AI is forecast to contribute up to $15.7 trillion to the global economy by 2030 which is more than the current output of China and India combined. However only 10 per cent of Australian CEOs are clear on how robotics and artificial intelligence can improve their customer's experience, and 65 per cent are concerned about changing consumer behaviour with respondents listing this as the likely number one disruptive trend over the next five years. In the year from October 2016 to September 2017, KPMG reported there were 155 frauds reported with a value of $482 million – a significant drop from 259 frauds worth $823 million the year before, however identity theft saw a sharp increase to nearly $17.9 million and cyber-crime is on the rise (including hacking, compromising computer accounts, skimming digital data and porting mobile phones) which accounted for 6 per cent of all frauds and 7 per cent by value ($33.8 million).

Modi made it clear that "the march of technology cannot be at the expense of further increasing the difference between societies over access to technology". "The evolution of technology has to be rooted in the ethic of'Sabka Saath, Sabka Vikaas'. Technology opens entirely new spheres and sectors for growth, and entirely new paradigms for more opportunities. "The road ahead for AI depends on and will be driven by human intentions'. It is our intention that will determine the outcomes of artificial intelligence," Modi said after inaugurating the Wadhwani Institute of Artificial Intelligence at the University of Mumbai's Kalina Campus here. "Can AI help us predict natural calamities?

Masaki Hojo, who built Daifuku Co. into the world's largest maker of machines for handling chip materials, is once again challenging market expectations even as he prepares to make his exit from the Japanese company. Daifuku is considering raising its earnings guidance for the medium term, according to Hojo, 69, who plans to step down as president at the end of next month. The need for more semiconductors in cars and robots, and consumers' growing appetite for e-commerce and big data can only mean more demand for Daifuku's machinery, he says, while China looms as a possible new market for expansion. Few outside of the chip industry will have heard of Daifuku, whose gear is found in cutting-edge Intel Corp. plants and helps Amazon.com The Osaka-based company also supplies baggage-handling systems for the world's biggest airports from Chicago's O'Hare to Beijing and London.

We need to consider systems and the brain machine interaction (BMI) area in IEEE SMC cybernetics as well as include human in the loop. The purpose conference and then join the SMC society. of this article is as follows: (1) To integrate the open source II. Past held events in the world from 2008 to 2017 Facebook AI Research (FAIR) DarkForest program of Facebook with Item Response Theory (IRT), to the new open Owing to the maturity of deep learning technologies and learning system, namely, DDF learning system; (2) To integrate computer hardware, Google combined them together with DDF Go with Robot namely Robotic DDF Go system; (3) To Monte Carlo Tree to beat many top professional Go players invite the professional Go players to attend the activity to play without handicaps in 2016 and 2017 [4-5]. This year is the first Go games on site with a smart machine. The research team will year to hold Human & Smart Machines Co-Learning @ IEEE apply this technology to education, such as, playing games to SMC 2017. However, we have carried out the events of humans enhance the children concentration on learning mathematics, playing Go with the computer Go programs for almost a decade languages, and other topics. With the detected brainwaves, the [6-7]. Figure 1 shows the past held events of Human vs. Computer robot will be able to speak some words that are very much to Go Competitions from 2008 to 2017 the point for the students and to assist the teachers in classroom (https://www.youtube.com/watch?v UkSOVnbC2Y8) funded in the future.

