Google's algorithms can now parse the structure and meaning of simple language as expertly as a trained linguist. This mastery of grammar and syntax helps the company deliver more accurate search results, and it will be increasingly important as more of its devices and services come to depend on voice control. Starting today, Google is opening up those algorithms to outside software developers. The tools released will help programmers build language-based apps and services that are less prone to annoying misunderstandings than many of today's chatbots. And it should help get developers hooked on the powerful machine-learning techniques Google is honing.

Tesla Motors (TSLA) partner Nvidia (NVDA) rocketed late Thursday after the maker of graphics chips beat Q1 sales expectations and topped earnings views by a penny, led by faster adoption of artificial intelligence technology that utilizes Nvidia graphics chips. In after-hours trading after its earnings release, Nvidia stock was up nearly 6%, rebounding from a 1.4% dip, to 35.57, in the regular session. Shares are up 8% for the year. For Q1, Nvidia reported 1.3 billion in sales and 33 cents earnings per share, up a respective 13% and 38% vs. the year-earlier quarter, and topping the consensus of 26 analysts polled by Thomson Reuters for 1.26 billion and 32 cents. CEO Jen-Hsun Huang credited accelerated growth of deep-learning, or AI, technology for the Q1 beat.

If big data is overhyped, AI or deep learning are stratospherically so. Things were relatively controlled until Facebook started talking up its Messenger bots, and all rational talk (or thought) ended. To get a little common sense on AI, I reached out to Louis Monier, perhaps most famous as the founder of the Altavista search engine, the Google of its time, and currently the chief scientist at Import.io, a web-based platform for extracting data from websites without writing any code. Monier is one of the world's leading authorities in deep learning, with research roots going all the way back to Xerox PARC in the early 1980s. While Monier acknowledges the "stunning applications" that AI facilitates, he's also cognizant of the perils it presents to outmoded labor markets.

Google filed a report with the California DMV Feb. 23 stating that a Lexus it was testing had tried to pass some sandbags in a wide lane and ended up hitting the side of a bus on Valentine's Day. No one was hurt, Google said in a written statement. FILE - In this Wednesday, May 13, 2015, file photo, Google's self-driving Lexus car drives along street during a demonstration at Google campus on in Mountain View, Calif. As Google cars encounter more and more of the obstacles and conditions that befuddle human drivers, the autonomous vehicles are likely to cause more accidents, such as a recent low-speed collision with a bus. PHOENIX -- Do you have a clean driving record and type 40 words per minute?

The concept of "machine learning" has tremendous potential to help health insurers leverage data and improve care, though one prominent insurance CEO argues that such disruptive technologies will never be able to replace the valuable role of clinicians. At UPMC, the Pittsburgh-based integrated health system's investment in big data analytics gave it a " 1.6 billion advantage," Pamela Peele, chief analytics officer for the company's Insurance Services Division, tells Healthcare Finance. Peele's team, she says, invented its own models that marry predictive analytics with claims and local demographic data. Then machine learning--a process in which software roots out trends that the system can act on--analyzes the data. For example, UMPC conducted "pure text mining" in about half a million clinical notes in members' electronic medical records to look for word signals that indicate a patient will show up in the emergency department in the near future, Peele tells the publication. Humana is also investing heavily in big data, CEO Bruce Broussard writes in a recent LinkedIn post.

Will data scientists disappear soon? I am asking the question as I see more and more papers about why data scientists may be a parenthesis in history. Latest I read is Will The'Best Job Of 2016' Soon Become Redundant? To his point, there is indeed a number of software and cloud services aiming at automating data science. Marr cites IBM Watson Analytics as a great example of this.

A single node in a simple neural network takes some number of inputs, and then performs a weighted sum of those inputs, multiplying them each by some weight before adding them all together. Then, some constant (called "bias") is added, and the overall sum is then squashed into a range (usually -1 to 1 or 0 to 1) using a nonlinear activation function, such as a sigmoid function.

While CRISPR, nanobots and head transplants are making headlines as medical breakthroughs, a number of new technologies are also making progress tackling some of the toughest age-old diseases still plaguing millions of people in the poorest parts of the world. In low income countries, over 75% of the population dies before the age of 70 due to infectious diseases including HIV/AIDS, lung infections, tuberculosis, diarrheal diseases, malaria, and increasingly, cardiovascular diseases. Over a third of deaths in low income countries are among children under age 14 primarily due to pneumonia, diarrheal diseases, malaria and neonatal complications. In the developed world, those living in extreme poverty, such as homeless populations, also die on average at age 48. Over the last year, artificial intelligence, robotics and biotechnology have all generated a number of new solutions that have the potential to dramatically reduce these problems.

IBM is teaming up with eight North American universities to further tune its cognitive system to tackle cybersecurity problems. Watson for Cyber Security, a platform already in pre-beta, will be further trained in "learning the nuances of security research findings and discovering patterns and evidence of hidden cyber attacks and threats that could otherwise be missed". IBM will work with eight US universities from autumn onwards for a year in order to push forward the project. The universities selected are California State Polytechnic University, Pomona; Pennsylvania State University; Massachusetts Institute of Technology; New York University; the University of Maryland, Baltimore County (UMBC); the University of New Brunswick; the University of Ottawa; and the University of Waterloo. The project is ultimately designed to bridge the cyber-security skills gap, a perennial issue in the industry.

Fifteen years of technology craziness and we've finally arrived squarely in the year 2001. We're back to the future because Silicon Valley in particular (and tech in general) has hyped Big Data for several years without answering the vital question, "To what end?" Having vast amounts of data was at first a problem, then an opportunity, before it settled in as an über-talked-about way to sell just about any variety of software. You Valley types know exactly what I'm talking about. So why 2001, and June 29, 2001 to be more specific? That's the moment that the hype over artificial intelligence hit its high water mark with the release of Spielberg's film, A.I. Artificial Intelligence.