The field of computational models of argument is emerging as an important aspect of artificial intelligence research. The reason for this is based on the recognition that if we are to develop robust intelligent systems, then it is imperative that they can handle incomplete and inconsistent information in a way that somehow emulates the way humans tackle such a complex task. And one of the key ways that humans do this is to use argumentation either internally, by evaluating arguments and counterarguments‚ or externally, by for instance entering into a discussion or debate where arguments are exchanged. As we report in this review, recent developments in the field are leading to technology for artificial argumentation, in the legal, medical, and e-government domains, and interesting tools for argument mining, for debating technologies, and for argumentation solvers are emerging.

Real-Time Strategy (RTS) games have become an increasingly popular test-bed for modern artificial intelligence techniques. With this rise in popularity has come the creation of several annual competitions, in which AI agents (bots) play the full game of StarCraft: Broodwar by Blizzard Entertainment. The three major annual StarCraft AI Competitions are the Student StarCraft AI Tournament (SSCAIT), the Computational Intelligence in Games (CIG) competition, and the Artificial Intelligence and Interactive Digital Entertainment (AIIDE) competition. In this paper we will give an overview of the current state of these competitions, and the bots that compete in them.

Amazon's Mechanical Turk is a platform for soliciting work on online tasks that has been used by market researchers, translators, and data scientists to complete surveys, perform work that cannot be easily automated, and create human-labeled data for supervised learning systems. Its namesake, the original Mechanical Turk, was an 18th-century chess-playing automaton gifted to the Austrian Empress Maria Theresa. An elaborate hoax, it concealed a human player amidst the clockwork machinery that appeared to direct each move on the board. Amazon's Mechanical Turk (mTurk), which they call "artificial artificial intelligence," isn't all that different. From the outside, mTurk appears to perform tasks automatically that only humans can, like identifying objects in photographs, discerning the sentiment towards a brand in a tweet, or generating natural language in response to a prompt.

Sequence modeling with neural networks has lead to powerful models of symbolic music data. We address the problem of exploiting these models to reach creative musical goals, by combining with human input. To this end we generalise previous work, which sampled Markovian sequence models under the constraint that the sequence belong to the language of a given finite state machine provided by the human. We consider more expressive non-Markov models, thereby requiring approximate sampling which we provide in the form of an efficient sequential Monte Carlo method. In addition we provide and compare with a beam search strategy for conditional probability maximisation. Our algorithms are capable of convincingly re-harmonising famous musical works. To demonstrate this we provide visualisations, quantitative experiments, a human listening test and audio examples. We find both the sampling and optimisation procedures to be effective, yet complementary in character. For the case of highly permissive constraint sets, we find that sampling is to be preferred due to the overly regular nature of the optimisation based results. The generality of our algorithms permits countless other creative applications.

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep $Q$-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.

This process generates an absurd amount of data that can be difficult for the average human to handle. Modern recruiters are very talented, but they inevitably miss employee placements due to human error in data collection and processing. Almost half of recruiters -- 46 percent -- say the most challenging part of their job is identifying the right candidates from a large applicant pool. Artificial Intelligence and machine learning have the potential to change the game by radically improving applicant vetting and intelligently matching candidates with jobs. Nearly all (96 percent) senior HR professionals believe that AI technology has the potential to enhance talent acquisition and retention.

Machine learning and artificial intelligence (ML/AI) mean different things to different people, but the newest approaches have one thing in common: They are based on the idea that a program's output should be created mostly automatically from a high-dimensional and possibly huge dataset, with minimal or no intervention or guidance from a human. Open source tools are used in a variety of machine learning and artificial intelligence projects. In this article, I'll provide an overview of the state of machine learning today. In the past, AI programs usually were explicitly programmed to perform tasks. In most cases, the machine's "learning" consisted of adjusting a few parameters, guiding the fixed implementation to add facts to a collection of other facts (a knowledge database), then (efficiently) searching the knowledge database for a solution to a problem, in the form of a path of many small steps from one known solution to the next. In some cases, the database wouldn't need to or couldn't be explicitly stored and therefore had to be rebuilt. Another example is steering a car.

With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification. Fortunately, however, the most successful approaches to object detection are currently extensions of image classification models. A few months ago, Google released a new object detection API for Tensorflow.

Keeping up with technology trends can be exhausting and confusing. Many times, the terms used to describe technology investing opportunities aren't always defined and can leave investors with more questions than answers. So let's take a quick look at how NVIDIA Corporation (NASDAQ:NVDA), a graphics process maker with a leadership position in these spaces, defines each of them -- and what the company's potential is in these businesses. Artificial intelligence (AI) is sometimes thought of as the intelligence we see from robots in movies or television shows. That level of AI isn't possible yet, and instead, tech companies that are working on artificial intelligence right now are usually doing what's called "narrow AI."