As human beings, we use our voices for conversation. When we interact with voice interfaces, therefore, our natural instinct is to apply the same rules that we would to a human conversation. We expect to be understood, but more than this, we expect the entity we're conversing with to remember the history of our conversation and understand the context of any following remarks. For some time, major search companies like Google and Bing have worked to teach their search engines to understand queries in natural language. Natural language search queries are queries that sound natural spoken aloud, such as, "How high is the Empire State building?" They often begin with question words ("When…?" "How…?" "Why…?"), contain stop words ("a", "the", "of", "for") and full sentences.

If that headline fails to get your attention, there's nothing about artificial intelligence (AI) or robotic process automation (RPA) that will shock or scare you. Will AI and RPA take your job and kill your career? It depends on what you do for a living, how old you are, where you live and your educational credentials. If you perform routine tasks or even what appear to be complex deductive inferential tasks we associate with "knowledge" industries, yes, your job and career are at significant risk: AI and RPA will absolutely, positively threaten your job, your career and your very professional existence. It would be naïve and irresponsible for me to say otherwise – just as it was naïve and irresponsible to tell horse breeders and coach manufacturers that automobiles were no threat at all, or minicomputer manufacturers that desktop computers would not threaten their markets – because, you know, "there is no reason for any individual to have a computer in his home." Let's also not believe for a moment that automation and low-level AI – combined with lower global labor costs – have been quiet over the past couple of decades.

Technology experts have stressed the need for schools to prepare students for tomorrow's jobs by increasingly teaching them artificial intelligence (AI) skills to an adequate level. Necip Ozyucel, Cloud and Enterprise Business Group Lead, Microsoft Gulf, said the latest World Economic Forum's'Future of Jobs' report, released in January this year showed that two in three children starting school this year are destined for professional roles yet to be created. "There is an urgent need to recognise that many of the jobs referred to by the World Economic Forum, are supported by artificial intelligence," Ozyucel told Khaleej Times in an exclusive interview. A recent survey by McKinsey & Company on specialist education practice - where 2,000 students, 2,000 teachers and 70 thought leaders across the Americas, Europe and Asia including the Gulf region were interviewed - found that students would be better prepared for'future jobs' if they shored up their soft skills, particularly the social and emotional. And only 42 per cent of employers consider today's graduates as having developed those attributes to an adequate level, according to Ozyucel, "When trying to determine the best approaches to bridge these soft-skills gaps, there is evidence in both studies that students and teachers alike favour collaborative learning scenarios. Mentor-led class discussions or group-learning scored consistently high as the most effective methods of learning," said Ozyucel.

Last month's E3 video game expo was a strange one for PlayStation. Traditionally, video games' biggest companies use the annual Los Angeles conference as an opportunity to bombard attendees and fans watching online with new games. For rival consoles Xbox and PlayStation, ostentatious showcase press events are especially important and competitive: once a customer chooses between the two, they are likely to play on that console for years to come. Microsoft showed 50 games at its Xbox press conference at E3 2018, on a big stage with lots of fancy lighting. By contrast, Sony constructed several elaborate sets on an LA film lot, each themed around a different forthcoming PlayStation 4 games: The Last of Us Part 2, Ghost of Tsushima, Spider-Man and Death Stranding.

By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question answering, and sentiment analysis. However, for agents to intelligently interact with humans, simply capturing the statistical patterns is insufficient. In this paper we investigate if, and how, grounded compositional language can emerge as a means to achieve goals in multi-agent populations. Towards this end, we propose a multi-agent learning environment and learning methods that bring about emergence of a basic compositional language. This language is represented as streams of abstract discrete symbols uttered by agents over time, but nonetheless has a coherent structure that possesses a defined vocabulary and syntax. We also observe emergence of non-verbal communication such as pointing and guiding when language communication is unavailable.

