UserReplay announced today the addition of a machine learning feature to its existing solution in order to better assist companies with gaining insight into their customers' online experiences and resolve issues in real time. UserReplay machine learning uncovers hard-to-discover revenue opportunities hidden in the powerful data set captured by UserReplay. The biggest benefit of UserReplay machine learning is its systematic and automatic uncovering of pain points that requires no human intervention. Analysts and consultants can now spend their time on more valuable endeavors while still gleaning comprehensive insights from mining customer experience data. For example, a leading national retailer's website was causing customers to see the error message "Sorry, some of the items in your basket just sold out." However, there was no indication of which item was sold out and none of the items when checked individually showed a stock warning.

If you really want to impress your friends and confound your enemies, you can invoke tensor products… People run in terror from the symbol. He was explaining some aspects of multidimensional Fourier transforms, but this comment is only half in jest; people get confused by tensor products. People who really understand tensors feel obligated to explain it using abstract language (specifically, universal properties). And the people who explain it in elementary terms don't really understand tensors. This post is an attempt to bridge the gap between the elementary and advanced understandings of tensors. We'll start with the elementary (axiomatic) approach, just to get a good feel for the objects we're working with and their essential properties.

Conversational interfaces will revolutionize the way we interact with computers, bringing artificial intelligence into everyday conversations and transactions. At Bot Day, you'll hear in-depth presentations from experts on how to implement AI-driven chatbots, with guidance on technology and frameworks, user experience, and business implications. Make plans to join us October 19, 2016 at the Mission Bay Conference Center in San Francisco for the first-ever O'Reilly Bot Day.

Deep learning can be understood as modeling high-level abstractions using data and a set of algorithms, with a deep graph and multiple processing layers of linear and non-linear equations. While it is largely a mathematical tool, the things that deep neural networks can do have surprised mathematicians. How can networks arranged in layers be quick to perform human tasks like face and object recognition if it has to go through layers of computations? This has perplexed mathematicians for sometime. However, what mathematics cannot makes sense of, physics explains simply.

U.S. chipmaker Nvidia nvda showed off on Monday a smaller and more efficient artificial intelligence computer for self-driving cars, saying it would power Baidu's bidu mapping and autonomous vehicle technology. Chinese web services company Baidu will deploy Nvidia's new Drive PX 2 as its in-vehicle car computer for its self-driving system, Nvidia said in a press release as it unveiled the computer at the GPU Technology Conference in Beijing. As more carmakers develop plans for self-driving technology to roll out in their vehicles in the next decade or less, Nvidia is trying to lower the barriers to entry, providing powerful computers to help automakers enter the market. Earlier this month, Nvidia and Baidu announced a partnership to develop a full self-driving car architecture from the cloud to the vehicle using both companies' expertise in artificial intelligence (AI). Nvidia said its new Drive PX 2 computer uses 10 watts of power and is half the size of the original version, launched in January.

Ever-increasing workload combined with dramatic reimbursement changes are driving development of methods that help clinicians gain value from medical images more efficiently. The brightest hope is artificial intelligence, fueled by advances in deep learning / convolutional neural networks. While the big vendors have made significant investments (Watson Health, GE Healthcare, Siemens etc.) and the usual academic groups are well represented, there is considerable activity at the smaller end of the scale. In preparation for the SIIM Conference on Machine Intelligence in Medical Imaging event in Alexandria, 12th -13th September we pulled together a list of the various start-ups working on machine learning solutions for medical imaging. It was surprising to see how many more have come out of stealth mode since RSNA last year – how many more are waiting in the wings?

Many consider artificial intelligence (AI) and machine learning, manifesting in the form of chatbots, the next great evolution of digital business (and pretty much all business for that matter). While the prospects can be both exciting and frightening for many (particularly those in the customer service realm), and the technology itself is still somewhat rudimentary/basic (dumb) in most cases, the potential is most certainly there and the savviest enterprises recognize it and are doing all they can to get involved in the development of their own bot offerings. It is unlikely of course that most enterprises will have the resources available to staff a team of AI experts and invest in the appropriate machine learning technologies to capitalize on the trend, but fortunately, there are numerous solutions available to make the release of a chatbot an actual reality. Let's take a closer look at some of the solutions Web professionals will discover as they research the opportunity. Before heading in to the world of bots, let's take a a moment to explore what we're dealing with.

LONDON The entrepreneur behind British software company Autonomy has invested in a start-up called Luminance that uses artificial intelligence to read documents and speed up the legal process around deals, potentially cutting out some lawyers. Mike Lynch, who founded technology investment fund Invoke Capital after leaving Autonomy, said on Wednesday he saw great potential in Luminance, which was set up by Cambridge University students and has worked with lawyers at Slaughter and May to develop software to analyze documents. "You can see the excitement around things like driverless cars, so this being all about contracts doesn't sound quite as riveting, but I actually think it's going to have a very big impact" Lynch told Reuters. "Lawyers will be able to do the things that matter rather than the grunt work, they can add better value by doing better analysis of what is found, rather than trying to plough through 50,000 documents." Founded by a combination of lawyers, experts in deals and mathematicians, Luminance has created software that it says can read and understand hundreds of pages of documents every minute, with clients charged according to usage.

The fitness class at the Do Life Shinagawa nursing home begins at 10:30 a.m., drawing about two dozen eager participants -- many of them pushing 70, 80 and even 90 years of age. At the front of the room is a human instructor, but it is the 16 inch tall robot standing on a table to his right that the crowd is paying attention to. The sleek, white humanoid robot is turned on and with fluid motion, stands up and spreads out its arms. "Why are you waking me up?" the robot asks in Japanese in an almost child-like voice. "Oh, you want to exercise?"

Google has been offering public cloud services for several years now, but the company has continued to lag behind Amazon and Microsoft in customer growth. Under the leadership of VMware co-founder Diane Greene, who serves as the executive vice president of Google Cloud Enterprise, the tech titan has focused harder on forging partnerships and developing products to appeal to large customers. It has added a number of key customers under Greene's tenure, including Spotify. One such win is Evernote, which announced Tuesday it would be migrating its service away from its private data centers and to Google's public cloud. When Evernote was looking for a public cloud provider, the company was interested in not only the base level infrastructure available, but also high-level machine learning services and services for building machine learning-driven systems, said Anirban Kundu, Evernote's CTO.