies can have on preventing the extinction of a species.

For almost 20 years, Lionhead Studios was a beacon of the UK games industry. In a medium where big budgets tend to shrink ambitions, here was a group of experimenters, inventors, and craftspeople who always produced something curious, whether that was creative oddity The Movies or the hugely successful Fable series. Formed in Guildford in 1996, the studio was independent for a decade before Microsoft acquired it. Another decade later, on 31 April 2016, the lights were turned off for the final time. Lionhead made games with big choices, and it was ended by a cruel one. For much of its history, the studio was synonymous with Peter Molyneux, the idiosyncratic game designer who co-founded seminal Guildford studio Bullfrog in the 1990s. There, he oversaw a string of classic sim titles – Populous, Powermonger, Syndicate, Theme Park – before selling up to Electronic Arts in 1995.

There is so much doom and gloom associated with robots and jobs it is time to add some common sense to the misunderstandings created by so called experts opinions about robots and jobs – thankfully authors from the OECD may have added some clarity to the debate -- 'finding that on average, across the 21 OECD countries, '9% of jobs rather than 47%, as proposed by Frey and Osborne face a high automatibility.' Capitalism, the term for our global'free' markets, is a uniquely future-oriented economic system in which people invest, make innovations, apply for patents, and in other ways bet on the future. Behind all of this we find the hallmark of humanity, which is our creative intelligence. It is intelligence that drives these investments and innovations, and intelligence that forges within many of us an intense curiosity of what the future may hold. It is also intelligence that forges in others an anxiety over what the future holds. For many the future is no longer a promise but a threat!

Wordnets are an effective resource for natural language processing and information retrieval, especially for semantic processing and meaning related tasks. So far, wordnets have been constructed for many languages. However, the automatic development of wordnets for low-resource languages has not been well studied. In this paper, an Expectation-Maximization algorithm is used to create high quality and large scale wordnets for poorresource languages. The proposed method benefits from possessing cross-lingual word sense disambiguation and develops a wordnet by only using a bi-lingual dictionary and a monolingual corpus. The proposed method has been executed with Persian language and the resulting wordnet has been evaluated through several experiments. The results show that the induced wordnet has a precision score of 90% and a recall score of 35%.

Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual world cloning method, and validate our approach by building and publicly releasing a new video dataset, called Virtual KITTI (see http://www.xrce.xerox.com/Research-Development/Computer-Vision/Proxy-Virtual-Worlds), automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow. We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance. As the gap between real and virtual worlds is small, virtual worlds enable measuring the impact of various weather and imaging conditions on recognition performance, all other things being equal. We show these factors may affect drastically otherwise high-performing deep models for tracking.

We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performance depends on a parameter called the graph coherence. On the contrary, the second strategy is adaptive but yields optimal results. Indeed, no more than O(k log(k)) measurements are sufficient to ensure an accurate and stable recovery of all k-bandlimited signals. This second strategy is based on a careful choice of the sampling distribution, which can be estimated quickly. Then, we propose a computationally efficient decoder to reconstruct k-bandlimited signals from their samples. We prove that it yields accurate reconstructions and that it is also stable to noise. Finally, we conduct several experiments to test these techniques.

CTO Corner is BITS's monthly publication covering emerging trends and technologies in the financial services industry. Artificial Intelligence (AI), defined as the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages, has been around for over 60 years.1 In his 1950 paper "Computing Machinery and Intelligence," Alan Turing opens with: "I propose to consider the question'can machines think?'"2 He proposed a test of a machine's ability to exhibit intelligent behavior, equivalent to, or indistinguishable from, that of a human being, which is now known as the Turing Test.3 AI as an academic discipline began at the famous 1955 Dartmouth conference organized by John McCarthy from Stanford University and Marvin Minsky from MIT.4 This CTO Corner explores both the potential for AI to transform the financial services industry and challenges it presents.

In December 2015 Leman Solicitors hosted the Future of Law event for 100 corporate counsel. We spoke about IBM's Watson which is being used to build A.I. applications in several sectors, notably ROSS in law. It's part of a disruption that we say is going to fundamentally change the way legal services are delivered. Another article in today's Business Insider UK reports: "Ask ROSS to look up an obscure court ruling from 13 years ago, and ROSS will not only search for the case in an instant -- without contest or complaint -- but it'll offer opinions in plain language about the old ruling's relevance to the case at hand." The article can be found here.

Robots can paint, but when it comes to writing, they shouldn't quit their day jobs. That's the combined conclusion from results of two contests announced this week. On the upside, artificial intelligence created some pretty impressive works for RobotArt.org's The contest challenged artists and engineers to create a robot that painted like a real artist. Essentially, the aim was to get "as many teams as possible to set up a robot that can do any sort of painting," the contest site explains.

DeepMind acquired startup by Google for 500M established a new collaboration with University of Oxford. The news is announced by Demis Hassabis, co-founder of DeepMind and VP of engineering at Google from a blog-post [1]. Deep learning researchers Prof Nando de Freitas, Prof Phil Blunsom, Dr Edward Grefenstette and Dr Karl Moritz Hermann, from University of Oxford, who teamed up earlier this year to co-found Dark Blue Labs, are hired by DeepMind. Also Dr Karen Simonyan, Max Jaderberg and Prof Andrew Zisserman, one of the world's foremost experts on computer vision systems, and they recently have a start-up called Vision Factory will join DeepMind from University of Oxford[1,2]. The three professors hired by DeepMind are holding joint appointments at Oxford University where they will continue to spend part of their time.