The dichotomy between machines and living things is narrowing. Today, artificial intelligence (AI) is embedded in all kinds of technology, from robots to social networks. This affects the youngest among us as we see the emergence of an "Internet of Toys." The trend is what prompted us to explore the impact of those "smart," interconnected playthings on children. We'll present our paper, "Hey Google, is it OK if I eat you?: Initial Explorations in Child-Agent Interaction," at the Interaction Design and Children conference at Stanford University on June 27.

Any denizen of these digital days can't help but feel surrounded by awesomely intelligent devices that seemingly know and control all. From artificially intelligently enabled mattresses that understand your sleep patterns better than you, to adaptively learning apps that anticipate your never-varying morning beverage order. When did this explosion of computer intelligence occur, and, really, how much easier is your life as a result? To be sure, artificial intelligence (AI) and machine learning (ML) have made, and continue to make enormous strides with an accelerating pace. Any industry or business that is not developing and embracing these technologies will certainly perish.

Enterprise applications vendor Infor recently showed a concept design for a similar agent, coincidentally also called Max. Project Max … uses intelligent automation across Infor's Sales Suite products to provide timely information, reminders and action prompts to sales reps as they're working out in the field. Now unbundling has reached the field of enterprise applications, due to the combined effect of integrated cloud platforms, smart mobile devices and intelligent agents. Expect to see new models of enterprise organization and teamwork to emerge as new functional bundles of automation enable new ways of working together to achieve business outcomes.

We develop a two-defender (Alice and Bob) invasion game using the method of projective simulation as an embodied model for artificial intelligence. We hope that it will be the first step towards the effect of perception on different actions in a given game. As a given perception of a given situation, the agent, say Alice, encounters some attack symbols coming from the right attacker where she can learn to prevent. However, some of these percepts are invisible for her. Instead, she perceives some other signs that are related to her partner's (Bob) task. We elaborate an example in which an agent perceives an equal portion of percepts from both attackers. Alice can choose to concentrate on her job, though she loses some attacks. Alternatively, she can have some sort of cooperation with Bob to get and give help. It follows that the maximum blocking efficiency in concentration is just the minimum blocking efficiency in cooperation. Furthermore, Alice would have a choice to select two different forgetting factors for blocking attacks and for helping task. Therefore, she can choose between herself and the other. Consequently, selfishness is discerned as an only Nash equilibrium in this game. It is a pure strategy and Pareto optimal and containing Shapley value in this superadditive coalition. Finally, we propose another perception for the same situation that can be tracked in the future regarding the present study.

Artificial Intelligence is poised to disrupt the entire eCommerce industry. An interesting convergence is taking place; one that will have enormous implications in the way retailers sell their products and services and the way consumers buy them. Artificial Intelligence capabilities and applications are attempting to solve real-world issues that eCommerce industry are facing. How Artificial Intelligence in e-commerce can play an important and game changing role, moving beyond customer segmentation to help them achieve the best possible results? The visual Search engine is one of the most exciting trends of Artificial Intelligence in eCommerce.

The recently proposed DeGroot-Friedkin model describes the dynamical evolution of individual social power in a social network that holds opinion discussions on a sequence of different issues. This paper revisits that model, and uses nonlinear contraction analysis, among other tools, to establish several novel results. First, we show that for a social network with constant topology, each individual's social power converges to its equilibrium value exponentially fast, whereas previous results only concluded asymptotic convergence. Second, when the network topology is dynamic (i.e., the relative interaction matrix may change between any two successive issues), we show that each individual exponentially forgets its initial social power. Specifically, individual social power is dependent only on the dynamic network topology, and initial (or perceived) social power is forgotten as a result of sequential opinion discussion. Last, we provide an explicit upper bound on an individual's social power as the number of issues discussed tends to infinity; this bound depends only on the network topology. Simulations are provided to illustrate our results.

Natural-language-facilitated human-robot cooperation (NLC), in which natural language (NL) is used to share knowledge between a human and a robot for conducting intuitive human-robot cooperation (HRC), is continuously developing in the recent decade. Currently, NLC is used in several robotic domains such as manufacturing, daily assistance and health caregiving. It is necessary to summarize current NLC-based robotic systems and discuss the future developing trends, providing helpful information for future NLC research. In this review, we first analyzed the driving forces behind the NLC research. Regarding to a robot s cognition level during the cooperation, the NLC implementations then were categorized into four types {NL-based control, NL-based robot training, NL-based task execution, NL-based social companion} for comparison and discussion. Last based on our perspective and comprehensive paper review, the future research trends were discussed.

