Recommender systems are automated computer programs that match items to users in different contexts. Such systems are ubiquitous and have become an integral part of our daily lives. Examples include recommending products to users on a site like Amazon, recommending content to users visiting a website like Yahoo!, recommending movies to users on a site like Netflix, recommending jobs to users on LinkedIn, and so on. Given the significant heterogeneity in user preferences, providing personalized recommendations is key to the success of such systems. To achieve this goal at scale, using machine learning models to estimate user preference from feedback data is essential.
Video is the world's largest generator of data, created every day by over 500 million cameras worldwide. That number is slated to double by 2020. The potential there, if we could actually analyze the data, is off the charts. It's data from government property and public transit, commercial buildings, roadways, traffic stops, retail locations, and more. The result would be what NVIDIA calls AI Cities, a thinking robot, with billions of eyes trained on residents and programmed to help keep people safe.
Sometimes they act based on gut instinct rather than sound logic. That's why Sentient Technologies (known as Sentient.AI) of San Francisco is working on Sentient Investment Management, an artificial-intelligence program that decides which stocks to buy and sell. "Our AI system can be more consistent and reliable," says IEEE Fellow Risto Miikkulainen, the company's vice president of research. "It buys and sells stocks based on an entire history of data rather than, for example, on a single unreliable piece of information that a stockbroker might fixate on." The company's researchers have been developing the program for the past 10 years to manage its investments.
A robot conducts the Orchestra Filarmonica di Lucca at Teatro Verdi in Pisa, Italy, this September. The ongoing artificial-intelligence revolution will change almost every line of work, creating enormous social and economic opportunities -- and challenges. Some believe that intelligent computers will push humans out of the job market and create a new'useless class'; others maintain that automation will generate a wide range of new human jobs and greater prosperity for all. Almost everybody agrees that we should take action to prevent the worst-case scenarios. The automation revolution is emerging from the confluence of two scientific tidal waves.
As our Intel CEO Brian Krzanich discussed earlier today at Wall Street Journal's D.Live event, Intel will soon be shipping the world's first family of processors designed from the ground up for artificial intelligence (AI): the Intel Nervana Neural Network Processor family (formerly known as "Lake Crest"). This family of processors is over 3 years in the making, and on behalf of the team building it, I'd like to share a bit more insight on the motivation and design behind the world's first neural network processor. Machine Learning and Deep Learning are quickly emerging as the most important computational workloads of our time. These methods allow us extract meaningful insights from data. We've been listening to our customers and applying changes to Intel's silicon portfolio to deliver superior Machine Learning performance.
Intel on Tuesday is taking the wraps off of the Nervana Neural Network Processor (NNP), formerly known as "Lake Crest," a chip three years in the making that's designed expressly for AI and deep learning. Along with explaining its unique architecture, Intel announced that Facebook has been a close collaborator as it prepares to bring the Nervana NNP to market. The chipmaker also laid out the beginnings of a product roadmap. While there are platforms available for deep learning applications, this is the first of its kind -- built from the ground up for AI -- that's commercially available, Naveen Rao, corporate VP of Intel's Artificial Intelligence Products Group, told ZDNet. It's rare for Intel to deliver a whole new class of products, he said, so the Nervana NNP family demonstrates Intel's commitment to the AI space.
Tableau Software is the Apple of the analytics market, with a huge fan base and enthusiastic customers who are willing to stand in long lines for a glimpse at what's next. Last week's Tableau Conference in Las Vegas proved that once again with record attendance of more than 14,000. The Tableau fan boys and fan girls were not disappointed, as the company detailed plenty of new capabilities. The highly anticipated Hyper engine, for example, is now in beta release 10.5 and is sure to be generally available by early next year. Hyper solves Tableau performance problems when dealing with high-scale data extracts.
Marijuana is said to cause permanent damage to the brain and can make users dependent on it, a new study suggested. A team of neuroscientists wanted to determine what makes marijuana addictive through long-term exposure to the drug, according to research published Monday in the journal JNeurosci. Scientific research has previously confirmed that frequent marijuana use can lead to addiction, but this study provides further detail into why this outcome is possible. Researchers at Brigham Young University's (BYU) neuroscience department injected teenage male mice test subjects with tetrahydrocannabinol (THC) -- marijuana's active ingredient -- for a weeks time. BYU researchers examined the mice's brain's ventral tegmental area (VTA), a cluster of neurons positioned near the midline in the midbrain.
PEOPLE often marry people who are just like them – similar in terms of social background, world view and race. Online dating may be changing that, however, breaking us out of our existing social circles. Economists Josué Ortega at the University of Essex, UK, and Philipp Hergovich at the University of Vienna, Austria, suggest it could even lead to more integrated societies. Before the first dating websites appeared in the 1990s, most people would meet dates through existing networks of friends or colleagues. But the rise of dating sites like Match.com and apps like Tinder has made online dating the norm for many.
The iPhone X will change everything when it arrives next month. It'll herald in a brave new notch-filled world with no home buttons and Face ID, a new face-recognition technology that unlocks the phone when you look at it. Mere weeks away from launch and a month after Sen. Al Franken (D-MN) penned a letter to Apple CEO Tim Cook voicing privacy concerns over Face ID, Apple has finally responded to his questions in what's clearly a move to pacify any lingering fears over its new biometric technology. SEE ALSO: Why you'll be forced to buy a case for your iPhone X Apple provided Mashable with a copy of the letter Cynthia Hogan, the company's VP for Public Policy, sent to Sen. Franken. On behalf of Apple, Hogan reiterates how Face ID works using the iPhone X's TrueDepth camera and sensors to scan and analyze a user's face based on depth maps and 2D images it creates.