If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Azeem Azhar is a strategist, product entrepreneur, and analyst living in London. He is the curator of the weekly newsletter Exponential View, from which the following is adapted. This is the first year I am presenting predictions for the coming year. I've received some incredibly helpful comments from readers via Twitter. This has encouraged me to stick my head above the parapet.
At a recent conference in 2017, Microsoft CEO Satya Nadella used the analogy of a corn maze to explain the difference in approach between a classical computer and a quantum computer. In trying to find a path through the maze, a classical computer would start down a path, hit an obstruction, backtrack; start again, hit another obstruction, backtrack again until it ran out of options. Although an answer can be found, this approach could be a very time-consuming. They take every path in the corn maze simultaneously." Thus, leading to an exponential reduction in the number of steps required to solve a problem.
Inspired by this model, we propose a framework for implementing human activity recognition on mobile devices. In this area, the mobile app is usually always on and the general challenge is how to balance accuracy and energy consumption. However, among existing approaches, those based on cellular IDs consume little power but are less accurate; those based on GPS/Wi-Fi sampling are accurate often at the costs of battery drainage; moreover, previous methods in general do not improve over time. To address these challenges, our framework consists of two modes: In the deliberation mode, the system learns cell ID patterns that are trained by existing GPS-/Wi-Fi-based methods; in the intuition mode, only the learned cell ID patterns are used for activity recognition, which is both accurate and energy efficient; system parameters are learned to control the transition from deliberation to intuition, when sufficient confidence is gained, and the transition from intuition to deliberation, when more training is needed. For the scope of this paper, we first elaborate our framework in a subproblem in activity recognition, trip detection, which recognizes significant places and trips between them.
Each political decision in fact implies some form of social reactions, it affects economic and financial aspects and has substantial environmental impacts. Improving decision making in this context could have a huge beneficial impact on all these aspects. There are a number of Artificial Intelligence techniques that could play an important role in improving the policy-making process such as decision support and optimization techniques, game theory, data and opinion mining and agent-based simulation. We outline here some potential use of AI technology as it emerged by the European Union (EU) EU FP7 project ePolicy: Engineering the Policy Making Life Cycle, and we identify some potential research challenges. They are extremely complex, occur in rapidly changing environments characterized by uncertainty, and involve conflicts among different interests.
The titles of the seven symposia were Artificial Intelligence for Human-Robot Interaction; Energy Market Prediction; Expanding the Boundaries of Health Informatics Using AI; Knowledge, Skill, and Behavior Transfer in Autonomous Robots; Modeling Changing Perspectives: Reconceptualizing Sensorimotor Experiences; Natural Language Access to Big Data; and The Nature of Humans and Machines: A Multidisciplinary Discourse. The highlights of each symposium are presented in this report. The primary goal of the AI for Human-Robot Interaction symposium was to bring together and strengthen the community of researchers working on the AI challenges inherent to human-robot interaction (HRI). HRI is an extremely interesting problem domain for AI and robotics research. It aims to develop robots that are intelligent, autonomous, and capable of interacting with, modeling, and learning from humans.
We now know the full genomes of more than 60 organisms. The experimental characterization of the newly sequenced proteins is deemed to lack behind this explosion of naked sequences (sequencefunction gap). The rate at which expert annotators add the experimental information into more or less controlled vocabularies of databases snails along at an even slower pace. Most methods that annotate protein function exploit sequence similarity by transferring experimental information for homologues. A crucial development aiding such transfer is large-scale, work-and management-intensive projects aimed at developing a comprehensive ontology for gene-protein function, such as the Gene Ontology project.
A key feature of the AAMAS conference is its emphasis on ties to real-world applications. The focus of this article is to provide a broad overview of application-focused papers published at the AAMAS 2010 and 2011 conferences. More specifically, recent applications at AAMAS could be broadly categorized as belonging to research areas of security, sustainability, and safety. We outline the domains of applications, key research thrusts underlying each such application area, and emerging trends. This emphasis of trying to marry theory and practice at AAMAS goes all the way back to the origins of its predecessor conferences, such as the first International Conference on Autonomous Agents (Johnson 1997).
Last week, Qualcomm announced the Snapdragon 845, which sends AI tasks to the most suitable cores. There's not a lot of difference between the three company's approaches -- it ultimately boils down to the level of access each company offers to developers, and how much power each setup consumes. Before we get into that though, let's figure out if an AI chip is really all that much different from existing CPUs. A term you'll hear a lot in the industry with reference to AI lately is "heterogeneous computing." It refers to systems that use multiple types of processors, each with specialized functions, to gain performance or save energy.
Before scientists can effectively capture and deploy fusion energy, they must learn to predict major disruptions that can halt fusion reactions and damage the walls of doughnut-shaped fusion devices called tokamaks. Today, researchers at the U.S. Department of Energy's Princeton Plasma Physics Laboratory (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability....
What do tomorrow's automakers have to do with net-zero buildings? Why it's important: This will transform the design and technology requirements for buildings in order to accommodate personal EVs and even electric fleets What It Is: Drillinginfo, a SaaS provider for the energy industry, has acquired Pattern Recognition Technologies (PRT), an energy forecasting software player. Why It Matters: Adding PRT's machine learning capabilities to predict energy consumption will allow Drillinginfo to enter horizontal markets in energy data analytics. This maneuver also bolsters Drillinginfo's North American customer base, particularly in clean energy data analytics. Why It Matters: Incumbents are reacting to the transition towards smart products by picking up smart home specialists.