A scene from'Minecraft: Story Mode,' which launches later this year. It could be "game over" for Minecraft fans who downloaded unauthorized mods (modifications) for their Android smartphone or tablet. Instead of finding new content or tools to tweak the wildly popular Minecraft: Pocket Edition mobile game, more than 80 malicious apps -- disguised as Minecraft mods -- contained Trojans that bombarded users with advertisements or redirected them to scam websites, says ESET, a Slovakia-based cybersecurity company. Lukas Stefanko, the malware researcher who discovered the fake mods, says there have been nearly 1 million downloads of the malicious apps from the Google Play store. "Users often fall for phony apps because they're promising to deliver something new for a famous game like Minecraft, plus many have positive – but fabricated – ratings," says Stefanko, in an interview with USA TODAY.

Reuters news agency reported on 16th February 2017 that "European lawmakers called...for EU-wide legislation to regulate the rise of robots, including an ethical framework for their development and deployment and the establishment of liability for the actions of robots including self-driving cars." The question of determining'liability' for decision making achieved by robots or artificial intelligence is an interesting and important subject as the implementation of this technology increases in industry, and starts to more directly impact our day to day lives. Indeed, as application of Artificial Intelligence and machine learning technology grows, we are likely to witness how it changes the nature of work, businesses, industries and society. And yet, although it has the power to disrupt and drive greater efficiencies, AI has its obstacles: the issue of'who is liable when something goes awry' being one of them. Like many protagonists in industry, Members of the European Parliament (MEPs) are trying to tackle this liability question.

As many as 30% of UK jobs could be at risk of automation by the year 2030. According to PwC's UK Economic Outlook report, as many as 30% of UK jobs could be affected by automation in the next 15 years. However, the report found that instead of entirely replacing UK jobs, robotics and AI will force change in the nature of jobs – meaning that these jobs will not disappear. The spread of automation should be offset by gains elsewhere in the economy, with the report forecasting productivity gains from the adoption of the technology. PwC estimates that automation will vary wildly between different sectors.

It is now table stakes for artificial intelligence algorithms to "learn" about the world around them. The next level: For AI bots to learn how to talk to each other -- and develop their own shared language. New research released last week by OpenAI, the artificial intelligence nonprofit lab founded by Elon Musk and Y Combinator president Sam Altman, details how they're training AI bots to create their own language, based on trial and error, as the bots move around a set environment. This is different from how artificial intelligence algorithms typically learn -- using large sets of data, like to recognize a dog by taking in thousands of pictures of dogs. The world the researchers created for the AI bots to learn in is a computer simulation of a simple, two-dimensional white square.

As many as 30pc of existing roles in the UK could be automated by 2030 with the most at risk industries being waste management, transportation and manufacturing, according to an analysis by PwC. However, the report stressed that automation won't result in rocketing unemployment. "The UK employment rate is at its highest level now since comparable records began in 1971, despite advances in digital and other labour-saving technologies," said John Hawksworth, chief economist at PwC. Mr Hawksworth anticipates that manual and routine tasks will be susceptible to automation, with social skills and creative roles being more protected. "That said, no industry is entirely immune from future advances in robotics and AI," he said.

Bayesian inference for complex models is challenging due to the need to explore high-dimensional spaces and multimodality and standard Monte Carlo samplers can have difficulties effectively exploring the posterior. We introduce a general purpose rejection-free ensemble Markov Chain Monte Carlo (MCMC) technique to improve on existing poorly mixing samplers. This is achieved by combining parallel tempering and an auxiliary variable move to exchange information between the chains. We demonstrate this ensemble MCMC scheme on Bayesian inference in Factorial Hidden Markov Models. This high-dimensional inference problem is difficult due to the exponentially sized latent variable space. Existing sampling approaches mix slowly and can get trapped in local modes. We show that the performance of these samplers is improved by our rejection-free ensemble technique and that the method is attractive and "easy-to-use" since no parameter tuning is required.

A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN). There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional method which we call Smart Augmentation and we show how to use it to increase the accuracy and reduce overfitting on a target network. Smart Augmentation works by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss. This allows us to learn augmentations that minimize the error of that network. Smart Augmentation has shown the potential to increase accuracy by demonstrably significant measures on all datasets tested. In addition, it has shown potential to achieve similar or improved performance levels with significantly smaller network sizes in a number of tested cases.

Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data. However, IRL remains mostly unexplored for multi-agent systems. In this paper, we show how the principle of IRL can be extended to homogeneous large-scale problems, inspired by the collective swarming behavior of natural systems. In particular, we make the following contributions to the field: 1) We introduce the swarMDP framework, a sub-class of decentralized partially observable Markov decision processes endowed with a swarm characterization. 2) Exploiting the inherent homogeneity of this framework, we reduce the resulting multi-agent IRL problem to a single-agent one by proving that the agent-specific value functions in this model coincide. 3) To solve the corresponding control problem, we propose a novel heterogeneous learning scheme that is particularly tailored to the swarm setting. Results on two example systems demonstrate that our framework is able to produce meaningful local reward models from which we can replicate the observed global system dynamics.

We cover many emerging markets in the startup ecosystem. Previously, we published posts that summarized Financial Technology, Internet of Things, Bitcoin, and MarTech in six visuals. This week, we do the same with Artificial Intelligence (AI). At this time, we are tracking 855 AI companies across 13 categories, with a combined funding amount of $8.75billion. To see all of our AI related posts, check out our blog!

The latest issue of the IEEE Robotics & Automation Magazine features a special report on open-source robotics hardware and its impact in the field. We've seen how, over the last several years, open source software--platforms like the Robot Operating System (ROS), Gazebo, and OpenCV, among others--has played a huge role in helping researchers and companies build robots better and faster. Can the same thing happen with robot hardware? It's already happening, says robotics researcher and RAM editor-in-chief Bram Vanderborght, who explains that building hardware has gotten much easier thanks to things like 3D printers, laser cutters, modular open electronics kits, and other rapid prototyping and fabrication techniques. And while "open-source robotics hardware is taking longer to catch on" compared to open-source robotics software, he notes that "several impressive examples exist, taking advantage of benefits of those novel rapid prototyping possibilities."