This intelligence, the Artificial General Intelligence, shall solve all our problems and the entire world is working on it. But the current Artificial Intelligence is mostly at the level of robotics. Fact is, that problem awareness is increased, we have recognized that we need to bundle our capacities to create something that helps mankind to find solutions against the most urgent problems, resource scarcity, world hunger, diseases, and social turmoil. In search of that Super Computer, the Super Intelligence, we forget that our planet has 7 billion super intelligences walking around and they are all waiting to give a meaning to their lives and bring in their cognitive-emotional talents: Humans. We try to walk with crutches whilst we already sit in a Formula 1 racing car.

The Volkswagen Group continues to drive the digital transformation forward and is expanding its competence with regard to artificial intelligence (AI), not only with a view to using this key technology in the field of autonomous driving and in production, but also to speed up corporate processes. To that end, Prof. Patrick van der Smagt will be joining the team of AI experts and data scientists at the Volkswagen Group's Data Lab in Munich from October, and will lead the AI team. Van der Smagt is Associate Professor at Technische Universität München and one of Europe's leading experts in deep learning and robotics. Dr. Martin Hofmann, Chief Information Officer at the Volkswagen Group, commented: "We are delighted that Professor van der Smagt will be joining the Data Lab. This shows how attractive the Volkswagen Group's IT labs are for distinguished scientists and experts in artificial intelligence. Top specialists from Volkswagen, industry partners and the academic world as well as startups jointly develop and test forward-looking ideas in our labs – working in an environment similar to Silicon Valley."

Most fintech companies offer better interfaces for handling money (bank accounts, loans, payments, etc.), but quite a few startups are also offering a solution for a much older need than how to make money. Israel company I Know First, managed by CEO Yaron Golgher, is one of these. The company's main product is an algorithm that provides a forecast for three thousand different investment instruments, including shares, commodities, interest rates, foreign currency, exchange traded funds (ETFs), and global indices. "The algorithm rates all the investment instruments, and singles out investment opportunities in the capital market on a daily basis, according to the pricing anomaly it finds," Golgher says in a "Globes" interview. Golgher: "The algorithm is self-learning. It is based on purely quantitative values, not reading news or any kind of analysis. There is no human factor here. The algorithm uses artificial intelligence, an area in which huge companies like Apple Computers, Google, and Facebook have recently been making massive investments. The algorithm was developed by our development team, headed by cofounder and CTO Dr. Lipa Roitman. Roitman is a scientist from the Weizmann Institute of Science with over 20 years of experience in the specific field of artificial intelligence, machine learning, and algorithms. The forecast is given for a period of time. What is interesting is that for every forecast, the algorithm also assigns a probability that the forecast will be fulfilled. Every customer can receive a forecast according to his investment preferences. For example, a person investing in technology shares can receive the best opportunities in this segment, a customer investing in commodities will receive the best opportunities in the commodities market, etc." "We have expanded this year to 12 new countries, including the US and Europe, with an emphasis on Italy and France, and Russia, too. We work with Latin America, especially Brazil. The business model is based on access to the algorithm to a varying extent, according to the customer's size."

Robot cars in the US will have to be fitted with black boxes that record what happens if they crash, under US policy covering the vehicles. The demand is part of a newly issued US Transportation Department policy covering autonomous vehicles. The guidelines will replace a patchwork of different, and often contradictory, rules drawn up by separate states. The US government plans to vet the code controlling robot cars before they win permission to drive alongside humans. "If a self-driving car isn't safe, we have the authority to pull it off the road," wrote President Barack Obama in an editorial for the Pittsburgh Post Gazette outlining the policy.

Contrary to what some people will try to tell you, there are Canadian values. We aren't just a collection of disparate people who happen to occupy the same country. We're one of the few nations on earth with a native-born and immigrant population that welcomes people from all over the world to come here, work hard, obey the law, live in peace and freedom, and help each other. To pick one of countless examples, Canada is Jamaican-Canadians extending a helping hand to a brilliant young Albanian-born computer student, whose family immigrated here 17 years ago when he was four, leaving behind the turmoil of the Kosovo war. Jurgen Aliaj, 21, is the student and the Jamaican-Canadians who are helping him head the Independent United Order of Solomon, a charity run by my friends Lloyd and Madaine Seivright.

