Nvidia had a huge 2016 with one of best performing stocks of the year. In the past 12 months, the graphics processing chipmaker's stock value has boomed 230%. This is mostly due to its impressive growth in artificial intelligence applications using its graphics processors in data centers and cars. Meanwhile, Nvidia maintains a fast-growing business in its core gaming market. Partially as a reflection of the growing importance of AI in the tech industry, Nvidia stole the opening Consumer Electronics Show keynote this year from Intel.

Deep learning is unlocking tremendous economic value across various market sectors. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. Intel Nervana has built a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. Nervana's platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.

The Internet of Things (IoT) is touching every technology sector around the world, and it's having a significant impact on how enterprises and consumers interact with machines and devices. TechRepublic talked to IoT experts in a range of disciplines to find out what they think the biggest trends will be in 2017. Participants were Kevin Curran, IEEE senior member and senior lecturer in computer science at Ulster University; Francesco Cetraro, head of registrations, .cloud; Artificial intelligence, augmented reality, virtual reality, healthcare IoT, industrial IoT, and wearables are some of the topics of conversation about where the Internet of Things is headed in 2017. Diabetics have been waiting for years for better technology to manage their condition. Some got tired of waiting and hacked together an open source hardware and software solution.

This is the second installment in a three-part review of 2016 in machine learning and deep learning. Part One, here, covered general trends. In Part Two, we review the year in open source machine learning and deep learning projects. Part Three will cover commercial machine learning and deep learning software and services. There are thousands of open source projects on the market today, and we cannot cover them all. We've selected the most relevant projects based on usage reported in surveys of data scientists, as well as development activity recorded in OpenHub. In this post, we limit the scope to projects with a non-profit governance structure, and those offered by commercial ventures that do not also provide licensed software. Part Three will include software vendors who offer open source "community" editions together with commercially licensed software.

From Amazon and Facebook to Google and Microsoft, leaders of the world's most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). While there is growing interest in AI, the field is understood mainly by specialists. Our goal for this primer is to make this important field accessible to a broader audience. We'll begin by explaining the meaning of'AI' and key terms including'machine learning'. We'll illustrate how one of the most productive areas of AI, called'deep learning', works.

We can all agree that being with a Google that long and contributing so much to search is a remarkable accomplishment and congratulate Singhal as he steps into a new time in life, focusing on philanthropy. As new leadership often means momentous refocusing, SEO professionals wonder how earned search may change as Giannandreas assumes this position, and if the change will generate ripples across the tech world as a whole. The future of how GoogleBot crawls and interprets web content looks promising under his leadership, as we observe how he impacts machine learning's future and how the Metaweb is woven. Amit went on to say that "search is stronger than ever, and will only get better in the hands of an outstanding set of senior leaders who are already running the show day-to-day. Our mission of empowering people with information and the impact it has had on this world cannot be overstated." John Giannandrea, who has been the forerunner overseeing artificial intelligence, such as in Google Algorithm RankBrain, has been employed at Google for six years and is currently the VP of engineering. As explained by Forbes in November, 2015 RankBrain's role took "a very large fraction" of the millions of queries that went through the search engine.

This post is the first in a series I'll be writing for Parallel Forall that aims to provide an intuitive and gentle introduction to deep learning. It covers the most important deep learning concepts and aims to provide an understanding of each concept rather than its mathematical and theoretical details. While the mathematical terminology is sometimes necessary and can further understanding, these posts use analogies and images whenever possible to provide easily digestible bits comprising an intuitive overview of the field of deep learning. I wrote this series in a glossary style so it can also be used as a reference for deep learning concepts. Part 1 focuses on introducing the main concepts of deep learning. Part 2 provides historical background and delves into the training procedures, algorithms and practical tricks that are used in training for deep learning. Part 3 covers sequence learning, including recurrent neural networks, LSTMs, and encoder-decoder systems for neural machine translation.

We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics. Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking. The robot gathered over 400 hours of experience by executing more than 50K pokes on different objects. We propose a novel approach based on deep neural networks for modeling the dynamics of robot's interactions directly from images, by jointly estimating forward and inverse models of dynamics. The inverse model objective provides supervision to construct informative visual features, which the forward model can then predict and in turn regularize the feature space for the inverse model. The interplay between these two objectives creates useful, accurate models that can then be used for multi-step decision making. This formulation has the additional benefit that it is possible to learn forward models in an abstract feature space and thus alleviate the need of predicting pixels. Our experiments show that this joint modeling approach outperforms alternative methods. We also demonstrate that active data collection using the learned model further improves performance.

In this paper, we study the support recovery guarantees of underdetermined sparse regression using the $\ell_1$-norm as a regularizer and a non-smooth loss function for data fidelity. More precisely, we focus in detail on the cases of $\ell_1$ and $\ell_\infty$ losses, and contrast them with the usual $\ell_2$ loss.While these losses are routinely used to account for either sparse ($\ell_1$ loss) or uniform ($\ell_\infty$ loss) noise models, a theoretical analysis of their performance is still lacking. In this article, we extend the existing theory from the smooth $\ell_2$ case to these non-smooth cases. We derive a sharp condition which ensures that the support of the vector to recover is stable to small additive noise in the observations, as long as the loss constraint size is tuned proportionally to the noise level. A distinctive feature of our theory is that it also explains what happens when the support is unstable. While the support is not stable anymore, we identify an "extended support" and show that this extended support is stable to small additive noise. To exemplify the usefulness of our theory, we give a detailed numerical analysis of the support stability/instability of compressed sensing recovery with these different losses. This highlights different parameter regimes, ranging from total support stability to progressively increasing support instability.

Asynchronous parallel optimization received substantial successes and extensive attention recently. One of core theoretical questions is how much speedup (or benefit) the asynchronous parallelization can bring to us. This paper provides a comprehensive and generic analysis to study the speedup property for a broad range of asynchronous parallel stochastic algorithms from the zeroth order to the first order methods. Our result recovers or improves existing analysis on special cases, provides more insights for understanding the asynchronous parallel behaviors, and suggests a novel asynchronous parallel zeroth order method for the first time. Our experiments provide novel applications of the proposed asynchronous parallel zeroth order method on hyper parameter tuning and model blending problems.