Deep learning systems continue to gain widespread adoption in the enterprise, tackling photo and voice recognition, customer service interactions, and even spotting abnormalities in medical records. But while the artificial intelligence (AI) models, which rely on massive data sets to "train" themselves on recognizing patterns and making predictions--throughout multiple iterations--timing is still an obstacle. Developing an accurate deep learning model can take up to days, or even weeks. On Tuesday, a new deep learning model developed by IBM Research--Distributed Deep Learning--made big strides in the field: It achieved a record for image recognition accuracy of 33.8%. The model, which used a massive data set of 7.5 million images, achieved "record communication overhead and 95% scaling efficiency on the Caffe deep learning framework over 256 GPUs in 64 IBM Power systems," according to IBM--all in just seven hours.

The folks at Sony Education are worried that some schlubby kid that's gonna fail gym could hack their school and change their grade to a pass. It's why the company is teaming up with IBM to use blockchain to create a secure academic platform for storing records. The idea is that every scrap of data about your kids' schooling goes into a record that can then be stored securely. No kid, you gotta learn to climb that rope or else you can kiss that scholarship to Harvard goodbye. Sony wants to use its platform as a way for schools to create a huge central database of pupils in a given region.

You've probably encountered the term "machine learning" more than a few times lately. Often used interchangeably with artificial intelligence, machine learning is in fact a subset of AI, both of which can trace their roots to MIT in the late 1950s. Machine learning is something you probably encounter every day, whether you know it or not. The Siri and Alexa voice assistants, Facebook's and Microsoft's facial recognition, Amazon and Netflix recommendations, the technology that keeps self-driving cars from crashing into things – all are a result of advances in machine learning. While still nowhere near as complex as a human brain, systems based on machine learning have achieved some impressive feats, like defeating human challengers at chess, Jeopardy, Go, and Texas Hold'em.

I've been following IBM's AI efforts with interest for a quite a while now. In my opinion, the company jump-started the current cycle of AI with the introduction of Watson back in the 2000s and has steadily been ramping up its efforts since then. Most recently, I wrote about the launch of PowerAI, IBM's software toolkit solution to use with OpenPOWER systems for enterprises who don't want to develop their AI solutions entirely from scratch but still want to be able to customize to fit their specific deep learning needs. Today, IBM Research announced a new breakthrough that will only serve to further enhance PowerAI and its other AI offerings--a groundbreaking Distributed Deep Learning (DDL) software, which is one of the biggest announcements I've tracked in this space for the past six months. Anyone who has been paying attention knows that deep learning has really taken off in the last several years.

Andrew Ng, one of the world's best-known artificial-intelligence experts, is launching an online effort to create millions more AI experts across a range of industries. Ng, an early pioneer in online learning, hopes his new deep-learning course on Coursera will train people to use the most powerful idea to have emerged in AI in recent years. AI experts have become some of the most sought-after and well-paid employees in today's tech economy. Deep learning involves teaching a machine to perform a complex task using large amounts of data along with a large simulated neural network. The technique has typically required deep technical knowledge and expertise to master (see "10 Breakthrough Technologies 2013: Deep Learning").

Imagine a scenario where every employee in the world has access to personalized learning and that learning helps them build knowledge and skills they need throughout their careers. The idea that everyone could have access to exactly the learning they need when they need it seemed like fantasy just a few short years ago. But now with machine learning a personalized learning experience is possible. Think about what this means. Because learning content is now available to everyone like a consumer product, there's no shortage of learning to be had.

Say Machine Learning to someone, and if they recognize the term, they'll probably think, "tech company." But while the origin stories of transformative technologies like machine learning, deep learning, and artificial intelligence often seem to take root in Silicon Valley, the truth is these are industry-agnostic innovations. Their impact is being felt across countless fields you might never have thought of as being ripe for technological advancement. Think about it like this: If you were a farmer, and someone came to you and said, there's a technology out there that can accurately predict your crop yields, would you be interested? Well, this is exactly what Descartes Labs does.

Statistical analysis (SA) is a complex process to deduce population properties from analysis of data. It usually takes a well-trained analyst to successfully perform SA, and it becomes extremely challenging to apply SA to big data applications. We propose to use deep neural networks to automate the SA process. In particular, we propose to construct convolutional neural networks (CNNs) to perform automatic model selection and parameter estimation, two most important SA tasks. We refer to the resulting CNNs as the neural model selector and the neural model estimator, respectively, which can be properly trained using labeled data systematically generated from candidate models. Simulation study shows that both the selector and estimator demonstrate excellent performances. The idea and proposed framework can be further extended to automate the entire SA process and have the potential to revolutionize how SA is performed in big data analytics.

I have a confession to make. I feel like a fraud. Every few days, I receive an email from either a friend, a friend of a friend, or a random company that asks me for my insights in Artificial Intelligence. These include entrepreneurs who have just sold their startups, Stanford MBA graduates who reject half a million dollar offers, venture capitalists, even major bank executives. A couple of years earlier, I wouldn't even have the courage to approach those people, let alone dreaming about them wanting to talk to me.

I have been working on three new AI projects, and am thrilled to announce the first one: deeplearning.ai, These courses will help you master Deep Learning, apply it effectively, and build a career in AI. Just as electricity transformed every major industry starting about 100 years ago, AI is now poised to do the same. Several large tech companies have built AI divisions, and started transforming themselves with AI. But in the next few years, companies of all sizes and across all industries will realize that they too must be part of this AI-powered future.