Social network analysis provides meaningful information about behavior of network members that can be used for diverse applications such as classification, link prediction. However, network analysis is computationally expensive because of feature learning for different applications. In recent years, many researches have focused on feature learning methods in social networks. Network embedding represents the network in a lower dimensional representation space with the same properties which presents a compressed representation of the network. In this paper, we introduce a novel algorithm named "CARE" for network embedding that can be used for different types of networks including weighted, directed and complex. Current methods try to preserve local neighborhood information of nodes, whereas the proposed method utilizes local neighborhood and community information of network nodes to cover both local and global structure of social networks. CARE builds customized paths, which are consisted of local and global structure of network nodes, as a basis for network embedding and uses the Skip-gram model to learn representation vector of nodes. Subsequently, stochastic gradient descent is applied to optimize our objective function and learn the final representation of nodes. Our method can be scalable when new nodes are appended to network without information loss. Parallelize generation of customized random walks is also used for speeding up CARE. We evaluate the performance of CARE on multi label classification and link prediction tasks. Experimental results on various networks indicate that the proposed method outperforms others in both Micro and Macro-f1 measures for different size of training data.

We propose an estimator of prediction error using an approximate message passing (AMP) algorithm that can be applied to a broad range of sparse penalties. Following Stein's lemma, the estimator of the generalized degrees of freedom, which is a key quantity for the construction of the estimator of the prediction error, is calculated at the AMP fixed point. The resulting form of the AMPbased estimator does not depend on the penalty function, and its value can be further improved by considering the correlation between predictors. The proposed estimator is asymptotically unbiased when the components of the predictors and response variables are independently generated according to a Gaussian distribution. We examine the behaviour of the estimator for real data under nonconvex sparse penalties, where Akaike's information criterion does not correspond to an unbiased estimator of the prediction error. The model selected by the proposed estimator is close to that which minimizes the true prediction error. In recent decades, variable selection using sparse penalties, referred to here as sparse estimation, has become an attractive estimation scheme [1, 2, 3]. The sparse estimation is mathematically formulated as the minimization of the estimating function associated with the sparse penalties. In this paper, we concentrate on the linear regression problem with an arbitrary sparse regularization.

Networks are a fundamental model of complex systems throughout the sciences, and network datasets are typically analyzed through lower-order connectivity patterns described at the level of individual nodes and edges. However, higher-order connectivity patterns captured by small subgraphs, also called network motifs, describe the fundamental structures that control and mediate the behavior of many complex systems. We develop three tools for network analysis that use higher-order connectivity patterns to gain new insights into network datasets: (1) a framework to cluster nodes into modules based on joint participation in network motifs; (2) a generalization of the clustering coefficient measurement to investigate higher-order closure patterns; and (3) a definition of network motifs for temporal networks and fast algorithms for counting them. Using these tools, we analyze data from biology, ecology, economics, neuroscience, online social networks, scientific collaborations, telecommunications, transportation, and the World Wide Web.

The rise of artificial intelligence will help improve productivity and lead to equitable development, said Prime Minister Narendra Modi at the University of Mumbai on Sunday. Modi, who inaugurated the Wadhwani Institute for Artificial Intelligence on the Kalina campus of the university, downplayed fears of humans losing jobs to robots. "With each wave of new technology, new opportunities arise. It opens an entirely new paradigm of opportunities. New opportunities have always outnumbered old ones," said Modi. "This optimism spells from my firm faith in the ancient Indian thinking that blended science and spirituality and found harmony between the two for the greater good of mankind," he said.

Robots swarming in 2018 Winter Olympics in PyeongChang to serve visitors, athletes and delegates with food, drinks and directions show South Korea's automaton way of hosting the Winter Olympic Games for the first time, with the country deciding to deploy at least 12 kinds of robots presented here in a list below: Korea Advanced Institute of Science and Technology's (Kaist's) Hubo robot is the first unit deployed by the country for the event. In December last year, the humanoid carried the Olympic torch, clearing a path for itself through a makeshift wall, set up as an obstacle, to hand the torch to a Kaist professor. In 2015, the humanoid had won Kaist a prize of $2 million by not falling off in order to complete simple tasks like opening doors. The robot also features a Bumble Bee-like ability to switch back and forth from a walking biped to a wheeled machine. The second robot deployment FX-2, that carried the professor who received the Olympic torch from the humanoid, is an eight-foot-tall human-operated robot weighing more than 600 pounds with a price tag close to $1 million.

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.