Much of our curriculum is based on feedback from corporate and government partners about the technologies they are using and learning. In addition to their feedback we wanted to develop a data-driven approach for determining what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average.

Robots that double as security guards are growing as a presence in cities across the U.S., per McClatchy DC. The bots, equipped with laser scanning, 360-degree video, and thermal imaging are self-sufficient, cheaper than human beings, can work 24-hour days, and require no benefits. Yes, but: Letting the bots loose on the streets can be dangerous, and some manufacturers are still working out how to ensure the robots don't hurt pedestrians in their path. Meanwhile, no mechanism to hold the robots, or their human operators, accountable has been established yet. "If you have a robot with no distinguishing marks, who are you going to call?" A. Michael Froomkin, a University of Miami Law School professor, told McClatchy.

Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner where knowledge gained from previous tasks is retained and used for future learning. It is essential towards the development of intelligent machines that can adapt to their surroundings. In this work we focus on a lifelong learning approach to generative modeling where we continuously incorporate newly observed streaming distributions into our learnt model. We do so through a student-teacher architecture which allows us to learn and preserve all the distributions seen so far without the need to retain the past data nor the past models. Through the introduction of a novel cross-model regularizer, the student model leverages the information learnt by the teacher, which acts as a summary of everything seen till now. The regularizer has the additional benefit of reducing the effect of catastrophic interference that appears when we learn over streaming data. We demonstrate its efficacy on streaming distributions as well as its ability to learn a common latent representation across a complex transfer learning scenario.

Is your learning establishment preparing for – or even aware of – the next revolution in human development: robots educating our young? Artificial intelligence (AI), though rudimentary at the moment, is already having an impact on daily life. Your mobile phone is fast becoming a personal assistant, learning to anticipate your needs. Computer games contain virtual characters that learn to respond to their environment and smart cars are doing the same in the real world. And the UK government is keen for the country to exploit AI, publishing a report on the impact of AI on the economy, estimating that it could add another £630bn to the UK economy by 2035.

Artificial intelligence is rapidly making its way into industries from cybersecurity to manufacturing, bringing with it a growing need for data scientists and developers with a proficiency in deep learning. California-based AI chipmaker Nvidia, one of our 50 Smartest Companies of 2017, today announced an expansion of its Deep Learning Institute (DLI) aimed at curbing this issue. Founded last year, the DLI aims to address the AI skills gap internationally by training up students and today's workforce in the ways of AI--and specifically deep learning, the technique that powers today's powerful speech and image recognition algorithms, among others. Deep learning is complex, and working in the field has traditionally required great technical knowledge and expertise (see "10 Breakthrough Technologies 2013: Deep Learning"). By involving numerous AI experts, Nvidia is attempting to make the technology more accessible.

Machine learning and its younger sibling deep learning are continuing their acceleration in terms of increasing the value of enterprise data assets across a variety of problem domains. A recent talk by Dr. Amitai Armon, Chief Data-Scientist of Intel's Advanced Analytics department, at the O'reilly Artificial Intelligence conference, New-York, September 27 2016, focused on the usage of Intel's new server processors for various machine learning tasks as well as considerations in choosing and matching processors for specific machine learning tasks. Intel formed a machine learning task force with a mission to determine how the company can advance the machine learning domain. The vast majority of machine learning code today runs on Intel servers but the company wanted to do even better for the present and the future use cases. We need to understand the needs for these domains and prepare processors for those needs," said Dr. Amitai Armon. "This is not a simple challenge because in machine learning you have many algorithms, many data types and the field is constantly evolving.

Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now. The term "AI" is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services. But quite often, many of these people don't understand what AI is.

Schools may be critiqued as "factories," but robots aren't going to replace human teachers any time soon. Still, that doesn't mean that artificially intelligent systems won't transform education just as they are changing a variety of fields and practices, from the way oncologists diagnose cancer to how lawyers analyze cases. Intelligent-tutoring systems like ALEKS (for Assessment and LEarning in Knowledge Spaces), Cognitive Tutor, and a new program in development by IBM's Watson initiative are starting to expand in K-12 education, and experts argue that teachers need new training not only to use intelligent systems in the classroom but also to prepare students for careers in increasingly technology-integrated fields. "Any skill that a computer can teach is going to be done by a computer in the workplace, and that's something people don't think about enough," said Christopher Dede, an education and technology professor at the Harvard Graduate School of Education. For that reason, he said, teachers can use computer programs not simply to replace pieces of their instruction, but to model for students how to work with technology professionally.

Then we invite you to check out this very friendly introduction we made at Udacity! There are actually 19 videos included in this playlist, covering topics like Linear Regression, Neural Networks, Hierarchical Clustering, and more. Really got the Machine Learning fever? Then consider enrolling in our Machine Learning Nanodegree program. It's the best way to learn everything you need to know to become a successful Machine Learning Engineer!

Over the past year, I've spent most of my working time doing deep learning research and internships. And a lot of that year was making very big mistakes that helped me learn not just about ML, but about how to engineer these systems correctly and soundly. One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time. However, there doesn't seem to be a solid tutorial online on how to actually write unit tests for neural network code. Even places like OpenAI only found bugs by staring at every line of their code and try to think why it would cause a bug.