As more companies wade into the business of building artificial intelligence systems to help you drive (or do the driving for you), a startup founded by an ex-Apple computer vision specialist is open sourcing a huge dataset that can help them on their road to autonomy. Mapillary, a Swedish startup backed by Sequoia, Atomico and others that has built a database of 130 million images through crowdsourcing -- think open-source Street View -- is releasing a free dataset of 25,000 street-level images from 190 countries, with pixel-level annotations that can be used to train automotive AI systems. The Mapillary Vistas Dataset claims to be "the world's largest, most diverse dataset for object recognition on street-level imagery." As with the rest of Mapillary's photos, the startup builds its image database on top of Mapbox and OpenStreetMap maps. The dataset is free for both academic and commercial researchers, and if anyone wants to build the results into commercial products, they must pay a commercial license.

People in Singapore are going to start seeing this Peugeot SUV driving itself on (a couple of) roads soon. Europe's fifth largest carmaker has partnered with MIT self-driving spin-out Nutonomy, to test the Peugeot 3008 model in the island. Nutonomy, which has been testing its software in a handful of self-driving Mitsubishis in Singapore for the past half year, says the new Peugeots will drive on the same small set of roads in the 0.77 square mile one-north business district. PSA Group, which owns Peugeot, says it plans to use the opportunity to learn what components to build into future self-driving cars. The Peugeot 3008 won Car of the Year 2017 in Geneva, at the International Motor Show.

In April 2017, DeepMind CEO Demis Hassabis drew on his eclectic experiences as an AI researcher, neuroscientist and videogames designer to discuss what is happening at the cutting edge of AI research, including the recent historic AlphaGo Go, and its future potential impact on fields such as science and healthcare, and how developing AI can help human better understand the human mind and explore new knowledge. Demis Hassabis (born 27 July 1976) was born to a Greek Cypriot father and a Chinese mother and grew up in North London. A child prodigy in chess, Hassabis reached master standard at the age of 13 with an Elo rating of 2300 (at the time the second highest rated player in the world Under-14 after Judit Polgár who had a rating of 2335) and captained many of the England junior chess teams. Now he is a pioneer in artificial intelligence, a neuroscientist, computer game designer, entrepreneur, and world-class games player.

Text Analytics Poll Shows Le Pen Positioned to "Trump" Macron To Americans following the French Presidential Election taking place in less than a week, it might appear as though recent history is repeating itself. And in many ways, it is. The post Text Analytics Reveals Potential French Election Upset appeared first on OdinText.

Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called "ground truth" communities. This works well in synthetic networks with planted communities because such networks' links are formed explicitly based on those known communities. However, there are no planted communities in real world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. Here, we show that metadata are not the same as ground truth, and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structure.

In this paper, we propose a semi-supervised clustering method, CEC-IB, that models data with a set of Gaussian distributions and that retrieves clusters based on a partial labeling provided by the user (partition-level side information). By combining the ideas from cross-entropy clustering (CEC) with those from the information bottleneck method (IB), our method trades between three conflicting goals: the accuracy with which the data set is modeled, the simplicity of the model, and the consistency of the clustering with side information. Experiments demonstrate that CEC-IB has a performance comparable to Gaussian mixture models (GMM) in a classical semi-supervised scenario, but is faster, more robust to noisy labels, automatically determines the optimal number of clusters, and performs well when not all classes are present in the side information. Moreover, in contrast to other semi-supervised models, it can be successfully applied in discovering natural subgroups if the partition-level side information is derived from the top levels of a hierarchical clustering.

Summary: Just how accurate are algorithms at spotting fake news and are we ready to turn them loose to suppress material they don't find credible. Here are some considerations and stories about some of the companies trying to build these fact-checkers. The first two are in fact fake. You may actually have seen them during last fall's election coverage. The third headline however is true, but not with the implications you may think.

If your company is not embracing artificial intelligence, it is going to suffer. CIO Dive is no exception. We've reported on the growing reliance on artificial intelligence everywhere from call centers to cloud computing to the airline industry. Yet, the state of technology is still a long way from imbuing machines with human-level intelligence. It's even further from the merging of machine and humans that fans of the technological singularity believe will unlock our true potential and, eventually, immortality. Despite the remarkable victory that Google's AI-powered Alpha computer scored against the world's top Go player, there is healthy debate around when machines will be able to truly attain human-like intelligence.

It's known for flat-pack wood, but Ikea's made no secret of its digital future. The Swedish business was quick to embed wireless charging into its furniture, and has already launched its own affordable line of smart lights you can control from your phone. Now, the company's Copenhagen-based innovation lab is testing out new digital waters: it's collecting research on public perception on artificial intelligence (AI). Launched this weekend by Space10 (the same lab responsible for Ikea's glorious indoor Growroom), Do You Speak Human? is a global survey to inform Ikea's future. It poses questions that refer to how AI might find purpose in your home like "should your AI fulfil your needs before you ask?" and "should your AI prevent you from making mistakes?"

The Google Home speaker finally went on sale in the UK in April and while it's a neat little smart speaker, it has some catching up to do. The Amazon Echo has already managed to garner more than 10,000 skills and the Home has very few. While the search giant works with developers to build up its selection, there are still some very useful things you can do with the smart speaker. Here's a selection of what we think are the best. One of the big draws for Google Home is the ability to use voice control on Chromecast and Chromecast Audio devices.