If you think chatbots are hot right now -- being used in psychotherapy, turning into racist trolls, and presenting an existential threat to Apple -- just wait until they turn into full-fledged personal assistants. In five years time, digital personal assistants will be even more important than smartphones, says University of Washington computer scientist Pedro Domingos, author of "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World." "What you have right now on your smartphone is dozens of apps," Domingos tells Tech Insider, "with each app doing it's own thing." On any given Friday night, you use one app to find a restaurant, another to buy a movie ticket, another to figure out how to get to where you're going, and another to find a date to take out with you. "It's incredibly annoying," he says, since the apps "don't talk to each other and you have to learn all these different interfaces."

Google is expanding its free software to now include self-learning smart computers. TensorFlow, the company bringing this software to Google users, will allow for anyone with access to computer software to create their own smart computer from scratch that can program itself. Users can customize the settings to specify what programs they want the computer to learn, and it takes off from there. Learned skills can range anywhere from drawing and talking to recognizing pictures. Making these programs available to programmers aids the next frontier for many tech vendors, as "machine-learning tech" is allowing them to better integrate services into their apps.

Cleveland Clinic to use IBM Watson for Genomic Research: Researchers at Cleveland Clinic will use IBM Watson technology in the area of genomic research to help oncologists deliver personalized medicine by uncovering new cancer treatment options for patients. The Lerner Research Institute's Genomic Medicine Institute at Cleveland Clinic plans to evaluate Watson's ability to help oncologists develop more personalized care to patients for a variety of cancers. Clinicians lack the tools and time required to bring DNA-based treatment options to their patients and to do so, they must correlate data from genome sequencing to reams of medical journals, new studies and clinical records. At a time when medical information is doubling every five years, a faster option is needed. This use of Watson aims to find the "needle in the haystack" through identifying patterns in genome sequencing and medical data to unlock insights that will help clinicians bring the promise of genomic medicine to their patients.

Google claimed that TensorFlow's distributed architecture gives it a high level of flexibility in how coders define models that train the software. "To make TensorFlow easier to use, we have included Python libraries that make it easy to write a model that runs on a single process and scales to use multiple replicas for training".Distributed computing allows neural networks to learn much faster than the network running on one computer. Engineering leader of TensorFlow Rajat Monga said the reason why TensorFlow's multi-server version was delayed for release because they found it hard to adapt the open-source software to be usable outside of the highly customized data centers of Google. But for many researchers, its expense might as well place it in outer space.TensorFlow comes in a branch of artificial intelligence called deep learning, it works the same way human brain cells interact together.Equally, having access to the combined power of even a small cluster of computers, rather than relying on one machine, means that the overall data throughput of machine learning models and the speed at which they deliver accurate results can be accelerated.Regardless of the advanced feature, TensorFlow has already gained popularity for its software.The Verge has a report covering some of the more compelling projects that developers have created using TensorFlow.

Google claimed that TensorFlow's distributed architecture gives it a high level of flexibility in how coders define models that train the software. "To make TensorFlow easier to use, we have included Python libraries that make it easy to write a model that runs on a single process and scales to use multiple replicas for training".Distributed computing allows neural networks to learn much faster than the network running on one computer. Engineering leader of TensorFlow Rajat Monga said the reason why TensorFlow's multi-server version was delayed for release because they found it hard to adapt the open-source software to be usable outside of the highly customized data centers of Google. "It would have been extremely hard to just take that and make it open source". But for many researchers, its expense might as well place it in outer space.TensorFlow comes in a branch of artificial intelligence called deep learning, it works the same way human brain cells interact together.Equally, having access to the combined power of even a small cluster of computers, rather than relying on one machine, means that the overall data throughput of machine learning models and the speed at which they deliver accurate results can be accelerated.Regardless of the advanced feature, TensorFlow has already gained popularity for its software.The Verge has a report covering some of the more compelling projects that developers have created using TensorFlow.

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods. In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological "labels" to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses. There are other current techniques that don't use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

Technology makes some types of jobs obsolete and creates other types of jobs -- that's been true since the stone age. While in the past, machines have replaced people in jobs that require physical labor, we're increasingly seeing traditionally white collar jobs augmented by machines: financial analysts, online marketers, and financial reporters, just to name a few. Of course, these advances also create new jobs. The electronic computers that we know today, for example, replaced human beings performing the actual calculations, but in the process created all kinds of new types of work. Artificial intelligence seems like it might work the same way, creating jobs for artificial intelligence researchers and slowly displacing all other kinds of knowledge work.

'Machine learning' is a term that has been used in our industry since the early days of programmatic, and more recently'artificial intelligence' is being used, partly because it is much-discussed term in other parts of the tech world, but also because it accurately describes what some ad technology companies in the industry are doing. However, the two terms shouldn't be used interchangeably, as John Trenkle, TubeMogul's Chief Scientist, explains here. Trenkle himself has 30 years of experience in machine learning, during which time he has worked on everything from developing advanced data mining capabilities for NASA right through to developing systems that can recognise license plates in Arabic.

Real-world artificial-intelligence applications are popping up in unexpected places--and much sooner than you might think. While winning a game of Go might be impressive, machine intelligence is also evolving to the point where it can be used by more people to do more things. That's how four engineers with almost zero knowledge of Japanese were able to create software, in just a few months, that can decipher handwriting in the language. The programmers at Reactive Inc. came up with an application that recognizes scrawled-out Japanese with 98.66 percent accuracy. The 18-month-old startup in Tokyo is part of a growing global community of coders and investors who are harnessing the power of neural networks to put AI to far more practical purposes than answering trivia or winning board games.

The source image on the left was used to generate the one on the right. A British startup is using the unique abilities of convolutional neural networks to do a sort of scaled-up version of Adobe's content-aware fill -- but instead of filling in the gaps in a picture, it's imagining a whole new picture, larger and more detailed than the original. Kind of hard to believe without seeing it, right? That's why they call their company Magic Pony. Just emerging from semi-stealth mode (and even then, only barely), Magic Pony Technology's researchers have trained their system by exposing it to high- and low-resolution versions of images and video, letting it learn the differences between the two.