AI has been making great strides in the past few years, beating humans at our own game, as well as augmenting and even replacing human controlled systems. However, some are still not impressed with these developments and feel more should be done. Such is the view of Professor Alexi Samsonovich, who announced that Russia "is on the verge" of a major AI milestone--robots that can feel human emotion! The announcement was made during the 2016 Annual International Conference on Biologically Inspired Cognitive Architectures (BICA) in New York City. Specifically, Samsonovich pointed to free thinking machines capable of feeling and understanding human emotions, understanding narratives and thinking in those narratives, as well as being capable to actively learn on their own.

It's difficult to predict how artificial intelligence technology will change over the next 10 to 20 years, but there are plenty of gains to be made. By 2018, robots will supervise more than 3 million human workers; by 2020, smart machines will be a top investment priority for more than 30 percent of CIOs. Everything from journalism to customer service is already being replaced by AI that's increasingly able to replicate the experience and ability of humans. What was once seen as the future of technology is already here, and the only question left is how it will be implemented in the mass market. Over time, the insights gleaned from the industries currently taking advantage of AI -- and improving the technology along the way -- will make it ever more robust and useful within a growing range of applications.

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have been recently introduced. In this work, we describe a novel parallel interacting MCMC scheme, called {\it orthogonal MCMC} (O-MCMC), where a set of "vertical" parallel MCMC chains share information using some "horizontal" MCMC techniques working on the entire population of current states. More specifically, the vertical chains are led by random-walk proposals, whereas the horizontal MCMC techniques employ independent proposals, thus allowing an efficient combination of global exploration and local approximation. The interaction is contained in these horizontal iterations. Within the analysis of different implementations of O-MCMC, novel schemes in order to reduce the overall computational cost of parallel multiple try Metropolis (MTM) chains are also presented. Furthermore, a modified version of O-MCMC for optimization is provided by considering parallel simulated annealing (SA) algorithms. Numerical results show the advantages of the proposed sampling scheme in terms of efficiency in the estimation, as well as robustness in terms of independence with respect to initial values and the choice of the parameters.

However, Oren Etzioni, professor of Computer Science at the University of Washington and CEO of the Allen Institute for Artificial Intelligence, argues that such headlines are in fact strongly influenced by the work of one man: professor Nick Bostrom of the Faculty of Philosophy at Oxford University, author of the bestselling treatise Superintelligence: Paths, Dangers, and Strategies. Essentially, Bostrom claims that if machine brains surpass human brains in general intelligence, the resultant new'superintelligence' could replace humans as the dominant lifeform on Earth. Furthermore, according to his findings, there's a 10-percent probability that human-level AI will be attained by 2022, a 50-percent probability that this feat will be achieved by 2040, and 90-percent probability that such an entity will be created by 2075. However, in his article published in the MIT Technology Review magazine Etzioni points out that Bostrom's main source of data is an aggregate of four different surveys of groups, including participants of the Philosophy and Theory of AI conference that was held in 2011 in Thessaloniki, and members of the Greek Association for Artificial Intelligence. Furthermore, it appears that Bostrom didn't provide the response rates or the phrasing of questions used during those surveys, and neither did he account for the reliance on data collected in Greece.

Demand for non-equity trading algorithms serving institutional asset managers and retail investors is expanding the prevalence of artificial intelligence in the world's financial markets. A recent report by Thomson Reuters estimates that algorithmic trading systems now handle 75 per cent of the volume of global trades worldwide and this figure is predicted, by those in the industry, to grow steadily. Firstly, while the institutional market has enjoyed a large variety of "algos" serving the equity markets to date, other areas such as futures are still witnessing huge product demand and innovation as a result. Secondly, regulations affecting the institutional investment market, such as the European Union Markets in Financial Instruments Directive II or MiFID II, are pushing for greater automation of trades in some asset classes which traditionally were not executed electronically. The fixed income market is a prime example and negotiations between industry groups are ongoing as to how practical a fully automated fixed income could really be, given the magnitude of the required shift from telephone to electronic trading.