The first human versus computer no-limit Texas hold ‘em competition took place from April 24–May 8, 2015 at River’s Casino in Pittsburgh, PA. In this article I present my thoughts on the competition design, agent architecture, and lessons learned. Several problematic hands from the competition are highlighted that reveal the most glaring weaknesses of the agent. The research underlying the agent is placed within a broader context in the AI research community, and several avenues for future study are mapped out.

From Alexa and self-driving cars to job applicant screening processes, artificial intelligence is fast becoming the norm in business. But it also could start playing far bigger roles in security, helping law enforcement and other protective agents figure out who's up to no good. As Fredrick Kunkle of The Washington Post reports, there's now an AI-based kiosk designed to detect whether travelers are fibbing. Designed by Aaron Elkins, assistant professor of the Fowler College of Business Administration at San Diego State University, the new AI lie detector goes by the name Automated Virtual Agent for Truth Assessments in Real Time, or AVATAR for short. Once you've scanned your ID or passport, the kiosk asks you a bunch of questions.

This article was written by James Le. It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start?

We consider settings where owners of electric vehicles (EVs) participate in a market mechanism to charge their vehicles. Existing work on such mechanisms has typically assumed that participants are fully rational and can report their preferences accurately via some interface to the mechanism or to a software agent participating on their behalf. However, this may not be reasonable in settings with non-expert human end-users.Thus, our overarching aim in this paper is to determine experimentally if a fully expressive market interface that enables accurate preference reports is suitable for the EV charging domain, or, alternatively, if a simpler, restricted interface that reduces the space of possible options is preferable. In doing this, we measure the performance of an interface both in terms of how it helps participants maximise their utility and how it affects deliberation time. Our secondary objective is to contrast two different types of restricted interfaces that vary in how they restrict the space of preferences that can be reported. To enable this analysis, we develop a novel game that replicates key features of an abstract EV charging scenario. In two experiments with over 300 users, we show that restricting the users' preferences significantly reduces the time they spend deliberating (by up to half in some cases). An extensive usability survey confirms that this restriction is furthermore associated with a lower perceived cognitive burden on the users. More surprisingly, at the same time, using restricted interfaces leads to an increase in the users' performance compared to the fully expressive interface (by up to 70%). We also show that some restricted interfaces have the desirable effect of reducing the energy consumption of their users by up to 20% while achieving the same utility as other interfaces. Finally, we find that a reinforcement learning agent displays similar performance trends to human users, enabling a novel methodology for evaluating market interfaces.

We study computational problems for two popular parliamentary voting procedures: the amendment procedure and the successive procedure. They work in multiple stages where the result of each stage may influence the result of the next stage. Both procedures proceed according to a given linear order of the alternatives, an agenda. We obtain the following results for both voting procedures: On the one hand, deciding whether one can make a specific alternative win by reporting insincere preferences by the fewest number of voters, the Coalitional Manipulation problem, or whether there is a suitable ordering of the agenda, the Agenda Control problem, takes polynomial time. On the other hand, our experimental studies with real-world data indicate that most preference profiles cannot be manipulated by only few voters and a successful agenda control is typically impossible. If the voters' preferences are incomplete, then deciding whether an alternative can possibly win is NP-hard for both procedures. Whilst deciding whether an alternative necessarily wins is coNP-hard for the amendment procedure, it is polynomial-time solvable for the successive procedure.

Artificial Intelligence (AI) is everywhere these days, but it's rarely discussed in detail, or with specific examples of how and why it will help improve the way we do things. Let's address that shortcoming by digging into a particular variant of AI that holds great promise: Swarm Intelligence. Swarm Intelligence is the idea of using many simplistic machine learning models each good at one small task to solve bigger, more complex problems. The idea is analogous to how swarms or hives act in the natural world. Take ants, for example: each performs a simple task that helps that hive work as a complex system.

NEW YORK CITY - 15 Jun 2017: LivePerson, Inc. (Nasdaq: LPSN), a leading provider of cloud mobile and online business messaging solutions, and IBM (NYSE: IBM) have announced LiveEngage with Watson, the first global, enterprise-scale, out-of-the-box integration of Watson-powered bots with human agents. The new offering combines IBM's Watson Virtual Agent technology with LivePerson's LiveEngage platform, allowing brands to rapidly and easily deploy conversational bots that get smarter with each interaction, and lets consumers message those brands from their smartphone - via the brand's app, SMS, Facebook Messenger, or even the brand's mobile site - instead of having to call an 800 number. This legacy approach has not kept pace with the consumer move to smartphones and messaging apps, now the dominant way consumers communicate digitally. Forrester's 2017 Customer Service Trends report revealed that "Customers of all ages are moving away from using the phone to using self-service -- web and mobile self-service, communities, virtual agents, automated chat dialogs, or chatbots -- as a first point of contact with a company" and, according to Dimension Data, while there has been a 12 percent decline in phone volume, there has been growth in every digital channel[2]. LiveEngage with Watson helps meet that demand - allowing consumers to message large brands from their smartphones and instantly get answers from AI-powered bots, with human care representatives brought in seamlessly, in real-time, if a bot is not able to resolve an issue satisfactorily.

Overall investment in automation technologies – including robotic process automation (RPA), autonomics, virtual customer service agents and personal assistants, natural language processing and machine learning – is expected to double in the next two years, the survey finds, as enterprises look to harness technologies that have the flexibility to solve more than one business problem. "Automation and artificial intelligence are top of mind for business executives and service providers alike – and with good reason," said Todd Lavieri, partner and president of ISG Americas. "Robotic process automation, autonomic systems and cognitive agents are making employees more productive by taking over routine, process-oriented tasks. At the same time, data scientists are using machine learning to find patterns and make predictions on vast troves of structured and unstructured data. These technologies, taken together, promise to usher in the next wave of enterprise growth and profitability."

It is quite difficult at first to precisely define'Complexity Science'. It is a new perspective of methodology and modeling approaches that are based more on reality than assumptions. Quite simply put, Complexity Science is a new way to grasp and manage reality. It does not study systems in isolation like gambling dice or planetary motion only. It studies the complex, holistic, inter-connected reality in which we actually live such as financial stock markets, social policies, economic policies, natural catastrophes and so on.

Researchers at Microsoft developed an artificial intelligence (AI) algorithm that can achieve the maximum score on Ms. Pac-Man, 999,999, four times greater than the highest human score. After recovering from your wave of relief at the news that we've solved the Ms. Pac-Man problem, you might wonder why our greatest minds were spending their days chasing that particular goal. As it turns out, this accomplishment is significant because the "divide-and-conquer" method used can be applied to teach AI agents to complete other complex tasks. The system, according to Microsoft's blog, was developed by a Maluuba, a deep learning startup company which was acquired by Microsoft earlier in the year. The divide-and-conquer method assigns individual AI agents different tasks but also allows them to work together collaboratively through a "top manager."