Showing papers on "Applications of artificial intelligence published in 2017"

Artificial intelligence in healthcare: past, present and future

Abstract: Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

Artificial intelligence in medicine

Abstract: Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.

Imitation Learning: A Survey of Learning Methods

Abstract: Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions.

Applications of artificial intelligence in intelligent manufacturing: a review

Abstract: Based on research into the applications of artificial intelligence (AI) technology in the manufacturing industry in recent years, we analyze the rapid development of core technologies in the new era of ‘Internet plus AI’, which is triggering a great change in the models, means, and ecosystems of the manufacturing industry, as well as in the development of AI. We then propose new models, means, and forms of intelligent manufacturing, intelligent manufacturing system architecture, and intelligent manufacturing technology system, based on the integration of AI technology with information communications, manufacturing, and related product technology. Moreover, from the perspectives of intelligent manufacturing application technology, industry, and application demonstration, the current development in intelligent manufacturing is discussed. Finally, suggestions for the application of AI in intelligent manufacturing in China are presented.

Artificial Intelligence: Foundations of Computational Agents

Abstract: Recent decades have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents. By showing how basic approaches fit into a multidimensional design space, readers can learn the fundamentals without losing sight of the bigger picture. The book balances theory and experiment, showing how to link them intimately together, and develops the science of AI together with its engineering applications. Although structured as a textbook, the book's straightforward, self-contained style will also appeal to a wide audience of professionals, researchers, and independent learners. AI is a rapidly developing field: this book encapsulates the latest results without being exhaustive and encyclopedic. It teaches the main principles and tools that will allow readers to explore and learn on their own. The text is supported by an online learning environment, AIspace, http://aispace.org, so that students can experiment with the main AI algorithms plus problems, animations, lecture slides, and a knowledge representation system, AIlog, for experimentation and problem solving.

A Review of Deep Learning Methods Applied on Load Forecasting

Abstract: The utility industry has invested widely in smart grid (SG) over the past decade. They considered it the future electrical grid while the information and electricity are delivered in two-way flow. SG has many Artificial Intelligence (AI) applications such as Artificial Neural Network (ANN), Machine Learning (ML) and Deep Learning (DL). Recently, DL has been a hot topic for AI applications in many fields such as time series load forecasting. This paper introduces the common algorithms of DL in the literature applied to load forecasting problems in the SG and power systems. The intention of this survey is to explore the different applications of DL that are used in the power systems and smart grid load forecasting. In addition, it compares the accuracy results RMSE and MAE for the reviewed applications and shows the use of convolutional neural network CNN with k-means algorithm had a great percentage of reduction in terms of RMSE.

Ternary neural networks for resource-efficient AI applications

Abstract: The computation and storage requirements for Deep Neural Networks (DNNs) are usually high. This issue limits their deployability on ubiquitous computing devices such as smart phones, wearables and autonomous drones. In this paper, we propose ternary neural networks (TNNs) in order to make deep learning more resource-efficient. We train these TNNs using a teacher-student approach based on a novel, layer-wise greedy methodology. Thanks to our two-stage training procedure, the teacher network is still able to use state-of-the-art methods such as dropout and batch normalization to increase accuracy and reduce training time. Using only ternary weights and activations, the student ternary network learns to mimic the behavior of its teacher network without using any multiplication. Unlike its {-1,1} binary counterparts, a ternary neural network inherently prunes the smaller weights by setting them to zero during training. This makes them sparser and thus more energy-efficient. We design a purpose-built hardware architecture for TNNs and implement it on FPGA and ASIC. We evaluate TNNs on several benchmark datasets and demonstrate up to 3.1 χ better energy efficiency with respect to the state of the art while also improving accuracy.

Standardizing Ethical Design for Artificial Intelligence and Autonomous Systems

Abstract: AI is here now, available to anyone with access to digital technology and the Internet. But its consequences for our social order aren't well understood. How can we guide the way technology impacts society?

