An unlikely partnership between two tech heavyweights symbolizes how cloud vendors prioritize machine learning and deep learning for the future of their platforms. Amazon Web Services (AWS) and Microsoft Azure are the two most popular public cloud providers, with the latter trying to encroach on the former's sizable market lead. But in a surprise move, the pair has put aside their rivalry to create Gluon, an open source deep learning library intended to automate certain processes and make machine learning more approachable to developers. Both of these companies, as well Google, IBM and others, see huge potential for machine learning in the cloud and deep learning applications built on their respective platforms. But these techniques are predominantly confined to the likes of data scientists, because typical developers lack the skills to build and train the models that underlie these applications.

By its own reckoning, Bitmain built 70 percent of all the computers on the Bitcoin network. It makes specialized chips to perform the critical hash functions involved in mining and trading bitcoins, and packages those chips into the top mining rig--the Antminer S9. As he told IEEE Spectrum contributing editor Morgen E. Peck in July: "It's quite personal that I wanted Bitcoin to be successful. But as a company we are not allowed to solely rely on the success of Bitcoin. That's a thing we cannot afford."

Machine learning, and especially deep learning, have turned out to be incredibly useful in the right hands, as well as incredibly demanding of computer hardware. The boom in availability of high-end GPGPUs (general purpose graphics processing units), FPGAs (field-programmable gate arrays), and custom chips such as Google's Tensor Processing Unit (TPU) isn't an accident, nor is their appearance on cloud services. There's the rub--or is it? There is certainly a perceived dearth of qualified data scientists and machine learning programmers. Whether there's a real lack or not depends on whether the typical corporate hiring process for data scientists and developers makes sense.

This morning at the WSJ's D.Live event, Intel formally unveiled its Nervana Neural Network Processor (NNP) family of chips designed for machine learning use cases. Intel has previously alluded to these chips using the pre-launch name Lake Crest. The technology underlying the chips is heavily tied to Nervana Systems, a deep learning hardware startup Intel purchased last August for $350 million. Intel's NNP chips nix standard cache hierarchy and use software to manage on-chip memory to achieve faster training times for deep learning models. Intel has been scrambling in recent months to avoid being completely leveled by Nvidia.

Today's paper is by Gao Huang, Yu Sun, et al. It introduces a new way to perturb networks during training in order to improve their performance. Before I continue, let me first state that this paper is a real pleasure to read; it is concise and extremely well written. It gives an excellent overview of the motivating problems, previous solutions, and Huang and Sun's new approach. I highly recommended giving it a read!

There are too many myths circling around artificial intelligence (AI) and its role in marketing. It's slanderous (to the technology industry) to denounce AI as only hype, and incorrect to assume AI is a fabrication coined by companies aiming to drum up publicity for what's merely a machine learning, deep learning, or automation tool. Most tech experts agree that, by 2050, AI will almost certainly handle the vast majority of routine marketing execution. And while AI won't necessarily take your job, it already is and will continue to augment the level of intelligence marketers operate with, the insights they can attain, and the kind of incredibly individualized communication they're are able to deliver to customers. What's clear is that artificial intelligence – as a marketing tool – is already being used by e-commerce companies to gain a competitive advantage.

Yoshua Bengio is one of the foremost thinkers in a field within artificial intelligence known as artifical neural networks and deep learning. Although significant progress has been made in recent years due to (among other factors) the combination of the proliferation of data, the decreasing cost of compute, and the tremendous amount of money and talent now devoted to artificial intelligence, Bengio chose this as a field of study during the 1980s, in the throes of what some referred to as the AI winter, seeing through a period when money and enthusiasm for artificial intelligence had dried up. Bengio is the co-author (with Ian Goodfellow and Aaron Courville) of Deep Learning, a book that Elon Musk referred to as "the definitive textbook on deep learning." On top of his growing influence in this field, he has also been enormously influential in shaping Montreal specifically and Canada more generally has become a hotbed for artificial intelligence. Bengio co-founded Element AI in 2016, which has a stated mission to "turn the world's leading AI research into transformative business applications."

Isaac Asimov's famous Three Laws of Robotics--constraints on the behavior of androids and automatons meant to ensure the safety of humans--were also famously incomplete. The laws, which first appeared in his 1942 short story "Runaround" and again in classic works like I, Robot, sound airtight at first: Of course, hidden conflicts and loopholes abound (which was Asimov's point). In our current age of advanced machine-learning software and autonomous robotics, defining and implementing an airtight set of ethics for artificial intelligence has become a pressing concern for organizations like the Machine Intelligence Research Institute and OpenAI. Christoph Salge, a computer scientist currently at New York University, is taking a different approach. Instead of pursuing top-down philosophical definitions of how artificial agents should or shouldn't behave, Salge and his colleague Daniel Polani are investigating a bottom-up path, or "what a robot should do in the first place," as they write in their recent paper, "Empowerment as Replacement for the Three Laws of Robotics."

Cutting Edge Deep Learning For Coders, Part 2 from fast.ai

On this article, I make clear the assorted roles of the information scientist, and the way knowledge science compares and overlaps with associated fields reminiscent of machine studying, deep studying, AI, statistics, IoT, operations analysis, and utilized arithmetic. As knowledge science is a broad self-discipline, I begin by describing the various kinds of knowledge scientists that one could encounter in any enterprise setting: you may even uncover that you're a knowledge scientist your self, with out realizing it. As in any scientific self-discipline, knowledge scientists could borrow strategies from associated disciplines, although we now have developed our personal arsenal, particularly strategies and algorithms to deal with very giant unstructured knowledge units in automated methods, even with out human interactions, to carry out transactions in real-time or to make predictions. To get began and acquire some historic perspective, you may learn my article about 9 types of data scientists, printed in 2014, or my article the place I evaluate knowledge science with 16 analytic disciplines, additionally printed in 2014. I additionally wrote in regards to the ABCD's of business processes optimization the place D stands for knowledge science, C for laptop science, B for enterprise science, and A for analytics science.