Microsoft has today announced that it will be moving its machine learning Computational Network Toolkit (CNTK) from its own hosting site, CodePlex, to GitHub and the MIT open source license. The move marks an effort to make it easier for developers to collaborate on building their own deep learning applications using the CNTK. Under the CodePlex license, access was restricted to academics only and it was wholly targeted to this audience. Now opening the project to GitHub, Microsoft hopes to attract a greater number, and a wider variety, of developers. Microsoft chief speech scientist, Xuedong Huang, underlined in today's announcement that its CNTK is highly optimised for speed – "The CNTK toolkit is just insanely more efficient than anything we have ever seen." He refers here to projects under way at Google (TensorFlow) and elsewhere, including Torch, Theano and Caffe.

Commercially available motorized microscopes can yield data at a throughput of 105 images per day, raising a strong need for automated data analysis (Conrad and Gerlich, 2010; Lock and Strömblad, 2010). Computational data analysis not only reduces the workload for the experimentalist, but also ensures objectivity and consistency in the annotation of large data sets (Danuser, 2011). The complexity and diversity in microscopic image data, however, poses challenges for developing suitable data analysis workflows. Bioimage informatics methods offer powerful solutions for specific image analysis tasks, such as object detection, motion analysis or measurements of morphometric features (Danuser, 2011; Murphy, 2011; Eliceiri et al., 2012; Myers, 2012). Most image analysis algorithms, however, have been developed for specific biological assays.

Welcome back to our series of articles sponsored by Intel – "Ask a Data Scientist." Once a week you'll see reader submitted questions of varying levels of technical detail answered by a practicing data scientist – sometimes by me and other times by an Intel data scientist. Think of this new insideBIGDATA feature as a valuable resource for you to get up to speed in this flourishing area of technology. If you have a big data question you'd like answered, please just enter a comment below, or send an e-mail to me at: daniel@insidehpc.com. This week's question is from a reader who wants an explanation of the "bias vs. variance tradeoff in statistical learning."

Python's growing adoption in data science has pitched it as a competitor to R programming language. With its various libraries maturing over time to suit all data science needs, a lot of people are shifting towards Python from R. This might seem like the logical scenario. But R would still come out as the popular choice for data scientists. People are shifting towards Python but not as many as to disregard R altogether. We have highlighted the pros and cons of both these languages used in Data Science in our Python vs R article.

Parsers are what allow applications like Google Now and Siri to understand the words you are either speaking or typing, label each word by its syntax and then discern your intent from what you've said/typed. The problem is, sentences become more complex and harder to decipher the longer they are. A sentence comprised of just 20 words may have hundreds of different syntax interpretations. Luckily, Parsey McParseyface is ranked as the most accurate parsing model currently available with a comprehension accuracy of about 95 percent.

This self-driving race car runs on NVIDIA's Pascal-based Tegra processors. Graphics processor specialist NVIDIA reported results on Thursday night, covering the first quarter of fiscal year 2017. In after-market trading, investors reacted to the report with an instant share-price boost of more than 7%. Sales exceeded the top-end of management's guidance for the quarter. GAAP earnings were expected to stop at roughly 190 million.

Infosys Ltd's embrace of automation and artificial intelligence technologies to boost employee productivity is taking longer than expected. India's second-largest software services exporter now expects the related productivity boost to reflect in a meaningful way only from April 2017. Chief executive officer Vishal Sikka had told Mint last October that he expected any "meaningful" impact to start reflecting by the end of March 2016. The development underlines the daunting task faced by Sikka, who is trying to put Infosys back on the global software services map and help the firm retain the bellwether tag in India's 146 billion outsourcing sector. Understandably, the theme of large-scale adoption of automation at the employer of more than 194,000 people was central to the US-based Sikka's five review meetings (including one with company's human resources head Kris Shankar) on his day-long trip to Bengaluru last Saturday.

When you inform Siri to set an alarm for five am, she'll set an alarm for five am. However for those who begin asking her which prescription ache killer is least more likely to upset your abdomen, she's probably not gonna know what to do--simply because that's a reasonably difficult sentence. Siri is a great distance from what laptop scientists name "pure language understanding." She will be able to't actually perceive the pure manner we people discuss--regardless of the way in which Apple portrays her in all these TV advertisements. In actual fact, we shouldn't actually be speaking about her as a "her" in any respect.

This self-driving race car runs on NVIDIA's Pascal-based Tegra processors. Graphics processor specialist NVIDIA (NASDAQ:NVDA) reported results on Thursday night, covering the first quarter of fiscal year 2017. In after-market trading, investors reacted to the report with an instant share-price boost of more than 7%. Sales exceeded the top-end of management's guidance for the quarter. GAAP earnings were expected to stop at roughly 190 million.

In a standing room only theater, a dozen Austin startups, ranging from emerging companies to bigger ventures, received awards Wednesday night as part of the Austin A-List of the Hottest Startups. The Austin Chamber, through its Innovate Austin initiative, and South by Southwest Interactive put the event on every year at ACL Live at the Moody Theater. An estimated 750 people attended the event. A panel of independent judges reviewed about 200 companies to pick the winners in three investment stage categories: emerging, growth and scale. "The A-List reinforces our efforts to attract funding, talent, and companies which enhances our diverse tech and innovation community," Michele Skelding, senior vice president of Global Technology and Innovation, Austin Chamber, said in a news release.