Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. Specialists describe deep learning as akin to a rocketship that needs a really big engine (a model) and a lot of fuel (the data) in order to go anywhere interesting. To get a better understanding of the issues involved in building compute systems for deep learning, I spoke with one of the foremost experts on this subject: Greg Diamos, senior researcher at Baidu. Diamos has long worked to combine advances in software and hardware to make computers run faster.

It's no secret that marketers and search optimization pros can be a bit obsessed with Google's zoo of algorithm updates. We've weathered the Penguins and Pandas that thankfully stopped old hacks from working these past couple of years; but what about what the next algo Zebra or Walrus, or maybe that bigger Elephant in the room affecting search traffic – personalization? This one's even bigger and less cuddly than the two that came before. The thing we should really be aware of, however, is the fact that Google's latest animal doesn't have a name because personalization is invisible. Still, I'm going to call it Elephant because that just feels right to me.

What makes a cartoon caption funny? As one algorithm found: a simple readable sentence, a negation, and a pronoun--but not "he" or "she." The algorithm went on to pick the funniest captions for thousands of the New Yorker's cartoons, and in most cases, it matched the intuition of its editors. Algorithms are getting much better at understanding language, and we are becoming more aware of this through stories like that of IBM Watson winning the Jeopardy quiz. Google released the word2vec tool, and Facebook followed by publishing their speed optimized deep learning modules.

The best view is always from the top. This holds true for the constellation of satellites hurling millions of pixels in images down to earth every day. In this age of breathtaking photography advancements, from drones and action cameras to submersible mobile phones, what has changed for an almost 60-year-old technology? It turns out that deep learning, i.e. an advanced machine learning technique that develops multilayered mathematical structures to classify images, is changing the way computers interpret large sets of satellite data. DigitalGlobe, a Colorado-based satellite imagery provider that supplies to such clients as Google Maps, is using a cloud-based, geospatial big data (GBD) platform to intelligently extract relevant images and points of interest from a vast compendium of above-earth photos.

LONDON -- The government is putting cutting edge technologies such as artificial intelligence (AI), 5G wireless internet, "smart" energy technology, and robotics at the heart of its new post-Brexit industrial strategy. Theresa May is set to launch the government's "Modern Industrial Strategy" on Monday at a regional meeting of the cabinet in the North West. The Prime Minister announced in a release on Sunday evening that the strategy would be focused around ten key strategic pillars, the first of which is: "Investing in science, research, and innovation." The release says that the £4.7 billion increase in research & development (R&D) funding announced in last year's Autumn Statement is central to the new industrial strategy. This investment will go to areas such as AI and robotics, Number 10 says.

Machine learning has been a constant on tech trend lists for years. This year, it's time to embrace what humans can learn by interacting with machine learning. As Google's head of Machine Intelligence, Blaise Aguera y Arcas noted in a recent Medium article: "Machine intelligence will expand our understanding of both external reality and our perceptual and cognitive processes." In the spring of 2016, Google's AlphaGo software, fueled by machine learning, beat the world's greatest human Go player, Lee Sedol. The victory was a major milestone for a specific type of AI, called deep neural networks, that is more closely modeled on the way humans think.

You do not have to model your time series forecast problem as-is. There are many ways to reframe your forecast problem that can both simplify the prediction problem and potentially expose more or different information to be modeled. A reframing can ultimately result in better and/or more robust forecasts. In this tutorial, you will discover how to reframe your time series forecast problem with Python. How to Reframe Your Time Series Forecasting Problem Photo by Sean MacEntee, some rights reserved.

From location beacons to artificial intelligence here is a comprehensive guide to what I predict will be the biggest marketing trends for the New Year. Pokémon Go brought augmented reality to the forefront for many marketers and we expect advertisers outside of the gaming realm to dive deep into this space in 2017. AR and VR technologies remain largely uncharted territory with literally endless creative possibilities. With this technology, the viewer becomes an integral part of the brand experience as opposed to a mere observer. Instead of a call to action, they are the action.

Wondering when your order from GameStop will be delivered? Simply open Facebook Messenger and ask. Looking for tips on how to pair that new jacket you're eagerly awaiting with jewellery? This is the new retail reality thanks to the emergence of chatbots. Consumers appreciate the level of personalization and convenience that chatbots deliver effortlessly -- and through the channels where they already spend time.

This is a follow up to the Introduction to Machine Learning video.