They might be small, but bark beetles can ruin a forest. In the US, tens of millions of acres have been devastated by them in the past decade alone. In Europe, however, a combination of drones and artificial intelligence might be giving trees a fighting chance. Bark beetles burrow into trees and lay eggs under the surface of the bark where the larvae feed on the tree's inner layers and eventually chew their way out. All of this damages the tree's vascular system, fatally weakening it.

Noninvasive brain–computer interface (BCI) systems can restore functions lost to disability -- allowing for spontaneous, direct brain control of external devices without the risks associated with surgical implantation of neural interfaces. But as machine-learning algorithms have become faster and more powerful, researchers have mostly focused on increasing performance by optimizing pattern-recognition algorithms. But what about letting patients actively participate with AI in improving performance? To test that idea, researchers at the École Polytechnique Fédérale de Lausanne (EPFL), based in Geneva, Switzerland, conducted research using "mutual learning" between computer and humans -- two severely impaired (tetraplegic) participants with chronic spinal cord injury. The goal: win a live virtual racing game at an international event.

Artificial Intelligence is already changing society. Algorithms and machine learning are trading millions of euros in financial markets; they are predicting what people want to search for online and what shows they might like to watch on Netflix; AI is already helping police identify criminals using facial recognition (albeit with mixed results), and sifting through climate change data. Soon, AI could be driving our cars and trains (even our ships and planes). How will these new technologies transform our workplaces, our homes, our cities, and our lives? Inevitably, there will be disruption.

The AI Expo Europe is set to arrive in the hub of AI innovation, Amsterdam, The Netherlands, in under 6 weeks' time and aims to'deliver AI for a smarter future.' The leading conference and exhibition will take place in the RAI, Amsterdam on 27-28 June, with over 8,000 attendees coming together to hear keynote presentations, panel discussions and to explore the start-up innovation area, focusing on the latest innovations in the AI & IoT landscape. The AI Expo Europe Conference and Exhibition is co-located with the IoT Tech Expo and the Blockchain Expo, so attendees can expect to learn about the convergence of three technologies (AI, IoT and blockchain) that are driving IT spending in 2018. Simon Poole, Head of Technology, Just Eat Simon Morel, Partner & Head of Product, BotSupply Dor Kedem, Senior Data Scientist, ING Filippo F.G. Della Casa, Head of Analytics, Leithà – Unipol Group Julio Peironcely, Head of Data Science team, Schiphol Airport Alex Sierkov, Director Product, Search and Machine Learning, HomeToGo Alexander Denev, Executive Director – Head of Quantitative Research and Advanced Analytics, IHS Markit Tom Ollerton, Innovation Director, We Are Social Daniel Gilbert, Director of Data, News UK Michael McDaid, Sales Director Europe, Progress DataRPM Britta Muzyk-Tikovsky, Managing Director, Capscovil Over the two days, attendees can get access to a co-located exhibition with over 300 exhibitors, 18 conference tracks with a brand-new AI agenda set to feature four important strands in the AI ecosystem. The AI Expo Europe is set to be the place to network, promote and showcase your brand alongside an audience of c-suite executives and venture capitalists.

Rats use brain cells called grid cells to help them navigate, and this ability has been recreated by an AI program.Credit: Al Fenn/LIFE Coll./Getty Scientists have used artificial intelligence (AI) to recreate the complex neural codes that the brain uses to navigate through space. The feat demonstrates how powerful AI algorithms can assist conventional neuroscience research to test theories about the brain's workings -- but the approach is not going to put neuroscientists out of work just yet, say the researchers. The computer program, details of which were published in Nature on 9 May1, was developed by neuroscientists at University College London (UCL) and AI researchers at the London-based Google company DeepMind. It used a technique called deep learning -- a type of AI inspired by the structures in the brain -- to train a computer-simulated rat to track its position in a virtual environment.

Some of the trends analyzed during the 4th edition of Madrid.AI included the possibilities of eye-tracking systems, the possibilities and limitations of Artificial Intelligence and the applications of Deep Learning in the real world.

Machine learning algorithms are everywhere. It is not just Facebook and Google. Companies are using them to provide personalized education services and advanced business intelligence services, to fight cancer and to detect counterfeit goods. The technology will make us collectively wealthier and more capable of providing for human welfare, human rights, human justice and the fostering of the virtues we need to live well in communities. We should welcome it and do all that we can to promote it. As with any new technology, there are ethical challenges.

Some progress has been made in encouraging girls to study physics at A-level, according to a report by the Institute of Physics (IoP). In 2016, 1.9% of girls chose A-level physics, up from 1.6% in 2011. But that compared with 6.5% for boys in 2016 and 44% of schools in England still send no girls at all to study the subject. The IoP said physics-based skills were essential for many future careers, from artificial intelligence to aerospace. However, the gender balance at physics A-level in England's schools has changed little in decades, with only 20% being female.

Spectral dimensionality reduction methods enable linear separations of complex data with high-dimensional features in a reduced space. However, these methods do not always give the desired results due to irregularities or uncertainties of the data. Thus, we consider aggressively modifying the scales of the features to obtain the desired classification. Using prior knowledge on the labels of partial samples to specify the Fiedler vector, we formulate an eigenvalue problem of a linear matrix pencil whose eigenvector has the feature scaling factors. The resulting factors can modify the features of entire samples to form clusters in the reduced space, according to the known labels. In this study, we propose new dimensionality reduction methods supervised using the feature scaling associated with the spectral clustering. Numerical experiments show that the proposed methods outperform well-established supervised methods for toy problems with more samples than features, and are more robust regarding clustering than existing methods. Also, the proposed methods outperform existing methods regarding classification for real-world problems with more features than samples of gene expression profiles of cancer diseases. Furthermore, the feature scaling tends to improve the clustering and classification accuracies of existing unsupervised methods, as the proportion of training data increases.

Emotion recognition has become a popular topic of interest, especially in the field of human computer interaction. Previous works involve unimodal analysis of emotion, while recent efforts focus on multi-modal emotion recognition from vision and speech. In this paper, we propose a new method of learning about the hidden representations between just speech and text data using convolutional attention networks. Compared to the shallow model which employs simple concatenation of feature vectors, the proposed attention model performs much better in classifying emotion from speech and text data contained in the CMU-MOSEI dataset.