In late post-revolutionary France one man was tasked to map out the country. Gaspard de Prony, a mathematician and engineer, decided to approach the task by creating logarithmic and trigonometric tables. These tables, which would come to be known as Tables of de Prony, were destined to speed up the trigonometric calculations needed to complete these cartographic task. In handling the vast amounts of data, de Prony asked for help. His team was divided in three levels of hierarchy: besides a couple of highly skilled mathematicians, several mathematicians with less sophisticated skills, he also hired sixty to eighty hairdressers.

In 2013 I had a long interview with Peter Lee, corporate vice president of Microsoft Research, about advances in machine learning and neural networks and how language would be the focal point of artificial intelligence in the coming years. At the time the notion of artificial intelligence and machine learning seemed like a "blue sky" researcher's fantasy. Artificial intelligence was something coming down the road … but not soon. I wish I had taken the talk more seriously. Language is, and will continue to be, the most important tool for the advancement of artificial intelligence.

Source: IDC Global Digital Data (in Exabyte) Enabling forces behind Artificial Applications 5. Artificial Intelligence, May 2016 5 Scope of report This report covers companies that provide the infrastructure for creating Artificial Intelligence. These Infrastructure companies include those working on Machine Learning, Deep Learning based platforms, libraries. Some of theses companies also provide platforms for Natural Language Processing and Visual Recognition. In the Applications section, the report covers companies leveraging AI techniques to build applications tailored for end use in Enterprise, Industry & Consumer sectors. Over $1B has been invested in AI-Infrastructure startups since 2010 with $340M being invested in 2015.

Neural turing machines and GANs often don't train well, with heavy dependence on rand seed. Unlike robust random forests, require heavy feature tuning. All of these are problems worth thinking about (and researching more). Deep learning practitioners are extraordinarily talented and imaginative.

Understanding deep learning requires re-thinking generalization Zhang et al., ICLR'17 This paper has a wonderful combination of properties: the results are easy to understand, somewhat surprising, and then leave you pondering over what it all might mean for a long while afterwards! What is it that distinguishes neural networks that generalize well from those that don't? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design. By "generalize well," the authors simply mean "what causes a network that performs well on training data to also perform well on the (held out) test data?" If you think about that for a moment, the question pretty much boils down to "why do neural networks work as well as they do?"

Deep learning is being applied extensively within all the tools that we use in everyday life, mobile phones, computers and even coffee machines. Therefore it is important to understand how the technology works. Over the last few weeks, we have been introducing the A-Z Glossary Series for Deep Learning, which will include explanations for key terms to help build a basic understanding in deep learning. Here is a summary and we begin with A to M with some of the most important terms in deep learning. Stay tuned as we will be sharing N-Z next week!

These days, you hear a lot about machine learning (or ML) and artificial intelligence (or AI) – both good or bad depending on your source. Many of us immediately conjure up images of HAL from 2001: A Space Odyssey, the Terminator cyborgs, C-3PO, or Samantha from Her when the subject turns to AI. And many may not even be familiar with machine learning as a separate subject. The phrases are often tossed around interchangeably, but they're not exactly the same thing. In the most general sense, machine learning has evolved from AI. In the Google Trends graph above, you can see that AI was the more popular search term until machine learning passed it for good around September 2015.

You are free to share this article under the Attribution 4.0 International license. A four-armed, marimba-playing robot can now write and play its own compositions with aid from artificial intelligence and deep learning. Researchers fed the robot nearly 5,000 complete songs--from Beethoven to the Beatles to Lady Gaga to Miles Davis--and more than 2 million motifs, riffs, and licks of music. Aside from giving the machine a seed, or the first four measures to use as a starting point, no humans are involved in either the composition or the performance of the music. The first two compositions are roughly 30 seconds in length.

Bayesian probabilistic models provide a nimble and expressive framework for modeling "small-world" data. In contrast, deep learning offers a more rigid yet much more powerful framework for modeling data of massive size. Edward is a probabilistic programming library that bridges this gap: "black-box" variational inference enables us to fit extremely flexible Bayesian models to large-scale data. Furthermore, these models themselves may take advantage of classic deep-learning architectures of arbitrary complexity. Edward uses TensorFlow for symbolic gradients and data flow graphs.

After discovering the amazing power of convolutional neural networks for image recognition in part five of this series, I decided to dive head first into Natural language Processing or NLP. This hotbed of machine learning research teaches computers to understand how people talk. When you ask Siri or the Google Assistant a question, it's NLP that drives the conversation. Of course, as an author of novels and articles, working with language seemed like the obvious next step for me. I may suck at math but words are my domain! So I set out to uncover what insights NLP could give me about my own area of mastery. I had so many questions. Had NLP uncovered the hidden keys to writing heart-wrenching poems? Could AIs turn phrases better than the Bard? Luckily, I had just the right project in mind to test the limits of NLP. I was in the midst of naming the second book in my epic sci-fi saga The Jasmine Wars but I'd struggled to find the perfect title. What if I could feed a neural net with the greatest titles of all time and have it deliver a title for the ages? This isn't my first foray into computer assisted title generation. There are a number of random title generators out on the interwebs that I've tried from time to time. They're the type of toy you play with for a few minutes and then move on.