Artificial Intelligence Techniques in Smart Grid and Renewable Energy Systems—Some Example Applications

Abstract: Artificial intelligence (AI) techniques, such as expert systems (ESs), fuzzy logic (FL), and artificial neural networks (ANNs or NNWs) have brought an advancing frontier in power electronics and power engineering. These techniques provide powerful tools for design, simulation, control, estimation, fault diagnostics, and fault-tolerant control in modern smart grid (SG) and renewable energy systems (RESs). The AI technology has gone through fast evolution during last several decades, and their applications have increased rapidly in modern industrial systems. This special issue will remain incomplete without some discussion on AI applications in SG and RESs. The paper will discuss some novel application examples of AI in these areas. These applications are automated design of modern wind generation system and its health monitoring in the operating condition, fault pattern identification of an SG subsystem, and control of SG based on real-time simulator. The concepts of these application examples can be expanded to formulate many other applications. In the beginning of the paper, the basic features of AI that are relevant to these applications have been briefly reviewed.

Medical Education Must Move From the Information Age to the Age of Artificial Intelligence.

Abstract: Noteworthy changes coming to the practice of medicine require significant medical education reforms. While proposals for such reforms abound, they are insufficient because they do not adequately address the most fundamental change-the practice of medicine is rapidly transitioning from the information age to the age of artificial intelligence. Increasingly, future medical practice will be characterized by: the delivery of care wherever the patient happens to be; the provision of care by newly constituted health care teams; the use of a growing array of data from multiple sources and artificial intelligence applications; and the skillful management of the interface between medicine and machines. To be effective in this environment, physicians must work at the top of their license, have knowledge spanning the health professions and care continuum, effectively leverage data platforms, focus on analyzing outcomes and improving performance, and communicate the meaning of the probabilities generated by massive amounts of data to patients, given their unique human complexities. The authors believe that a "reboot" of medical education is required that makes better use of the findings of cognitive psychology and pays more attention to the alignment of humans and machines in education and practice. Medical education needs to move beyond the foundational biomedical and clinical sciences. Systematic curricular attention must focus on the organization of professional effort among health professionals, the use of intelligence tools involving large data sets, and machine learning and robots, all the while assuring the mastery of compassionate care.

Internet of skills, where robotics meets AI, 5G and the Tactile Internet

Abstract: Capitalizing on the latest developments in 5G and ultra-low delay networking as well as Artificial Intelligence (AI) and robotics, we advocate here for the emergence of an entirely novel Internet which will enable the delivery of skills in digital form. We outline the technical challenges which need to be overcome to enable such a vision, i.e., on the development of a 5G Tactile Internet, standardized haptic codecs, and AI to enable the perception of zero delay networks. The paper is concluded with an overview on the current capabilities, and the standardization initiatives in the IEEE 5G Tactile Internet standards working group as well as the IEEE 5G Initiative.

Challenges and opportunities: from big data to knowledge inAI 2.0

Abstract: In this paper, we review recent emerging theoretical and technological advances of artificial intelligence (AI) in the big data settings. We conclude that integrating data-driven machine learning with human knowledge (common priors or implicit intuitions) can effectively lead to explainable, robust, and general AI, as follows: from shallow computation to deep neural reasoning; from merely data-driven model to data-driven with structured logic rules models; from task-oriented (domain-specific) intelligence (adherence to explicit instructions) to artificial general intelligence in a general context (the capability to learn from experience). Motivated by such endeavors, the next generation of AI, namely AI 2.0, is positioned to reinvent computing itself, to transform big data into structured knowledge, and to enable better decision-making for our society.

Current trends in the development of intelligent unmanned autonomous systems

Abstract: Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.

The AI detectives.

Abstract: Deep neural networks, or deep learning, as the field is also called, have the potential to revolutionize scientific discovery. But as these networks are applied to more and more disciplines, many scientists, whose very enterprise is founded on explanation, have been left with a nagging question: Why, model, why? This interpretability problem is galvanizing a new generation of researchers in both industry and academia. Just as the microscope revealed the cell, these researchers are crafting tools that will allow insight into how neural networks make decisions. Some tools probe the artificial intelligence (AI) without penetrating it; some are alternative algorithms that can compete with neural nets, but with more transparency; and some use still more deep learning to get inside the black box. Taken together, they add up to a new discipline. Some call it "AI neuroscience."

