We consider an online version of the robust Principle Component Analysis (PCA), which arises naturally in time-varying source separations such as video foreground-background separation. This paper proposes a compressive online robust PCA with prior information for recursively separating a sequences of frames into sparse and low-rank components from a small set of measurements. In contrast to conventional batch-based PCA, which processes all the frames directly, the proposed method processes measurements taken from each frame. Moreover, this method can efficiently incorporate multiple prior information, namely previous reconstructed frames, to improve the separation and thereafter, update the prior information for the next frame. We utilize multiple prior information by solving $n\text{-}\ell_{1}$ minimization for incorporating the previous sparse components and using incremental singular value decomposition ($\mathrm{SVD}$) for exploiting the previous low-rank components. We also establish theoretical bounds on the number of measurements required to guarantee successful separation under assumptions of static or slowly-changing low-rank components. Using numerical experiments, we evaluate our bounds and the performance of the proposed algorithm. In addition, we apply the proposed algorithm to online video foreground and background separation from compressive measurements. Experimental results show that the proposed method outperforms the existing methods.

In any deep learning project, it's almost impossible to imagine an upper limit on the amount of data needed for training models and conducting analyses. "We need to get more data," said Patrick Lucey, director of data science at sports consulting company STATS LLC in Chicago. We want to reconstruct that story, [and] tell better stories, and we're limited because we can't get all the data we want." Deep learning, as defined by the use of multiple machine learning algorithms, such as neural networks strung together, isn't necessarily a new concept. However, it started to gain more widespread traction last year, as researchers and enterprises realized that analytical models could be turned loose on the massive troves of data businesses had accumulated since the dawn of the big data era. Deep learning algorithms require experience to sharpen their recommendations, and big data provides them with exactly the fuel they need. But this raises the question of when is enough data enough? Some of the most prominent deep learning examples used hundreds of thousands, even millions of records during the model training process. At STATS, Lucey has access to ample data, but said he still feels models could function better with more. The company maintains databases of game data going back to its beginnings in 1981. Its deepest data sets go back to 2010 with the NBA, and come from its SportVU system, a network of cameras installed at sports arenas that captures player movement data. This wealth of data has enabled Lucey and his team to do some interesting things with deep learning. For example, he and his team developed a model that looks at video data from NBA games and analyzes players' body positions to better define what an open shot looks like. Another STATS project applied deep learning algorithms to English Premier League soccer. STATS analyzed data beyond traditional statistics, like shots and goals, to understand the factors that led to longshot Leicester City Football Club taking home the title in the league's 2015-2016 season, which ended last May. The data science team at STATS primarily builds models in open source tools, such as the Google-created TensorFlow and scikit-learn, a library of machine learning models built in Python. These projects have been successful, according to Lucey. However, he added that he's already looking to sharpen analyses, and he thinks more data will help. In addition to larger data volumes, more detailed information will be necessary, he noted. Deep learning algorithms thrive on detailed data as much as large amounts of data, and that will play an important role as these models continue to improve and describe the world more accurately. "That's the key -- finding that context," Lucey said. "You can get a good prediction, but if it's washed over by context, it's not as valuable.

He is also the main author of H2O's Deep Learning. Before joining H2O, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C /MPI and had access to the world's largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker.

Autonomous rubbish trucks could soon be putting binmen out of a job. Volvo has made a robot truck that makes picking up rubbish safer, swifter and more efficient - but it also cuts the manpower needed for the job. Sensors on the truck guide it around cluttered areas as the vehicle follows a pre-determined route from one bin to the next. It can drive unsupervised between two different bins, freeing up the driver to collect rubbish. Gearchanging, steering and speed are constantly optimised for low fuel consumption and emissions.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. With its sensing, mapping, and localization capabilities, the Google Tango platform has a lot of promise for robotics. Here's an update on some of what Google has been working on with it: And here's the talk that Johnny Lee from the Tango project gave at Google I/O earlier this month.

As a Venture Capital firm for Artificial Intelligence we follow the growing AI market closely. For the German AI Landscape Map, we created a list of over 600 European AI startups based on internal research mainly deriving from our network and Crunchbase. Not every company that lists AI as a part of their product has AI in it. We have therefore taken the freedom to clean the raw data. We've ended up with 81 German Artificial Intelligence startups, which made it onto our map.

A woman poses next to French designer Gael Langevin's InMoov robot after he unveiled it at a technology fair in Bucharest, Romania, Friday, May 26, 2017. Based on an idea developed from a prosthetic hand Langevin made in 2011, the first-ever made on a 3D printer, the robot can be programmed to speak English, Spanish, French, Russian and Dutch, with a basic model costing about 1,500 euros, $1,665.

For those of us who zone out during university lectures, the temptation multiplies when you taking classes from home. Next fall, a business school in France will try to stop online students from getting distracted with an AI app called Nestor. To judge your level of attention, it can track your face and eyes and even detect when you pull out a phone. If you start to slack off, it can warn you via pop-up messages or emails, and tell you roughly when you may drift away again. The bot will be used for two classes at the ESG Business school, including a 30 hour "street marketing" course, as part of a distance learning program.

The developmental trajectory of future apps will be heavily influenced by AI and machine learning, says a new report by F5 Networks, released ahead of the annual EMEA F5 Agility conference in Barcelona, Spain. The Future of Apps study, commissioned to The Foresight Factory, says developments in artificial intelligence and machine learning are likely to include more personalised, predictive services in areas such as cognitive health and finance. EMEA is already poised for the next wave of advances in AI, says the report. Nearly a third of surveyed respondents across Europe and South Africa say they use voice commands on their mobile devices. Already, some 10,000 third-party voice enabled apps are available for use with Amazon's Alexa at the end of Q1 2017.

While traditionally it has been the task of front-end developers to transform the work of designers from raw graphical user interface mockups to actual source code, this trend might soon be a thing of the past – courtesy of artificial intelligence. Copenhagen-based startup UIzard Technologies has leveraged the latest developments in the field of machine learning to build a neural network that, once fed with raw screenshots of graphical user interface, proceeds to automatically generate code. What is particularly intriguing is that the so-called Pix2Code model has the capacity to produce code for three different platforms, including Android and iOS as well as other web-based technologies. As UIzard founder Tony Beltramelli explains in his research, the novel approach could potentially "end the need for manually-programmed" user interfaces altogether. At present, the method generates code from screenshots with an impressive accuracy of over 77 percent, but the consistency of the algorithm is likely to improve in the future.