We study the problem of incorporating risk while making combinatorial decisions under uncertainty. We formulate a discrete submodular maximization problem for selecting a set using Conditional-Value-at-Risk (CVaR), a risk metric commonly used in financial analysis. While CVaR has recently been used in optimization of linear costs functions in robotics, we take the first stages towards extending this to discrete submodular optimization and provide several positive results. Specifically, we propose the Sequential Greedy Algorithm that provides an approximation guarantee on finding the maxima of the CVaR cost function under a matroidal constraint. The approximation guarantee shows that the solution produced by our algorithm is within a constant factor of the optimal and an additive term that depends on the optimal. Our analysis uses the curvature of the submodular set function, and proves that the algorithm runs in polynomial time. This formulates a number of combinatorial optimization problems that appear in robotics. We use two such problems, vehicle assignment under uncertainty for mobility-on-demand and sensor selection with failures for environmental monitoring, as case studies to demonstrate the efficacy of our formulation.

In this project, we developed a deep learning system applied to human retina images for medical diagnostic decision support. The retina images were provided by EyePACS (Eyepacs, LLC). These images were used in the framework of a Kaggle contest (Kaggle INC, 2017), whose purpose to identify diabetic retinopathy signs through an automatic detection system. Using as inspiration one of the solutions proposed in the contest, we implemented a model that successfully detects diabetic retinopathy from retina images. After a carefully designed preprocessing, the images were used as input to a deep convolutional neural network (CNN). The CNN performed a feature extraction process followed by a classification stage, which allowed the system to differentiate between healthy and ill patients using five categories. Our model was able to identify diabetic retinopathy in the patients with an agreement rate of 76.73% with respect to the medical expert's labels for the test data.

Cooperation of a large number of possibly small-sized robots, called robotic swarm, will play a significant role in complex missions that existing operational concepts using a few large robots could not deal with [1]. Even if every single robot (or called agent) in a swarm is incapable of accomplishing a task alone, their cooperation will lead to successful outcomes [2]-[5]. The possible applications include environmental monitoring [6], ad-hoc network relay [7], disaster management [8], cooperative radar jamming [9], to name a few. Due to the large cardinality of a swarm robot system, however, it is infeasible for human operators to supervise each agent directly, but needed to entrust the swarm with certain levels of decision-makings (e.g., task allocation, path planning, and individual control). Thereby, what only remains is to provide a high-level mission description, which is manageable for a few or even a single human operator. Nevertheless, there still exist various challenges in the autonomous decisionmaking of robotic swarms. Among them, this paper addresses a task allocation problem where the number of agents is higher than that of tasks: how to partition a set of agents into subgroups and assign the subgroups to each task.

Emotions are intimately tied to motivation and the adaptation of behavior, and many animal species show evidence of emotions in their behavior. Therefore, emotions must be related to powerful mechanisms that aid survival, and, emotions must be evolutionary continuous phenomena. How and why did emotions evolve in nature, how do events get emotionally appraised, how do emotions relate to cognitive complexity, and, how do they impact behavior and learning? In this article I propose that all emotions are manifestations of reward processing, in particular Temporal Difference (TD) error assessment. Reinforcement Learning (RL) is a powerful computational model for the learning of goal oriented tasks by exploration and feedback. Evidence indicates that RL-like processes exist in many animal species. Key in the processing of feedback in RL is the notion of TD error, the assessment of how much better or worse a situation just became, compared to what was previously expected (or, the estimated gain or loss of utility - or well-being - resulting from new evidence). I propose a TDRL Theory of Emotion and discuss its ramifications for our understanding of emotions in humans, animals and machines, and present psychological, neurobiological and computational evidence in its support.

The first ever computer manual which was written by a Victorian woman 175 years ago has been sold at auction for nearly £100,000 - nearly 20 times its expected price. The edition, 'Sketch of the Analytical Engine by by L.F. Menabrea with notes by Ada Lovelace', was snapped up by an anonymous buyer after being sold by Moore Allen & Innocent in Cirencester, Glocestershire. Ada, the only legitimate child of poet Lord Byron, was a maths prodigy who died aged 36. A rare book by Ada Lovelace, the Victorian woman renowned as the world's first computer programmer has sold at auction for £95,000 During her short life, she played a key role in the early development of computer programming, becoming friends with mathematician Charles Babbage over his automatic mechanical calculator, the Difference Engine. Lovelace played a key role in the 1843 book'Sketch of the Analytical Engine', of which just seven copies are thought to exist - one of which sold at auction for £95,000.