Meet Gisele, she lives in a nice house, but it's not just a house. Thanks to a handful of connected devices and craft ai agents ruling over them, her house reacts to and learns from its occupants. Let's start with basic automation. For instance, depending on the outside light intensity, the house will adjust the light of the room in which Gisele is. She doesn't have to turn any switch on or off: she is localized inside the house by an indoor positioning system, such as a Beacon, and the connected light bulbs, such as a LIFX or Philips Hue, react accordingly, following simple logic defined in craft ai.

"An Introduction to Agent-based Modeling" is a well-written and honest look at the benefits and limitations of agent-based modeling. Agent-based modeling is a computer simulation that assigns properties to agents, and the environment they interact with. Agent-based modeling demonstrates that agents acting of their own accord will collectively self-organize into predictable macro-behavior (a concept that's similar to Adam Smith's invisible hand theory). Some of this macro-behavior will alter the environment and eco-system (a concept that's termed "emergent"). The authors are honest enough to admit that agent-based modeling is not predictive (it's too determinant on the algorithms and parameters that humans assign the model), but it can be a powerful tool for education and communication.

An artificial intelligence agent developed by two Carnegie Mellon University computer science students has proven to be the game's ultimate survivor --, outplaying both the game's built-in AI agents and human players. The students, Devendra Chaplot and Guillaume Lample, used deep-learning techniques to train the AI agent to negotiate the game's 3-D environment, still challenging after more than two decades because players must act based only on the portion of the game visible on the screen. Their work follows the groundbreaking work of Google's DeepMind, which used deep-learning methods to master two-dimensional Atari 2600 videogames and, earlier this year, defeat a world-class professional player in the board game Go. In contrast to the limited information provided in Doom, both Atari and Go give players a view of the entire playing field. "The fact that their bot could actually compete with average human beings is impressive," said Ruslan Salakhutdinov, an associate professor of machine learning who was not involved in the student project.

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.

We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two approaches for learning in these domains: Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses deep Q-learning, while the latter exploits the fact that, during learning, agents can backpropagate error derivatives through (noisy) communication channels.

AI has a long way to go before it sees widespread adoption in the enterprise, but more and more companies are looking at the potential its capabilities have to offer. "It's still not too late to get started, but if you aren't taking steps now to plan and act for 2020, much less 2025, you'll very quickly find yourself fighting a 21st century war with 20th century weapons," according to the study. Recent reports have pointed to automation supported by intelligent software agents as causing a disruption in the workforce. Forrester projects that by 2021, AI and cognitive technologies will cause the elimination of 6% of U.S. jobs. But, according to Cognizant, only one-third of respondents think it is "very likely" that fewer people will be needed in the workforce. Instead, most think that people and the ingenuity they have to offer will still be required.

The literature on judgment aggregation is moving from studying impossibility results regarding aggregation rules towards studying specific judgment aggregation rules. Here we give a structured list of most rules that have been proposed and studied recently in the literature, together with various properties of such rules. We first focus on the majority-preservation property, which generalizes Condorcet-consistency, and identify which of the rules satisfy it. We study the inclusion relationships that hold between the rules. Finally, we consider two forms of unanimity, monotonicity, homogeneity, and reinforcement, and we identify which of the rules satisfy these properties.

We determine the quality of randomized social choice mechanisms in a setting in which the agents have metric preferences: every agent has a cost for each alternative, and these costs form a metric. We assume that these costs are unknown to the mechanisms (and possibly even to the agents themselves), which means we cannot simply select the optimal alternative, i.e. the alternative that minimizes the total agent cost (or median agent cost). However, we do assume that the agents know their ordinal preferences that are induced by the metric space. We examine randomized social choice functions that require only this ordinal information and select an alternative that is good in expectation with respect to the costs from the metric. To quantify how good a randomized social choice function is, we bound the distortion, which is the worst-case ratio between expected cost of the alternative selected and the cost of the optimal alternative. We provide new distortion bounds for a variety of randomized mechanisms, for both general metrics and for important special cases. Our results show a sizable improvement in distortion over deterministic mechanisms.

Business and technology management executives wondered what big data meant, when the cloud would disrupt their companies, and how to engage effectively on social channels. In 2016, Hadoop turned 10, the cloud has been around even longer, and social has become a way of business and life. As a refresh to my 2014 blog and report, here are the next 15 emerging technologies Forrester thinks you need to follow closely. We organize this year's list into three groups -- systems of engagement technologies will help you become customer-led, systems of insight technologies will help you become insights-driven, and supporting technologies will help you become fast and connected. You might have noticed a few glaring omissions.

As autonomous technology continues to evolve, the Pentagon finds itself being pulled in two directions, enticed by the capabilities that autonomous systems could provide while also insistent it always be subservient to humans, and a set of human morals and mindsets. That tension was on full display Aug. 25, when a new report from a key Pentagon advisory group called for an acceleration of autonomous systems within the US military at the same time the country's second highest ranking uniformed officer warned that there will need to be limits on how the technology is used in order to avoid the dreaded killer-robot scenario. Speaking at the Center for Strategic and International Studies, Gen. Paul Selva, vice chairman of the Joint Chiefs of Staff, laid out his concerns with the "Terminator Conundrum," the idea that a fully autonomous system could be created with the capability to make decisions about when and where to inflict violence. While noting that technologists in the Pentagon believe that capability is still a decade away, Selva noted that 15 years ago he was told a digital rendering of the world would be impossible and never happen, before dryly telling the audience" "So I guess Google Earth is an impossibility." He also threw his support behind the idea of a treaty or global convention against the creation of wholly autonomous systems that can operate without a man in the loop controlling it, saying: "I do think we need to examine the bodies of law and convention that might constrain anyone in the world from building that kind of a system.