Taking a vehicle to the race track to improve it has been a thing for almost as long as we've had cars. Henry Ford built his brand's name on his early racing exploits, and so have countless others. So it's natural that some of the people interested in self-driving cars have been thinking about how the track can benefit this new technology. Joshua Schachter is one such person, and he's organizing the first autonomous track day, to be held on May 28th-29th at Thunderhill Raceway in Willow, California. Self-driving cars and racing are two of my favorite things, so I spoke to Schachter to find out more.

This is a good question, and one that seems to get raised time and time again. Myself and a colleague (Sven Crone from Lancaster University in the UK) published a paper on this issue last year in the International Journal of Forecasting. A summary of our findings can also be found in the book "Credit Scoring, Response Modeling and Insurance Rating. There are also some very good papers by G. Weiss from 2004/5 which are highly cited and referenced in our paper/book. What we found was that for some methods of model construction sample imbalance was not an issue at all – not even a tiny amount.

A team of scientists from the Moscow Institute of Physics and Technology (MIPT) have created prototypes of "electronic synapses" based on ultra-thin films of hafnium oxide (HfO2). These prototypes could potentially be used in fundamentally new computing systems. The paper has been published in the journal Nanoscale Research Letters. The group of researchers from MIPT have made HfO2-based memristors measuring just 40x40 nm2. The nanostructures they built exhibit properties similar to biological synapses.

Unless otherwise specified, we will refer to non-GAAP metrics on the call. The non-GAAP measures exclude the net impact from revenue deferrals and the impact of integration and restructuring charges. The non-GAAP financial measures provided should not be considered as a substitute for or superior to the measures of financial performance prepared in accordance with GAAP. They are included as additional clarifying items to aid investors in further understanding the company's third-quarter performance in addition to the impact that these items and events had on the financial results. All growth comparisons we make on the call relate to the corresponding period of last year unless otherwise noted.

Facebook on Tuesday announced the opening of a new artificial intelligence lab in Paris to expand a push to make its online social network smarter and more profitable. The new Paris AI lab--the third after two it operates in the United States--has six researchers at work and will more than double that number by the end of the year, executives from the US-based company said. The recruits will come from France's top public and private technological institutions. Since 2013, Facebook has been looking to push the envelope for artificial intelligence. It hired Yann LeCun, a renowned French professor at New York University specialised in "deep learning" algorithms, to run the initiative.

PAVIS department at Istituto Italiano di Tecnologia (IIT) (http://www.iit.it/pavis) is looking for a highly qualified candidate with a strong background in Computer Vision, Pattern Recognition and Machine Learning, with particular emphasis on recognition, video analysis, behavior understanding and prediction. As the activities may be carried out in collaboration with other research units inside IIT, previous multidisciplinary experience is an added value which will be duly considered. The main mission of PAVIS (Pattern Analysis and Computer Vision) is to design and develop innovative video surveillance systems, characterized by the use of highly-functional smart sensors and advanced video analytics features. PAVIS also plays an active role in supporting the other research units inside IIT providing scientists in Neuroscience, Nanophysics and other departments/centers with ad-hoc solutions. To this end, the group is involved in activities concerning computer vision and pattern recognition, machine learning, multimodal\multimedia data analysis and sensor fusion, and embedded computer vision systems.

Business intelligence has gone from static reports that tell you what happened, to interactive dashboards where you can drill into information to try and understand why it happened. New big data sources, including Internet of Things (IoT) devices, are pushing businesses from those reactive analytics – whether you look back once a month to spot trends or once a day to check for problems – to proactive analytics that give you alerts and real-time dashboards. That makes better use of operational data, which is more useful while it's still current, before conditions change. "There's a demand for real-time dashboards," says Herain Oberoi from Microsoft's Cortana Analytics team. "A lot of businesses want to get the pulse of their business. But dashboards show things that have already happened."

Artificial Intelligence (AI) is making progress in great strides, or at least it appears so! Almost no week passes by without some major announcements of new challenges solved by AI technology or new products powered by AI. Indeed many quantifiable factors attest an unprecedented level of activity: capital investments, number of academic papers, number of products involving AI technology, they all are on a steep rise in the past five years. Computers are already very capable at some specialized tasks that require reasoning and other abilities that we typically associate with intelligence. For example, computers can play a decent game of chess or can help us order our holiday photos. Despite this genuine progress, we are still a long way from human level intelligence because our best artificial intelligence systems are not general purpose. They cannot quickly adapt to novel tasks the way most humans can do.

Scientists at Glasgow University have established a world first by cracking the communication code of our brains. Pioneering research in the field of cognitive neuroimaging has revealed how brains process what we see. The work has been led by Prof Philippe Schyns, the head of Glasgow's school of psychology, with more than a little help from Voltaire and Salvador Dali. How Dali's mind worked is a matter of continuing conjecture. But one of his works has helped unlock how our minds work.

Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context, and successful approaches are known to benefit many applications in Natural Language Processing. Although supervised learning has been shown to provide superior WSD performance, current sense-annotated corpora do not contain a sufficient number of instances per word type to train supervised systems for all words. While unsupervised techniques have been proposed to overcome this data sparsity problem, such techniques have not outperformed supervised methods. In this paper, we propose a new approach to building semi-supervised WSD systems that combines a small amount of sense-annotated data with information from Word Sense Induction, a fully-unsupervised technique that automatically learns the different senses of a word based on how it is used. In three experiments, we show how sense induction models may be effectively combined to ultimately produce high-performance semi-supervised WSD systems that exceed the performance of state-of-the-art supervised WSD techniques trained on the same sense-annotated data. We anticipate that our results and released software will also benefit evaluation practices for sense induction systems and those working in low-resource languages by demonstrating how to quickly produce accurate WSD systems with minimal annotation effort.