If you've ever attended an AI conference, I bet you passed under the placid gaze of a chrome-plated humanoid, lovingly selected from an ocean of creepy robot stock images that marketing teams can't resist pasting on every billboard these days. Clearly, I'm personally guilty of using octarine-blue sci-fi art to lure weary travelers to my blog. It certainly works, which is why it's a pity that those images have next to nothing to do with AI. You'd think we'd all be more ashamed of ourselves, but don't worry, AI is too useful to go away, no matter how much we all cry wolf. Marketing folk run around trying to get your attention with sci-fi gimmicks, but the reason you'll stick around long enough to buy into AI is entirely different.

Rapid advances in computing and robotics have led to calls for government controls. Before acting, we need to distinguish among the many meanings and applications of the technology. New technologies often spur public anxiety, but the intensity of concern about the implications of advances in artificial intelligence (AI) is particularly noteworthy. Several respected scholars and technology leaders warn that AI is on the path to turning robots into a master class that will subjugate humanity, if not destroy it. Others fear that AI is enabling governments to mass produce autonomous weapons--"killing machines"--that will choose their own targets, including innocent civilians. Renowned economists point out that AI, unlike previous technologies, is destroying many more jobs than it creates, leading to major economic disruptions. There seems to be widespread agreement that AI growth is accelerating.

Interaction function (IFC), which captures interactions among items and users, is of great importance in collaborative filtering (CF). The inner product is the most popular IFC due to its success in low-rank matrix factorization. However, interactions in real-world applications can be highly complex. Many other operations (such as plus and concatenation) have also been proposed, and can possibly offer better performance than the inner product. In this paper, motivated by the success of automated machine learning, we propose to search for proper interaction functions (SIF) for CF tasks. We first design an expressive search space for SIF by reviewing and generalizing existing CF approaches. We then propose to represent the search space as a structured multi-layer perceptron, and design a stochastic gradient descent algorithm which can simultaneously update both architectures and learning parameters. Experimental results demonstrate that the proposed method can be much more efficient than popular AutoML approaches, and also obtain much better prediction performance than state-of-the-art CF approaches.

Matrix factorization methods are extensively employed to understand complex data. In this paper, we introduce the cross-product penalized component analysis (XCAN), a sparse matrix factorization based on the optimization of a loss function that allows a trade-off between variance maximization and structural preservation. The approach is based on previous developments, notably (i) the Sparse Principal Component Analysis (SPCA) framework based on the LASSO, (ii) extensions of SPCA to constrain both modes of the factorization, like co-clustering or the Penalized Matrix Decomposition (PMD), and (iii) the Group-wise Principal Component Analysis (GPCA) method. The result is a flexible modeling approach that can be used for data exploration in a large variety of problems. We demonstrate its use with applications from different disciplines.

The prediction of stock prices is an important task in economics, investment and financial decision-making. It has for several decades, spurred the interest of many researchers to design stock price predictive models. In this paper, the symbiotic organisms search algorithm, a new metaheuristic algorithm is employed as an efficient method for training feedforward neural networks (FFNN). The training process is used to build a better stock price predictive model. The Straits Times Index, Nikkei 225, NASDAQ Composite, S&P 500, and Dow Jones Industrial Average indices were utilized as time series data sets for training and testing proposed predic-tive model. Three evaluation methods namely, Root Mean Squared Error, Mean Absolute Percentage Error and Mean Absolution Deviation are used to compare the results of the implemented model. The computational results obtained revealed that the hybrid Symbiotic Organisms Search Algorithm exhibited outstanding predictive performance when compared to the hybrid Particle Swarm Optimization, Genetic Algorithm, and ARIMA based models. The new model is a promising predictive technique for solving high dimensional nonlinear time series data that are difficult to capture by traditional models.

Roughly half of the 821 million people considered hungry by the United Nations are those who dedicate their lives to producing food for others: farmers. This is largely attributed to the vulnerability of farmers to agricultural risks, such as extreme weather, conflict, and market shocks. Smallholder farmers, who produce some 60-70% of the world's food, are particularly vulnerable to risks and food insecurity. Emerging technologies such as Artificial Intelligence (AI), however, have been particularly promising in tackling challenges such as lack of expertise, climate change, resource optimization and consumer trust. AI assistance can, for instance, enable smallholder farmers in Africa to more effectively address scourges such as viruses and the fall armyworm that have plagued the region over the last 40 years despite extensive investment, said David Hughes, Co-Founder of PlantVillage and Assistant Professor at Penn State University at a session on AI for Agriculture at last week's AI for Good Global Summit.

We present RUSLAN -- a new open Russian spoken language corpus for the text-to-speech task. RUSLAN contains 22200 audio samples with text annotations -- more than 31 hours of high-quality speech of one person -- being the largest annotated Russian corpus in terms of speech duration for a single speaker. We trained an end-to-end neural network for the text-to-speech task on our corpus and evaluated the quality of the synthesized speech using Mean Opinion Score test. Synthesized speech achieves 4.05 score for naturalness and 3.78 score for intelligibility on a 5-point MOS scale.

Our USP in the PR market is to research to find the next influential technology and business leader who can support POC data to the media. In today's digital thunderstorm of news, publications, are more than ever, reliant on the principles of journalism. That is why we are excited to announce we have been nominated for Best Integrated Agency in the 2019 Prolific London Awards. Our mission to create dynamic client campaigns through digital innovation, keeps us at the forefront of leading business and technology media conversations and we are proud to share this nomination with our dedicated international team. Our work with UK based WAN Data Acceleration company Bridgeworks Ltd., has produced a thriving external communications strategy to attract multi-million dollar business contracts in global markets like the US, Europe and South Africa.

Today, can all companies use artificial intelligence (AI), regardless of their size or sector of activity? Nozha Boujemaa: Yes, in fact the difficulty does not come from AI itself but from the data. People have not yet understood how important their structuring is. A company that wants to use AI must be able to exploit data even if they are multi-source, so they must be structured. We always talk about algorithms, but the algorithm is only the engine.

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.