Moral decision making frameworks for artificial intelligence

Abstract: The generality of decision and game theory has enabled domain-independent progress in AI research. For example, a better algorithm for finding good policies in (PO)MDPs can be instantly used in a variety of applications. But such a general theory is lacking when it comes to moral decision making. For AI applications with a moral component, are we then forced to build systems based on many ad-hoc rules? In this paper we discuss possible ways to avoid this conclusion.

Intelligence on tap: artificial intelligence as a new design material

Abstract: Through a combination of factors, AI has recently made significant progress and is now integrated in many successful products. In the future, AI will become available as a resource to use by non-experts — intelligence on tap. Interaction designers need to consider AI as a new design material, with its own unique opportunities and limitations.

Us vs. Them: Understanding Artificial Intelligence Technophobia over the Google DeepMind Challenge Match

Abstract: Various forms of artificial intelligence (AI), such as Apple's Siri and Google Now, have permeated our everyday lives. However, the advent of such "human-like" technology has stirred both awe and a great deal of fear. Many consider it a woe to have an unimaginable future where human intelligence is exceeded by AI. This paper investigates how people perceive and understand AI with a case study of the Google DeepMind Challenge Match, a Go match between Lee Sedol and AlphaGo, in March 2016. This study explores the underlying and changing perspectives toward AI as users experienced this historic event. Interviews with 22 participants show that users tacitly refer to AlphaGo as an "other" as if it were comparable to a human, while dreading that it would come back to them as a potential existential threat. Our work illustrates a confrontational relationship between users and AI, and suggests the need to prepare for a new kind of user experience in this nascent socio- technological change. It calls for a collaborative research effort from the HCI community to study and accommodate users for a future where they interact with algorithms, not just interfaces.

A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

Abstract: Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learning algorithms. Papers published from 2007 to 2017 were reviewed and keywords were used to identify 20 articles spanning the most prominent machine learning algorithms. Most articles reviewed consisted of training data obtained from sensors attached to the equipment. The training of the machine learning algorithm consisted of designed experiments to simulate different faulty and normal processing conditions. The areas of application varied from wear of cutting tool in computer numeric control (CNC) machine, surface roughness fault, to wafer etching process in semiconductor manufacturing. In all cases, high fault classification rates were obtained. As the interest in smart manufacturing increases, this review serves to address one of the cornerstones of emerging production systems.

Virtual assistants and self-driving cars

Abstract: Self-driving cars are technologically a reality and in the next decade they are expected to reach the highest level of automation. While there is general agreement that an advanced human-autonomous vehicle (HAV) interaction is key to achieve the benefits of self-driving cars, it is less clear what role artificial intelligence (AI) should play in this context. While the scientific community is debating on the role and intersections of AI, autonomous vehicles and related issues, above all ethics, the automotive industry is already presenting AI-based products and services that may influence, in a direction or in another, our technological and societal futures. This paper focuses on virtual assistants, the personification of the car intelligence incorporating, among others, an algorithmic “brain”, a synthetic human “voice” and powerful sensor-based “senses”. Should virtual assistants just assist humans or replace them whenever necessary? Should their scope of action be limited to safety-related driving tasks or to any activity performed in the car or controlled from the car? Although at a very early stage of commercial development, the paper will review the state-of-the-art of in-car virtual assistants underlining their role and functions in the connected and automated driving ecosystem. By drawing from earlier reflections on automation, robots and intelligent agents, it will then identify a series of issues to be addressed by the scientific community, policy-makers and the automotive industry stakeholders.

An intentional AI for hanabi

Abstract: Cooperative games with partial observability are a challenging domain for AI research, especially when the AI should cooperate with a human player. In this paper we investigate one such game, the award-winning card game Hanabi, which has been studied by other researchers before. We present an agent designed to play better with a human cooperator than these previous results by basing it on communication theory and psychology research. To demonstrate that our agent performs better with a human cooperator we ran an experiment in which 224 participants played one or more games of Hanabi with different AIs, and will show that our AI scores higher than previously published work in such a setting.

