Can I have an AI and two blockchains? That's a joke but people tend to confuse or misuse the terms. There is undoubtedly a hype around these terms. In this post we are going to talk about AI (Artificial Intelligence), what it is and whether it is able to achieve what it is promising. We are going to do this, while going through the cases presented at the AI Congress 2018.

Asynchronous stochastic approximations are an important class of model-free algorithms that are readily applicable to multi-agent reinforcement learning (RL) and distributed control applications. When the system size is large, the aforementioned algorithms are used in conjunction with function approximations. In this paper, we present a complete analysis, including stability (almost sure boundedness) and convergence, of asynchronous stochastic approximations with asymptotically bounded biased errors, under easily verifiable sufficient conditions. As an application, we analyze the Policy Gradient algorithms and the more general Value Iteration based algorithms with noise. These are popular reinforcement learning algorithms due to their simplicity and effectiveness. Specifically, we analyze the asynchronous approximate counterpart of policy gradient (A2PG) and value iteration (A2VI) schemes. It is shown that the stability of these algorithms remains unaffected when the approximation errors are guaranteed to be asymptotically bounded, although possibly biased. Regarding convergence of A2VI, it is shown to converge to a fixed point of the perturbed Bellman operator when balanced step-sizes are used. Further, a relationship between these fixed points and the approximation errors is established. A similar analysis for A2PG is also presented.

Abstract-- We propose a reliable intersection control mechanism for strategic autonomous and connected vehicles (agents) in non-cooperative environments. Each agent has access to his/her earliest possible and desired passing times, and reports a passing time to the intersection manager, who allocates the intersection temporally to the agents in a First-Come- First-Serve basis. However, the agents might have conflicting interests and can take actions strategically. To this end, we analyze the strategic behaviors of the agents and formulate Nash equilibria for all possible scenarios. Furthermore, among all Nash equilibria we identify a socially optimal equilibrium that leads to a fair intersection allocation, and correspondingly we describe a strategy-proof intersection mechanism, which achieves reliable intersection control such that the strategic agents do not have any incentive to misreport their passing times strategically. I. INTRODUCTION Instead of classical yet inefficient traffic lighting systems, a First-Come-First-Serve (FCFS) based autonomous intersection control, which utilizes the connectivity of the autonomous agents with each other and the infrastructure, e.g., an intersection manager, has been introduced in [1].

Much of the day-to-day work of UX design focuses on optimizing manual, paper-based processes through the use of technology. Consider Amazon's origins, for example. Buying a book from a catalog has been around since 1498 when Aldus Manutius published a catalog of the books he printed. Almost 500 years later, Amazon put their catalog online and created a virtual, online experience for people to buy books. In Amazon's early years, this focus led to innovative steps in traditional transaction-based human–computer interaction (HCI).

Breakthroughs in technology are typically attributed to a single lone genius, but research led by DeepMind scientist Thore Graepel suggests the full power of AI will be unleashed through a collective approach of multi-agents. The UCL machine learning professor helped create AlphaGo, which pursued an individual strategy called competitive self-play to become the first computer program to defeat a human professional Go player in 2015. He's since turned his focus from competition to cooperation, using deep reinforcement learning to understand how teamwork develops among self-interested agents, whether they're computer programmes or human social dilemmas. "We believe that this kind of model is a powerful baseline to study these kinds of social dilemmas in more detail," said Graepel at the AI for Social Good symposium at the Turing Institute. His work forms part of DeepMind's ambitious mission "to solve intelligence".

Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans' ability to represent the mental states of others, including their desires, beliefs, and intentions. We propose to train a machine to build such models too. We design a Theory of Mind neural network -- a ToMnet -- which uses meta-learning to build models of the agents it encounters, from observations of their behaviour alone. Through this process, it acquires a strong prior model for agents' behaviour, as well as the ability to bootstrap to richer predictions about agents' characteristics and mental states using only a small number of behavioural observations. We apply the ToMnet to agents behaving in simple gridworld environments, showing that it learns to model random, algorithmic, and deep reinforcement learning agents from varied populations, and that it passes classic ToM tasks such as the "Sally-Anne" test (Wimmer & Perner, 1983; Baron-Cohen et al., 1985) of recognising that others can hold false beliefs about the world. We argue that this system -- which autonomously learns how to model other agents in its world -- is an important step forward for developing multi-agent AI systems, for building intermediating technology for machine-human interaction, and for advancing the progress on interpretable AI.

