TutorSpace, as it has been named by multimedia analytics scientists at Xerox Research Centre India, is intended to turn instructional videos into "next-generation" textbooks. As Om Deshmuk, a Xerox senior research scientist in multimedia analytics, explained in a video of the project, right now, the amount of instructional content available in video form online can be overwhelming to students. Through machine learning TutorSpace also makes it possible to find content tailored to a student's learning patterns. Now Xerox has licensed TutorSpace to education technology company Impartus for use in its e-learning products.

"Artificial intelligence is the art and science of making machines intelligent," Corrado explained. According to Corrado, the brain's billions of neurons all make tiny decisions based on small amounts of information, but working together they can perform advanced thinking tasks. Moving back to the image recognition example, Corrado explained that these artificial neurons will individually scan tiny patches of pixels in an image and make some judgment about them. Asked how machine learning works for things like booking a movie ticket -- a task Google's AI-powered Google Assistant performed during CEO Sundar Pichai's keynote -- Corrado explained that parts of that task were not done by AI.

Moving to the right, credit card fraud detection and spam filtering have higher levels of predictability, but current-day systems still generate significant numbers of false positives and false negatives. Consider two of the relatively higher predictability problems mentioned earlier--spam filtering and driverless cars. In contrast, above the frontier, we find that even the best current diabetes prediction systems still generate too many false positives and negatives, each with a cost that is too high to justify purely automated use. On the other hand, the availability of genomic and other personal data could improve prediction accuracy dramatically (long orange horizontal arrow) and create trustworthy robotic healthcare professionals in the future.

Magenta will use TensorFlow, the machine-learning engine that Google built and opened up to the public at the end of 2015, to determine whether AI systems can be trained to create original pieces of music, art, or video. Much in the same way that Google opened up TensorFlow, Eck said Magenta will make available its tools to the public. Roberts also showed off a simple digital synthesizer program he'd been working on, where an AI could listen to notes that he played, and play back a more complete melody from those notes: The goal of the project, Eck suggested, could well be to create a system that could give a listener "musical chills" with entirely new pieces of music, on a regular basis, as they sit listening to computer-generated music from the comfort of their couch at home. Eck said the inspiration for Magenta had come from other Google Brain projects, like Google DeepDream, where AI systems were trained on image databases to "fill in the gaps" in pictures, trying to find structures in images that weren't necessarily present in the images themselves.

Magenta will use TensorFlow, the machine-learning engine that Google built and opened up to the public at the end of 2015, to determine whether AI systems can be trained to create original pieces of music, art, or video. Much in the same way that Google opened up TensorFlow, Eck said Magenta will make available its tools to the public. Roberts also showed off a simple digital synthesizer program he'd been working on, where an AI could listen to notes that he played, and play back a more complete melody from those notes: The goal of the project, Eck suggested, could well be to create a system that could give a listener "musical chills" with entirely new pieces of music, on a regular basis, as they sit listening to computer-generated music from the comfort of their couch at home. Eck said the inspiration for Magenta had come from other Google Brain projects, like Google DeepDream, where AI systems were trained on image databases to "fill in the gaps" in pictures, trying to find structures in images that weren't necessarily present in the images themselves.

Google executives talk about the company's future in artificial intelligence. During his keynote talk, Pichai also showed a video of several robot arms that a research group at Google taught to pick up objects. "It's also conflated with the fact that people look at things like robots learning to pick things up and that's somehow inherently scary to people," Giannandrea said. In April, Facebook unveiled a new Applied Machine Learning group.

These tools allow machines to read and understand English text (such as text you type into a browser to do a Google search). And the Parsey McParseface program implements SyntaxNet in English (it learned from an annotated collection of old newswire stories called The Penn Treebank Project). Here's an example of how it parses and analyzes an English sentence:Using deep neural networks, SyntaxNet is implemented in Google's TensorFlow (see Google open-sources its TensorFlow machine learning system). On a standard benchmark consisting of randomly drawn English newswire sentences ("Penn Treebank"), Parsey McParseface recovers individual dependencies between words with over 94% accuracy, Google says.

In order to outperform more traditional machine learning methods, deep neural nets require large amounts of data and expertise with highly-parallel and high-performance graphics processing unit (GPU) computing. Insilico Medicine is working on over a dozen different applications of deep learning methods to regenerative medicine, embryonic development, cross-species comparison and drug discovery and repurposing providing contract research services and developing a range of molecules for cancer, metabolic and CNS pathologies. We want to minimize animal testing and simulate many biological processes in silico", said Putin, deep learning lead at Insilico Medicine, Inc. To develop a data set of blood biochemistry and cell count samples Insilico Medicine collaborated with the largest independent laboratory test service provider in Eastern Europe, Invitro Laboratories. Using this data set Insilco Medicine scientists then trained 40 different deep neural networks (DNNs) of different depth with a single neuron output predicting chronological age and optimized using different optimizers and started organizing these DNNs into an ensemble.

RED SAP Solutions has announced that it is working with SAP on a project that will apply Machine Learning to the recruitment process. According to RED, it will use the Machine Learning based solution to improve the service it offers to its clients, enabling them to cut time to hire and recruit the best people more quickly, and therefore reduce cost to hire. The project is already tapping into 15 years' information, and every time RED adds new requirements, processes potential matches, sends client details to candidates and make placements, it says it will continue to improve its predictions. This will improve productivity for RED, and enable the company to improve its customer service and widen its portfolio of client-centric services.

RED SAP Solutions has announced that it is working with SAP on a project that will apply Machine Learning to the recruitment process. According to RED, it will use the Machine Learning based solution to improve the service it offers to its clients, enabling them to cut time to hire and recruit the best people more quickly, and therefore reduce cost to hire. The project is already tapping into 15 years' information, and every time RED adds new requirements, processes potential matches, sends client details to candidates and make placements, it says it will continue to improve its predictions. This will improve productivity for RED, and enable the company to improve its customer service and widen its portfolio of client-centric services.