Impact of Robotics, RPA and AI on the insurance industry: challenges and opportunities

Abstract: We consider the current challenges and opportunities in applications of Robotics to financial services and to insurance in particular. Combinations of Robot Process Automation (RPA) with digitization have been considered by the industry, with important benefits in cost reduction and efficiency. We highlight the general benefits of RPA and the related implementation challenges in detail. We discuss more advanced Artificial Intelligence (AI) applications, arguing that such applications depend on the general advancements of AI, where human level interaction is not yet available. We discuss the great potential for AI applications in the near future and consider some initial examples. We also briefly discuss the hard problems of AI in relation to intelligence and consciousness in the introductory part, and briefly look at the implications AI and robots could have for human society and employment.

Ray: A Distributed Framework for Emerging AI Applications

Abstract: The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray---a distributed system to address them. Ray implements a unified interface that can express both task-parallel and actor-based computations, supported by a single dynamic execution engine. To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state. In our experiments, we demonstrate scaling beyond 1.8 million tasks per second and better performance than existing specialized systems for several challenging reinforcement learning applications.

AI Glossary: Artificial intelligence, in so many words

Abstract: Just what do people mean by artificial intelligence (AI)? The term has never had clear boundaries. When it was introduced at a seminal 1956 workshop at Dartmouth College, it was taken broadly to mean making a machine behave in ways that would be called intelligent if seen in a human. An important recent advance in AI has been machine learning, which shows up in technologies from spellcheck to self-driving cars and is often carried out by computer systems called neural networks. Any discussion of AI is likely to include other terms as well. We present a glossary of key words and phrases.

Imagining Artificial Intelligence Applications with People with Visual Disabilities using Tactile Ideation

Abstract: There has been a surge in artificial intelligence (AI) technologies co-opted by or designed for people with visual disabilities. Researchers and engineers have pushed technical boundaries in areas such as computer vision, natural language processing, location inference, and wearable computing. But what do people with visual disabilities imagine as their own technological future? To explore this question, we developed and carried out tactile ideation workshops with participants in the UK and India. Our participants generated a large and diverse set of ideas, most focusing on ways to meet needs related to social interaction. In some cases, this was a matter of recognizing people. In other cases, they wanted to be able to participate in social situations without foregrounding their disability. It was striking that this finding was consistent across UK and India despite substantial cultural and infrastructural differences. In this paper, we describe a new technique for working with people with visual disabilities to imagine new technologies that are tuned to their needs and aspirations. Based on our experience with these workshops, we provide a set of social dimensions to consider in the design of new AI technologies: social participation, social navigation, social maintenance, and social independence. We offer these social dimensions as a starting point to forefront users' social needs and desires as a more deliberate consideration for assistive technology design.

Building AI Applications: Yesterday, Today, and Tomorrow

Abstract: AI applications have been deployed and used for industrial, government, and consumer purposes for many years. The experiences have been documented in IAAI conference proceedings since 1989. Over the years, the breadth of applications has expanded many times over and AI systems have become more commonplace. Indeed, AI has recently become a focal point in the industrial and consumer consciousness. This article focuses on changes in the world of computing over the last three decades that made building AI applications more feasible. We then examine lessons learned during this time and distill these lessons into succinct advice for future application builders.

The Minds of Many: Opponent Modeling in a Stochastic Game

Abstract: The Theory of Mind provides a framework for an agent to predict the actions of adversaries by building an abstract model of their strategies using recursive nested beliefs. In this paper, we extend a recently introduced technique for opponent modelling based on Theory of Mind reasoning. Our extended multi-agent Theory of Mind model explicitly considers multiple opponents simultaneously. We introduce a stereotyping mechanism, which segments the agent population into sub-groups of agents with similar behaviour. Sub-group profiles guide decision making. We evaluate our model using a multi-player stochastic game, which presents agents with the challenge of unknown adversaries in a partially-observable environment. Simulation results demonstrate that the model performs well under uncertainty and that stereotyping allows larger groups of agents to be modelled robustly. The findings show that Theory of Mind modelling is useful in many artificial intelligence applications.