The international audience at the recent World Summit AI 2017 in Amsterdam, The Netherlands, reportedly topped out at 2,400. Not bad for an inaugural event, writes Jeremy Cowan, editorial director of IoT Now. And it was the goal of a panel of AI heavyhitters to inspire. Amazon's Ralf Herbrich joked that it might be quicker to say where artificial intelligence (AI) is not impacting Amazon's business. He cited current uses of AI in areas as diverse as demand pricing, fresh fruit ripeness prediction, and in Alexa.

Britain and France's leading tech sectors will be brought closer together with plans for a digital conference - or digital colloque - to promote deeper integration in the digital economy, the Digital, Culture, Media and Sport Secretary Matt Hancock (pictured with his team) has announced. The UK tops the list in Europe for global tech investors, with its tech firms attracting more venture capital funding than any other European country in 2017. In December it was named by Oxford Insights as the best prepared country in the world for artificial intelligence (AI) implementation. France has made big strides in creating new tech businesses and encouraging entrepreneurs, with Paris's newly built Station F, a former railway station hosting startups, multinationals and investors, symbolising the country's ambition. Mr Hancock met his French counterpart, Françoise Nyssen, at the UK France Summit hosted by the Prime Minister and the French President, Emmanuel Macron, at Royal Military Academy in Sandhurst.

According to sources in the recruitment industry, the year 2018 is going to witness a massive increase in demand for professionals with expertise in emerging technologies such as Artificial Intelligence (AI) and Machine Learning. Even though people specializing in Big Data and Analytics will still be sought after, AI and ML are going to be the next big thing. Robotics and automation have already made massive encroachment in the manufacturing sector. According to a study by Oxford University's Department of Engineering, nearly 47% jobs will face the risk of being automated, over a span of two decades. Employment in transportation, logistics and office administration is at a high risk of replacement.

After a recent seminar at Stanford, I had a chat with adjunct professor Jerry Kaplan about artificial intelligence embodiment. The question was: Who are the thinkers and companies who are really pushing AI theory? Some experts believe that for artificial intelligence or artificial general intelligence (AGI) to function peacefully and effectively in society, it needs a body. Thoughts vary on the level of embodiment, the mortality of that body, and the complexity of sensing abilities and empathy needed. So I did some research into the current state of robots and AI embodiment to identify the clusters and trends in this area of technology.

The Accenture Strategy report, Reworking the Revolution: Are you ready to compete as intelligent technology meets human ingenuity to create the future workforce?, estimates that if businesses invest in Artificial Intelligence (AI) and human-machine collaboration at the same rate as top performing companies, they could boost revenues by 38 percent by 2022 and raise employment levels by 10 percent. Collectively, this would lift profits by US$4.8 trillion globally over the same period. For the average S&P500 company, this equates to US$7.5 billion of revenues and a US$880 million lift to profitability. Both leaders and workers are optimistic about the potential of AI on business results and on work experiences, according to the study. Seventy-two percent of the 1,200 senior executives surveyed said that intelligent technology will be critical to their organization's market differentiation and 61 percent think the share of roles requiring collaboration with AI will rise in the next three years.

Netflix's long list of suggested movies and TV shows is a fantastic example of a personalized user experience. In fact, about 70 percent of everything users watch is a personalized recommendation, according to the company. Getting to that point hasn't been easy, and improving on its recommendation system is an ongoing process. Netflix has spent well over a decade developing and refining its recommendations. In 2006, it launched the Netflix Prize to search for machine learning experts who could improve its previous algorithm.

Switzerland's largest bank is expanding a site in Ticino to specialize in artificial intelligence. The move will create 80 new jobs in southern Switzerland. The University of Lugano IDSIA institute has advanced to a recognized research center for machine learning and artificial intelligence. Now, Zurich-based UBS is linking up with the institute, news agency «AWP» (behind paywall, in German) reported on Wednesday. The bank plans to expand a site in Manno, southern Switzerland, into a center for artificial intelligence, analytics and innovation.

Artificial intelligence and blockchain initiatives have emerged as critical new areas of focus for IT systems buyers in the UK, and across Europe, the Middle East and Africa (Emea), in the Computer Weekly/TechTarget IT Priorities survey for 2018. Discover how organisations are going about their BI and analytics on the newer data stores. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

Data association, the reasoning over correspondence between targets and measurements, is a problem of fundamental importance in target tracking. Recently, belief propagation (BP) has emerged as a promising method for estimating the marginal probabilities of measurement to target association, providing fast, accurate estimates. The excellent performance of BP in the particular formulation used may be attributed to the convexity of the underlying free energy which it implicitly optimises. This paper studies multiple scan data association problems, i.e., problems that reason over correspondence between targets and several sets of measurements, which may correspond to different sensors or different time steps. We find that the multiple scan extension of the single scan BP formulation is non-convex and demonstrate the undesirable behaviour that can result. A convex free energy is constructed using the recently proposed fractional free energy (FFE). A convergent, BP-like algorithm is provided for the single scan FFE, and employed in optimising the multiple scan free energy using primal-dual coordinate ascent. Finally, based on a variational interpretation of joint probabilistic data association (JPDA), we develop a sequential variant of the algorithm that is similar to JPDA, but retains consistency constraints from prior scans. The performance of the proposed methods is demonstrated on a bearings only target localisation problem.

The objective of this paper is to define an effective strategy for building an ensemble of Genetic Programming (GP) models. Ensemble methods are widely used in machine learning due to their features: they average out biases, they reduce the variance and they usually generalize better than single models. Despite these advantages, building ensemble of GP models is not a well-developed topic in the evolutionary computation community. To fill this gap, we propose a strategy that blends individuals produced by standard syntax-based GP and individuals produced by geometric semantic genetic programming, one of the newest semantics-based method developed in GP. In fact, recent literature showed that combining syntax and semantics could improve the generalization ability of a GP model. Additionally, to improve the diversity of the GP models used to build up the ensemble, we propose different pruning criteria that are based on correlation and entropy, a commonly used measure in information theory. Experimental results, obtained over different complex problems, suggest that the pruning criteria based on correlation and entropy could be effective in improving the generalization ability of the ensemble model and in reducing the computational burden required to build it.