The winds of change are blowing through Silicon Valley--not terribly unusual, granted, considering that something new seems to come out of that part of the world several times a year. But this time, we seem to be witnessing the opening of what promises to be a sea of change in computing: The joining of all our devices--including computers, smartphones, smart homes, and even smart cars--with the virtual synapses of a serviceable artificial intelligence, complete with realistic conversational abilities. We've already seen some earlier attempts at realizing this dream. But, some sassy dialogue notwithstanding, these machine "intelligences" have notorious limitations, something the designers of the newest entrant on the scene, Viv, hope to do away with once and for all. Viv's creators, Dag Kittlaus and Adam Cheyer--who also created Siri, before selling it to Apple--hope that their newest creation will become the portal through which users interface with the internet.

Get ready because artificial intelligence is on the White House's radar, and it's taking the field seriously. The White House this week announced the new National Science and Technology Council (NSTC) Subcommittee on Machine Learning and Artificial Intelligence will meet for the first time next week, and the White House Office of Science and Technology Policy will cohost four workshops to create a dialogue around the positives and negatives of artificial intelligence and machine learning. "Like any transformative technology … artificial intelligence carries some risk and presents complex policy challenges along several dimensions, from jobs and the economy to safety and regulatory questions," Deputy U.S. Chief Technology Officer Ed Felten wrote. "For example, A.I. will create new jobs while phasing out some old ones--magnifying the importance of programs like TechHire that are preparing our workforce with the skills to get ahead in today's economy, and tomorrow's." According to Felten, the new subcommittee is meant to monitor advances and milestones in the field, including in the federal realm, the private sector, and internationally.

A file photo dated 06 January 2004 shows Apple Computer's CEO Steve Jobs sitting in front of a project Apple Computer logo during his keynote speech at the MacWorld Expo in San Francisco, California, USA. Apple's (AAPL) epic fall on the stock market took a symbolic turn Thursday after its market value again dropped below its top rival's. That makes it second fiddle behind Google parent Alphabet (GOOGL) at 499.9 billion. Shares of Apple have been in freefall this year, dropping more than 14%, amid the company's disappointing first-quarter results. It's the second time this year Alphabet's market value has surpassed Apple's.

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.