How would you go about teaching an artificial intelligence how to love? Much has been written about Spike Jonze's Her, the Oscar-nominated tale of love between man and operating system. Poetic license aside, is that really possible? What computers lack are bodies. The thoughts and feelings and emotions we call "love" are not abstract experiences; they're intertwined with senses and hormones.

This is the latest installment in a regular series of conversations with William McDonough (@billmcdonough), designer, architect, author and entrepreneur. Joel Makower: Tell me about the innovation future roundtable you recently convened. Bill McDonough: I have been working with companies that are looking at the future of mobility in India, and designing factories and other things for them. The chairman said he would like to connect to some of the advanced thinking across many sectors and integrate that with some conversations that he could participate in. The first person I thought of for that was Jack Hidary.

Rembrandt was arguably the first artist to really master the "selfie," and he did so all the way back in the 1600s. Now, a team of technologists working with Microsoft are bringing Rembrandt's technique into the modern age--they have produced a 3D printed painting in the style of the Dutch master. "Our goal was to make a machine that works like Rembrandt," Emmanuel Flores, director of technology for the project, told the BBC. "We will understand better what makes a masterpiece a masterpiece." To accomplish this feat, data on Rembrandt's works was gathered by computers, which discovered patterns in how he would paint certain features, like facial features, for example. Then, machine-learning algorithms were created that could output a new portrait in the familiar Rembrandt style.

If you notice any typos (besides the known issues listed below) or have suggestions for exercises to add to the website, do not hesitate to contact any of the authors directly by e-mail: Ian ( lastname.firstname The book itself is now complete and we are not currently making revisions beyond correcting any minor errors that remain.

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.