Ready or not, autonomous robots are leaving laboratories to be tested in real-world contexts. With more and more people living in cities, these technologies offer ways to cope with ageing populations and poorly maintained infrastructures, while promoting safer transport, productive manufacturing and secure energy supplies. Urban "living labs" are one way scientists are trying to understand how autonomous robots – or Robotics and Autonomous Systems (RAS), to give them their full title – will affect our everyday lives. Autonomous robots are interconnected, interactive, cognitive and physical tools, which can perceive their environments, reason about events, make or revise plans and control their own actions. These technologies are designed to draw on big data and connect with the Internet of Things, to make our lives easier by increasing accuracy and efficiency.

Artificial intelligence-based sales acceleration software platform 19th Mile has raised $180,000 (Rs 1.2 crore) in angel investment from high-net-worth individuals, the company said in a media statement. The key investors include Rishi Vasudev, vice president of fashion at Flipkart; Excelsior Investments; Ritesh Vohra, partner at IDFC Real Estate Fund; Praveer Kumar, chief technology officer at payments solutions startup Finxera; Prashant Gupta, head of engineering at Finxera; and a few others, the statement added. Ltd, the company was founded 2015 by Vijay Gogoi, who worked at consultancy giant Accenture before starting up. An electronics engineer from National Institute of Technology, Rourkela and MBA from MDI, Gurgaon, Gogoi has 19 years of experience in management consulting, sales, talent development, and general management, and has been a technology entrepreneur for the past two years. The company will use the funds to strengthen its product, bolster its technology team, and will launch brand awareness initiatives in India and other international markets, the statement said.

A new deep learning approach can better discern changes in the eyes of glaucoma patients, according to a new study in Biomedical Optics Express. Glaucoma is a group of diseases that damage the eye's optic nerve and can cause vision loss, including blindness. Although there is no cure, early detection and treatment can delay its progression. The progression is marked by complex structural changes in the optic nerve head tissues, such as the thinning of retinal nerve fiber layers and the width of membranes. Current deep learning methods applied to optical coherence tomography, which uses light to take cross-section images, can detect these changes automatically, but existing methods require a different tissue-specific algorithm to examine each type of tissue.

THE GIST: Although media headlines imply we are already living in a future where AI has infiltrated every aspect of society, this actually sets unrealistic expectations about what AI can really do for humanity. Governments around the world are racing to pledge support to AI initiatives, but they tend to understate the complexity around deploying advanced machine learning systems in the real world. This article reflects on the risks of "AI solutionism": the increasingly popular belief that, given enough data, machine learning algorithms can solve all of humanity's problems. There is no AI solution for everything. All solutions come at a cost and not everything that can be automated should be.

Classifying the complexity of arithmetic computations is a crucial endeavour in theoretical computer science. Particularly interesting are the decidability and complexity issues pertaining to iterative arithmetic computations, i.e. computations consisting of a repeated application of some set of arithmetic operations on some initial value. Examples of such problems include matrix powering over various semirings [16, 9], the Skolem problem ("Does a given linear recurrent sequence contain a zero term?") and its variants [26, 27, 6], or the related Orbit problem [21, 4]. It is often natural to consider bounded or finite-horizon variants of the problems, which ask, given a time horizon H, whether a certain property holds within the first H iterations of the computation [9]. In this paper, we study the complexity of finite-horizon arithmetic computations arising in algorithmic decision making and operations research.

Implicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface features from the input sentence pairs. Thus, properly representing the text is very crucial to this task. In this paper, we propose a model augmented with different grained text representations, including character, subword, word, sentence, and sentence pair levels. The proposed deeper model is evaluated on the benchmark treebank and achieves state-of-the-art accuracy with greater than 48% in 11-way and $F_1$ score greater than 50% in 4-way classifications for the first time according to our best knowledge.

Abstract-- Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e. Some of these have been investigated by sub-disciplines of Artificial Intelligence (AI) including navigation & mapping, perception, knowledge representation & reasoning, planning, interaction, and learning. The different sub-disciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this paper, we survey and discuss AI techniques as'enablers' for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy. I. INTRODUCTION Robot technology has improved tremendously over the last decade. Consequently, autonomous robot systems have been able to operate in increasingly complex environments and for increasingly long periods of time, i.e. weeks, months, or years. When a fully modelled robot is deployed in a completely known, static environment, the challenge of long-term autonomy (LTA) reduces to one of robustness, i.e. enabling the robot to remain operational for as long as possible. Without these simplifying assumptions autonomous robots face a number of interrelated challenges. The first refers to the application requirements, e.g., the robot platform (hardware and software), environment and tasks to be performed.

Mathematically, ternary coding is more efficient than binary coding. It is little used in computation because technology for binary processing is already established and the implementation of ternary coding is more complicated, but remains relevant in algorithms that use decision trees and in communications. In this paper we present a new comparison of binary and ternary coding and their relative efficiencies are computed both for number representation and decision trees. The implications of our inability to use optimal representation through mathematics or logic are examined. Apart from considerations of representation efficiency, ternary coding appears preferable to binary coding in classification of many real-world problems of artificial intelligence (AI) and medicine. We examine the problem of identifying appropriate three classes for domain-specific applications. Keywords: optimal coding, decision trees, ternary logic, artificial intelligence Introduction The problem of optimal coding of numbers has been examined by many scholars (e.g.

Today, when asked about artificial intelligence (AI), many people start painting science fiction inspired images of machine-ruled futures and robots completing manual tasks for human beings. To them, AI is only a concept, something that's going to happen tomorrow. In reality, artificial intelligence is already part of our lives. We use AI every day. Is 2018 The Year That The AI Revolution Goes Mainstream?

The astonishing thing about stem cells is that they can be coaxed, in the laboratory, into becoming nearly any kind of cell--from bone marrow to heart muscle. That remarkable capability has for years kept scientists busy tinkering with stem cells and injecting them into animal models in an attempt to grow and replace damaged tissue. Such scientists have received a ton of attention in that line of work. But there's a smaller group of researchers working, to far less fanfare, on a different part of the stem cell challenge: how to deliver those cells to the body's hard-to-reach places. Researchers typically deliver stem cells via injection--a needle.