Intel's Nervana platform is a $400 million investment in AI Back in November, Intel announced what it claims is a comprehensive AI platform for data center and compute applications called Nervana, with its focus aimed directly at taking on Nvidia's GPU solutions for enterprise users. The platform is the result of the chipmaker's acquisition of 48-person startup Nervana Systems back in August for $400 million that was led by former Qualcomm researcher Naveen Rao. Built using FPGA technology and designed for highly-optimized AI solutions, Intel claims Nervana will deliver up to a 100-fold reduction in the time it takes to train a deep learning model within the next three years. The company intends to integrate Nervana technology into Xeon and Xeon Phi processor lineups. During Q1, it will test the Nervana Engine chip, codenamed'Lake Crest,' and make it available to key customers later within the year.

You're out with your mates, having a great time, then you whip out your phone to take a cheeky selfie to record your merry antics. Only when you look back at the photo you're not quite as photogenic as you thought. Selfies aren't exactly the most flattering way to take a portrait. But all that looks set to change thanks to the clever folks at the Adobe Research team. They've been exploring the future of selfie photography by packing artificial intelligence and deep learning tools into smartphones.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. This video was published on March 31, not April 1, which I assume means that Pepper's fish mode is going to be a real thing: In fact, it's possible that this capability has already been enabled, so feel free to toss your Pepper into the nearest lake and let us know how it goes. Kinema Pick is the world's first Deep Learning 3D Vision system for industrial robots.

Artificial intelligence has reached peak hype. News outlets report that companies have replaced workers with IBM Watson and that algorithms are beating doctors at diagnoses. New AI startups pop up everyday, claiming to solve all your personal and business problems with machine learning. Ordinary objects like juicers and Wi-Fi routers suddenly advertise themselves as "powered by AI." Not only can smart standing desks remember your height settings, they can also order you lunch.

Machine learning couldn't be hotter. A type of artificial intelligence that enables computers to learn to perform tasks and make predictions without explicit programming, machine learning has caught fire among the hip tech set, but remains a somewhat futuristic concept for most enterprises. But thanks to technological advances and emerging frameworks, machine learning may soon hit the mainstream. Consulting firm Deloitte expects to see a big increase in the use and adoption of machine learning in the coming year. This is in large part because the technology is becoming much more pervasive.

AI research has a long history of repurposing old ideas that have gone out of style. Now researchers at Elon Musk's open source AI project have revisited "neuroevolution," a field that has been around since the 1980s, and achieved state-of-the-art results. The group, led by OpenAI's research director Ilya Sutskever, has been exploring the use of a subset of algorithms from this field, called "evolution strategies," which are aimed at solving optimization problems. Despite the name, the approach is only loosely linked to biological evolution, the researchers say in a blog post announcing their results. On an abstract level, it relies on allowing successful individuals to pass on their characteristics to future generations.

The Long Short-Term Memory recurrent neural network has the promise of learning long sequences of observations. It seems a perfect match for time series forecasting, and in fact, it may be. In this tutorial, you will discover how to develop an LSTM forecast model for a one-step univariate time series forecasting problem. Time Series Forecasting with the Long Short-Term Memory Network in Python Photo by Matt MacGillivray, some rights reserved. This is a big topic and we are going to cover a lot of ground. This tutorial assumes you have a Python SciPy environment installed. You can use either Python 2 or 3 with this tutorial. You must have Keras (2.0 or higher) installed with either the TensorFlow or Theano backend.

Computer-based artificial intelligence (AI) has been around since the 1940s, but the current innovation boom around everything from virtual personal assistants and visual search engines to real-time translation and driverless cars has led to new milestones in the field. And ever since IBM's Deep Blue beat Russian chess champion Garry Kasparov in 1997, machine versus human milestones inevitably bring up the question of whether or not AI can do things better than humans (it's the the inevitable fear around Ray Kurzweil's singularity). As image recognition experiments have shown, computers can easily and accurately identify hundreds of breeds of cats and dogs faster and more accurately than humans, but does that mean that machines are better than us at recognizing what's in a picture? As with most comparisons of this sort, at least for now, the answer is little bit yes and plenty of no. Less than a decade ago, image recognition was a relatively sleepy subset of computer vision and AI, found mostly in photo organization apps, search engines and assembly line inspection.

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is complete and will remain available online for free.

This paper presents a Convolutional Neural Network (CNN) based page segmentation method for handwritten historical document images. We consider page segmentation as a pixel labeling problem, i.e., each pixel is classified as one of the predefined classes. Traditional methods in this area rely on carefully hand-crafted features or large amounts of prior knowledge. In contrast, we propose to learn features from raw image pixels using a CNN. While many researchers focus on developing deep CNN architectures to solve different problems, we train a simple CNN with only one convolution layer. We show that the simple architecture achieves competitive results against other deep architectures on different public datasets. Experiments also demonstrate the effectiveness and superiority of the proposed method compared to previous methods.