Autonomous systems can substantially enhance a human's efficiency and effectiveness in complex environments. Machines, however, are often unable to observe the preferences of the humans that they serve. Despite the fact that the human's and machine's objectives are aligned, asymmetric information, along with heterogeneous sensitivities to risk by the human and machine, make their joint optimization process a game with strategic interactions. We propose a framework based on risk-sensitive dynamic games; the human seeks to optimize her risk-sensitive criterion according to her true preferences, while the machine seeks to adaptively learn the human's preferences and at the same time provide a good service to the human. We develop a class of performance measures for the proposed framework based on the concept of regret. We then evaluate their dependence on the risk-sensitivity and the degree of uncertainty. We present applications of our framework to self-driving taxis, and robo-financial advising.

BICA Labs Laboratories for Biologically Inspired Cognitive Architectures 2. Blockchain: Distributed Ledger Technology (DLT) TRUSTED PERSISTENT DATA RECORDS INSIDE TRUSTLESS ENVIRONMENTS WITHOUT CENTRAL GOVERNANCE SECURED BY ECONOMIC INCENTIVES 4. Distributed database (ledger) Distributed computations (state changes), Turing-complete OR incomplete Peer-to-peer mesh network Cryptographically secured TECHNOLOGY ECONOMICS BLOCKCHAIN Multiagent economy Game theory Free open market Non-state decentralized economies linked to particular types of resources or businesses 6. NO CENTRALIZATION 7. NO DATA OLIGOPOLY 9. Blockchain tech intro 10. Important Blockchains Bitcoin Ripple Ethereum ZCashDashNEM Most popular cryptocurrency & source code base Value transfer network Smart contracts Proof of Importance Governance model Zero-knowledge 11. Most important ready-to-go DLTs cores Bitcoin Core Graphene Scorex Tendermint Ripple / Stellar Language C/C C Scala Go C Consensus PoW dPoS PoW, 2x PoS PoS Blockchains Bitcoin, Dash, Litecoin, ... BitShares, Steem, Golos – (Waves experiments) Cosmos (under dev) Ripple, Stellar, Infra (e-Auction) " " Most proved Blazingly fast Modular PoS Fast & proved "–" Hard to understand Complex model Limited functionality, no real-world impl Immature Complex model 16. Languages for working with DLTs cores C/C -- Bitcoin, Graphene, Ripple: performance Go -- Ethereum, Tehndermint Python -- Ethereum experiments with blockchain Rust -- Ethereum, Bitcoin: performance efficient code Scala -- Scorex (fast blockchain prototyping) Java -- NEM JavaScript (Angular, React): UI & APIs 17. Meta-languages for smart contracts Solidity: JavaScript-like Serpent: Python-like Viper: Python-like (Serpent 2.0) 18. Blockchain & AI Technical Synergy 19. Civilization 4.0 key factors Quantum Computing Generic Artificial Intelligence Transhumanism Life extension Cyborgization Cosmic Expansion SINGULARITY 31.

The buzzwords "Industry 4.0" and "digital business" represent the start of a complex transformational process that will deeply affect industry and society during the next decade. This transformation is based on the convergence of the real (analog) world and the virtual (digital) world by means of machineto- machine (M2M) communication, autonomous systems (for example, robotics) and the Internet of Things (IoT). The German government uses the term "Industry 4.0" as the title of a government project promoting the computerization of traditional industries and the creation of intelligent factories (smart factories) that will be supported by cyberphysical systems and the IoT. The digits "4.0" in Industry 4.0 stand for the fourth industrial revolution: the transition of production from digital processing to fully interconnected processes, products and services. It follows the evolution of production processes for tradable goods from manufacturing to industry production (the first revolution), the move from steam-driven machine production to electricity-driven production (the second revolution) and the shift from analog processing to digital processing and microelectronics (the third revolution). One of the major features of Industry 4.0 is the ability of machines and devices to communicate with each other without a human interface.