A standard belief on emerging collective behavior is that it emerges from simple individual rules. Most of the mathematical research on such collective behavior starts from imperative individual rules, like always go to the center. But how could an (optimal) individual rule emerge during a short period within the group lifetime, especially if communication is not available. We argue that such rules can actually emerge in a group in a short span of time via collective (multi-agent) reinforcement learning, i.e learning via rewards and punishments. We consider the gathering problem: several agents (social animals, swarming robots...) must gather around a same position, which is not determined in advance. They must do so without communication on their planned decision, just by looking at the position of other agents. We present the first experimental evidence that a gathering behavior can be learned without communication in a partially observable environment. The learned behavior has the same properties as a self-stabilizing distributed algorithm, as processes can gather from any initial state (and thus tolerate any transient failure). Besides, we show that it is possible to tolerate the brutal loss of up to 90\% of agents without significant impact on the behavior.

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this gap. We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering. Our main contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed elicitation strategies outperform the currently used pairwise methods, and found that users prefer ranking most. Our experiments further show that utilising monotonicity information in GPs by using a linear prior mean at the start and virtual comparisons to the nadir and ideal points, increases performance. We demonstrate our decision support framework in a real-world study on traffic regulation, conducted with the city of Amsterdam.

The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this paper, we introduce an efficient algorithm to address the above problem in a fully decentralized (peer-to-peer) and asynchronous fashion, with provable convergence rate. We show how to make the algorithm differentially private to protect against the disclosure of information about the personal datasets, and formally analyze the trade-off between utility and privacy. Our experiments show that our approach dramatically outperforms previous work in the non-private case, and that under privacy constraints, we can significantly improve over models learned in isolation.

Accepted: January 16, 2018 Online Published: January 29, 2018 URL: https://doi.org/10.5539/ass.v14n2p145 Abstract This study examines the two important aspect of latest technology issues in Islamic finance that related to artificial intelligence (AI) and smart contract. AI refers to the ability of machines to understand, think, and learn in a similar way to human beings, indicating the possibility of using computers to simulate human intelligence. Smart contract is a computer code running on top of a block-chain containing a set of rules under which the parties to that smart contract agree to interact with each other. The main objectives of this article are to evaluate the operations of AI and smart contract, to make comparison between the operations of AI and smart contract. This article concludes that AI and smart contract will have a huge impact in future for Islamic Finance industry. Keywords: Artificial intelligence (AI), smart contract, digital banking, Islamic Finance 1. Preliminary Artificial Intelligence (AI) is the intelligence machines that have the ability to think. At this point, Artificial Intelligence (AI) offers rapid advancement in technology that mimic human intelligence. It's believed that intelligence machines associated with human thinking activities such as decision making and problem solving learning. Professor J. McCarthy (1955) established the concept of artificial intelligence (AI) during the first artificial intelligence conference at Dartmouth conferences in year 1956. This evolution confirm by Bogue (2014) who described Artificial intelligence (AI) as an intelligent agent system that takes actions in maximize the chances of success in a particular task. Pan (2016) revealed that AI becomes extremely critical when it applies to the technology. According to research report on artificial intelligence, this market is expected to be worth $16.06 billion by 2022. This market is expected to grow at 62.9% compound annual growth rate (CAGR) from 2016 to 2022 (Research and Markets, 2017). In 25th April 2016 to 27th May 2016, a special report under the subheading "Outlook on Artificial Intelligence in the Enterprise 2016" have been produce by Narrative Science in collaboration with National Business Research Institute. This report deployed an online survey with a total of 235 respondents.