At a recent dinner, I was asked, "how could a marketer get started with machine learning and AI?". Plenty of vendors offer specific solutions, but what if we just wanted to dip our toes in the water? What if we even just wanted to know where the water is? Let's look at how to plan and build our first machine learning/AI project with the AI/Machine Learning Lifecycle: Before we choose a technology or platform, choose a marketing problem of scale that we want to solve. What marketing challenge do we face that artificial intelligence is well-suited to solve?

This paper studies the cooperative training of two probabilistic models of signals such as images. Both models are parametrized by convolutional neural networks (ConvNets). The first network is a descriptor network, which is an exponential family model or an energy-based model, whose feature statistics or energy function are defined by a bottom-up ConvNet, which maps the observed signal to the feature statistics. The second network is a generator network, which is a non-linear version of factor analysis. It is defined by a top-down ConvNet, which maps the latent factors to the observed signal. The maximum likelihood training algorithms of both the descriptor net and the generator net are in the form of alternating back-propagation, and both algorithms involve Langevin sampling. We observe that the two training algorithms can cooperate with each other by jumpstarting each other's Langevin sampling, and they can be naturally and seamlessly interwoven into a CoopNets algorithm that can train both nets simultaneously.

Much of our curriculum is based on feedback from corporate and government partners about the technologies they are looking to learn. But we wanted to develop a more data-driven approach to 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. What are the most popular ML packages? Let's look at a ranking based on package downloads and social website activity. The ranking is based on average rank of CRAN (The Comprehensive R Archive Network) downloads and Stack Overflow activity (full ranking here [CSV]).

Summary: For those of you traditional data scientist who are interested in AI but still haven't given it a deep dive, here's a high level overview of the data science technologies that combine into what the popular press calls artificial intelligence (AI). We and others have written quite a bit about the various types of data science that make up AI. Still I hear many folks asking about AI as if it were a single entity. AI is a collection of data science technologies that at this point in development are not even particularly well integrated or even easy to use. In each of these areas however, we've made a lot of progress and that's caught the attention of the popular press.

Scality CEO Jérôme Lecat takes a look at how deep learning can transform businesses. If you work in information technology the chances are you have noticed regular articles in the media about artificial intelligence (AI), machine learning and deep learning. Some commentators make no distinction between these terms and they often use them interchangeably. But to attribute the same meaning to these names is an oversimplification which is unhelpful to those looking for new ways to add value to their businesses. While AI, machine learning and deep learning are often intertwined, they hinge upon different technologies and have their own unique attributes.

On Wednesday, January 25, 2017, Baidu chief scientist, Coursera co-founder, and Stanford adjunct professor Andrew Ng spoke at the Stanford MSx Future Forum. The Future Forum is a discussion series that explores the trends that are changing the future. During his talk, Professor Ng discussed how artificial intelligence (AI) is transforming industry after industry.

This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence. The ambiguities and noise inherent in human communication render traditional symbolic AI techniques ineffective for representing and analysing language data. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks.

I will dispense with any amenities and cut right to the chase. When it comes to corporate learning and training the numbers are truly staggering. Trust me, there's a lot more where this came from meaning there is no shortage of stats and research that speak to the benefits of e-Learning. In a piece last year for PC Magazine, Rob Marvin wrote something that of course struck a chord with me. I say of course because of me being the pop culture savant that I am.

The patter of automated machinery fills the air inside wire-basket manufacturer Marlin Steel's bustling factory in a rugged industrial section of this city. Maxi Cifarelli, 25, of Baltimore, peers through safety goggles at a flat screen, her left knee bent and heel resting on her chair. Two years after earning a fine arts degree from Towson University with a specialty in interdisciplinary object design, she now spends her work days working with a personality-free machine with a name to match: a computer numerical control, or CNC, router. With automation poised to sweep through the economy, some fear that it will kill more jobs than it creates. But Cifarelli's experience is the opposite. She befriended automation, instead of fighting it, and she has a job because of it.

In a fourth-floor Tufts lab, a computer program was in the process of convincing a student that she was actually interacting with a human. It was spring 2015, and the student had come to the lab for a study involving a new way of teaching people to play the piano. The beginning of the session had been fairly unremarkable. The researchers--Beste Yuksel, E16, then a Ph.D. candidate in computer science, and Kurt Oleson, A15, a brain science major with a minor in music engineering--put a headband-like contraption on the student's head and sat her down at a piano keyboard. In front of the keyboard was a computer screen that displayed the soprano line of a Bach piano chorale that she was supposed to play.