The machine learning solution from Google offers learning services with pre-trained models as well as the option of generating your own tailor-made models. A neural net-based platform, it performs better and more accurately than other learning systems on the market. The technology is currently available for developers in limited preview phase. The cloud machine learning tool can be used with some of the technologies that Google employs in its services, such as voice searches and translations in Gmail, therefore speeding up the development process. Its main advantages include greater speed, scalability and usability for all the applications featured in these services.

Recurrent Neural Networks (RNNs), and specifically a variant with Long Short-Term Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data. I will summarize my own experience with training these models for automated image captioning and for generating text character by character, with a particular focus on understanding the source of their impressive performance and their limitations.

We caught up with Matt Taylor from Numenta -- an organization whose mission is to lead a new era of machine intelligence and build computer systems around the principles of the brain. Matt shared his thoughts and insights on the open source community around their exciting projects. Find out what he says, and check out the Numenta community channel on Gitter. Tell us about a little bit about yourself and the Numenta community. How did it all begin?

H2O is feature-rich open source machine learning platform known for its R and Spark integration and it's ease of use. This is an overview of using H2O deep learning for data science with the Internet of Things. H2O is an Open Source machine learning platform for smarter applications. At the Data Science for IoT course, we have been following H2O for features such as Open Source, R integration, Spark integration, Deep Learning and it's ease of use. This blog is authored by Sibanjan Das and Ajit Jaokar as part of our work at the Data Science for IoT course exploring H2O Deep Learning for Internet of Things.

From undergrads in music and social work, to PhDs in philosophy, to those who never graduated high school -- I've had quite a variety of co-workers during the ten years I've been in tech. Of course, in every software company you'll find your traditional computer science and engineering graduates as well. However, there's a significant and growing population of developers who took a different path to learn to code and are building a profession out of it. I hold a degree in linguistics, which in most universities is not a computational program, but an anthropological one. Required coursework includes topics such as historical and cultural language studies.

MENLO PARK (Web Desk) – Facebook has begun using artificial intelligence to help people with visual impairments to recognize objects in pictures and then describe photos aloud. Blind Facebook users scrolling through their feed have known for a while exactly what they were missing. Text-to-speech dictation software that describes the back-and-forth comments and recites friends' status updates would offer little when users came across an image: "Photo," the machine would say. Maybe a name, if the photo was tagged with a person. The feature was being tested on mobile devices powered by Apple iOS software and which have screen readers set to English.

It's a cool opener on the concepts, but leaves the actual implementations very hazy. For instance, I would love to understand how to create my own environment (or task, for that matter). Instead, this tutorial just throws ready-made stuff at you which, I reckon, isn't very helpful in actually understanding. If there exists good explanations involving programming I would be very keen in looking into them.

IT HAS wormed its way into almost every sphere of life, and the law is no exception. Artificial intelligence can now handle a lot of the drudgery of legal work: sifting mountains of documents for relevant titbits, for example, or automatically drafting and checking boilerplate contracts. There's even a "superintelligent attorney" app, ROSS, powered by IBM's Watson supercomputer, that fields legal queries by speed-reading legislation and other resources. But what does it mean for the law when an algorithm, rather than a person, calls the shots? Frank Levy at the Massachusetts Institute of Technology and Dana Remus at the University of North Carolina School of Law have been on the case, exploring the potential ramifications of robotic legal assistants.

MORRISTOWN --On April 1 The Peck School honored third-graders Sophie Cheng and Scarlette Liftin for being the regional winners of the 24th annual Toshiba and National Science Teachers Association ExploraVision program, a science competition for students in grades K to 12. Students compete in six regions broken down into four age groups. Sophie and Scarlette were the Region 2 winners in the grades K to 3 age group. To participate in the contest, students work in teams of 2 to 4 under the guidance of a teacher to research a current technology and try to imagine how it might be used in the future. Sophie and Scarlette worked with teacher Jennifer Garvey on their project, the personal Interactive Picture Frame. The project, which took its inspiration from the Harry Potter series, delves into the possibility of using artificial intelligence technology to make photographs come to life by enabling those pictured to speak and move.

Google is getting ready for deep learning and it wants you to be ready as well, which is why the tech giant has launched a three month course in order to help you learn its next level machine language. Deep learning is a machine learning technique that has become the foundation of the several services that Google already provides (this would include everything from speech recognition to automatically sorting your photo collection). The course is available to see on educational site Udacity, and could actually take longer than three months, depending on how quick you are to learn it. The course details state that if a student or any interested other person is able to invest 6 hours a week into the course, then they will be able to complete it in a period of months. This also means that if you spend more time on it, you will be able to complete the course in a faster period of time, which makes it really flexible for several students who are engineers, who do not have a lot of time on their hands (the scenario is also vice versa).