If you have spent time either formally or informally learning machine learning, no doubt that you have been amazed at some point both at what machine learning is capable of, and your ability to learn it. At the same time, I was somewhat upset to learn that machine learning is, in fact, not magic, and actually based on sound principles. But that is beside the point. The student of machine learning can often also glean all sorts of tangential knowledge when studying and practicing, the "collateral knowledge," if you will. For your consideration, here are a few things I've picked up along the way, which I assuredly would not know if not for hours spent exploring and applying the craft of machine learning.

Machine Learning (ML) is one of the hot buzzwords these days, but even though EDA deals with big-data types of issues it has not made much progress incorporating ML techniques into EDA tools. Many EDA problems and solutions are statistical in nature, which would suggest a natural fit. So why is it so slow to adopt machine learning technology, while other technology areas such as vision recognition and search have embraced it so easily? "You can smell a machine learning problem," said Jeff Dyck, vice president of technical operation for by Solido Design Automation. "We have a ton of data, but which methods can we apply to solve the problems? That is the hard part. You cannot open a text book or take a course and apply those methods to solve all problems. Engineering problems require a different angle."

Faculty and staff from several schools at Carnegie Mellon University are joining forces in an effort to accelerate the science of Artificial Intelligence. University leaders said they hope that by pulling together more than 100 faculty through the creation of CMU AI, it will maintain the university's role as a leader in the field. CMU School of Computer Science dean Andrew Moore said the "confederation" of faculty and students from various disciplines, which will allow the school to offer what he calls "full stack" education and research. "That means [the students] need to be able to hang out and work on projects in labs not just with the technology experts on specific parts of AI, like machine learning or computer vision, but they have seen examples of putting everything together," Moore said. Moore said the university has been able to build great AI systems that combine technologies from several different disciplines.

For all the excitement around artificial intelligence and other automation technologies, we've only seen a fraction of the opportunities automation will create. With all the change still to come, it's worth considering now the role these technologies will play in your company's future. The practical benefits of automation are many: fewer data-entry errors, faster customer service response times, workload automation, better resource management, and the ability to turn legacy data into powerful insights. These functions enable your company to operate more efficiently. They also empower your employees to excel in new and exciting ways.

Underneath the hype, real breakthroughs in AI are transforming businesses. Intelligence engineers are creating software that doesn't just do what it's told, but also anticipates the needs of its users and customers through knowledge, pattern recognition, and reasoning. O'Reilly AI brings together leaders and innovators to explore the most promising applied deep learning and edge intelligence topics, including: If you're looking for groundbreaking research, compelling use cases, rock-solid technical skills--and maybe one or two crazy ideas that just might change the world--join us in New York June 26–29 for the O'Reilly AI Conference. Need help convincing your manager? Each training course takes place June 26–27 and is limited in size to maintain a high level of learning and instructor interaction.

Sometimes I write a blog just to formulate and organize a point of view, and I think it's time that I pull together the bounty of excellent information about Machine Learning. This is a topic with which business leaders must become comfortable, especially tomorrow's business leaders (tip for my next semester University of San Francisco business students!). Machine learning is a key capability that will help organizations drive optimization and monetization opportunities, and there have been some recent developments that will place basic machine learning capabilities into the hands of the lines of business. By the way, there is an absolute wealth of freely-available material on machine learning, so I've included a sources section at the end of this blog for folks who want more details on machine learning. Time to dive into the world of machine learning!

Editor's note: The TensorFlow API has undergone changes since this series was first published. However, the general ideas are the same, and an otherwise well-structured tutorial such as this provides a great jumping off point and opportunity to consult the API documentation to identify and implement said changes. In this tutorial I'll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first in a series of seven parts where various aspects and techniques of building Recurrent Neural Networks in TensorFlow are covered. A short introduction to TensorFlow is available here.

This online course is designed to teach you how to create deep learning Algorithms in Python by two expert Machine Learning & Data Science experts. This course is split into 32 sections which cover over 179 Artificial Neural Network topics using a video format – receive a certificate of completion at the end of the course. Online learning is very flexible (expiry dates may vary from course to course depending on the course provider). Artificial intelligence is growing exponentially. There is no doubt about that.

But new UC Berkeley research suggests that Siri and other digital helpers could someday learn the algorithms that humans have used for centuries to create and understand metaphorical language. Mapping 1,100 years of metaphoric English language, researchers at UC Berkeley and Lehigh University in Pennsylvania have detected patterns in how English speakers have added figurative word meanings to their vocabulary. The results, published in the journal Cognitive Psychology, demonstrate how throughout history humans have used language that originally described palpable experiences such as "grasping an object" to describe more intangible concepts such as "grasping an idea." "The use of concrete language to talk about abstract ideas may unlock mysteries about how we are able to communicate and conceptualize things we can never see or touch," said study senior author Mahesh Srinivasan, an assistant professor of psychology at UC Berkeley. "Our results may also pave the way for future advances in artificial intelligence."

If the casting director of the TV show "Silicon Valley" were asked to produce a canonical example of an applicant to Y Combinator's incubator program, she may well have come up with the guy strolling to the front of in a basement auditorium at Stanford on a mid-April day this year. He's a goateed bro in his mid-twenties, rocking a grey pullover hoodie, a brown stocking cap, and an eagerness to share his killer idea--Airbnb for parking! He's come to the Gates Computer Science Building to pitch it to the two interlocutors holding "Office Hours," where savvy veterans of the startup process dispense wisdom to aspiring Mark Zuckerbergs. The bro is clearly happy when both of them--Sam Altman, the head of Y Combinator, and Yuri Sagalov, a startup CEO who completed YC's startup boot camp in 2010 and now is a part-time partner there--express excitement at the concept. But Altman, a wiry 32-year-old who himself is wearing a zip-up hoodie, hits the brakes on the lovefest. "When is this going to launch?" he asks. The founder says the app is six months out. Steven Levy is Backchannel's Editor in Chief. Sign up to get Backchannel's weekly newsletter. "How about six days?" asks Altman.