Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem encountered in compressive sensing, where one seeks to recover a sparse signal from a small number of noisy linear measurements. In this paper, we propose a novel neural-network architecture that decouples prediction errors across layers in the same way that the approximate message passing (AMP) algorithm decouples them across iterations: through Onsager correction. Numerical experiments suggest that our "learned AMP" network significantly improves upon Gregor and LeCun's "learned ISTA" network in both accuracy and complexity.

Google just paid for part of its acquisition of DeepMind in a surprising way. The internet giant is using technology from the DeepMind artificial intelligence subsidiary for big savings on the power consumed by its data centers, according to DeepMind Co-Founder Demis Hassabis. In recent months, the Alphabet Inc. unit put a DeepMind AI system in control of parts of its data centers to reduce power consumption by manipulating computer servers and related equipment like cooling systems. It uses a similar technique to DeepMind software that taught itself to play Atari video games, Hassabis said in an interview at a recent AI conference in New York. The system cut power usage in the data centers by several percentage points, "which is a huge saving in terms of cost but, also, great for the environment," he said.

This is one of those sites you're going to want to try yourself. Take any black and white image, feed it to the algorithm, and watch as it spits out its best guess at a color version, which is often quite convincing. Using a deep learning algorithm developed by Richard Zhang, Phillip Isola, and Alexei Efros of UC Berkeley, the process was trained on one million images. Though it currently fools humans only 20% of the time, that's still a significantly higher rate than previous iterations and represents an exciting step forward, and further training should only increase that rate. Imagine a time when Photoshop can colorize a photo in one step, leaving the end-user to just tweak a few hues here and there.

Jan: Astonishing increases in computing power and data availability in recent years have been the main benefactors of deep learning technology. Hitoshi: Some of the easily understandable applications, such as image recognition, video captioning and beating the world champion of Go, are pushing people hard to be excited. From a technical perspective, the generality and high accuracy that deep learning has is the main motivation for using it instead of other machine learning methods. In our case, for example, our AI engine learns how traders trade from the technical chart, no matter what kind of strategy or what kind of indicators they use. Alesis: The computational power and tools to utilize that power has definitely enabled the recent advancements in Deep Learning.

Google DeepMind's artificial intelligence AlphaGo made history when it won the complex game of Go against Lee Sedol, one of the greatest world players. As Elon Musk pointed out at the time, experts in the field thought AI was a decade away from reaching that milestone. The momentous event showed that AI was gaining skills typically reserved for humans far faster than we expected. And that very fact could be a problem, Ian Pearson, a futurist with an 85% accuracy track record, told Tech Insider. "You could end up with superhuman machines going down that road," Pearson said.

About NVIDIA NVIDIA (NASDAQ: NVDA) is a computer technology company that has pioneered GPU-accelerated computing. It targets the world's most demanding users -- gamers, designers and scientists -- with products, services and software that power amazing experiences in virtual reality, artificial intelligence, professional visualization and autonomous cars. Certain statements in this press release including, but not limited to, statements as to: the benefits and impact of the NVIDIA Inception Program; NVIDIA's commitment to help companies related to artificial intelligence; and funding through NVIDIA's GPU Ventures Program are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-Q for the fiscal period ended May 1, 2016. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge.

Here's What We Can Expect from Deep Learning in 2016 and Beyond

CES--Volvo Cars will use the NVIDIA DRIVE PX 2 deep learning- based computing engine to power a fleet of 100 Volvo XC90 SUVs starting to hit the road next year in the Swedish carmaker's Drive Me autonomous-car pilot program, NVIDIA announced today. Autonomous technology is an important contributor to Volvo's Vision 2020 -- its guiding principles for creating safer vehicles. This work has resulted in world-leading advancements in autonomous and semi-autonomous driving, and a new safety benchmark for the automotive industry. "Our vision is that no one should be killed or seriously injured in a new Volvo by the year 2020," said Marcus Rothoff, director of the Autonomous Driving Program at Volvo Cars. "NVIDIA's high-performance and responsive automotive platform is an important step towards our vision and perfect for our autonomous drive program and the Drive Me project."

That burrito in your hands--so warm, so gooey, the richness cut by cilantro and red-hot spice. Before you take a bite, you'd better take a picture. Multiply that impulse by tens of thousands and you get Yelp's database of images, drawn from burrito joints, cocktail bars, and more. Until recently, Yelp was dependent on users to tag their images with search-friendly metadata. But now, using the kind of deep learning techniques that are transforming the field of AI, Yelp is starting to see the business benefits of using software intelligence to power its listing pages and user recommendations.

Not many people understand how these unwanted messages are separated from wanted messages. You can't simply filter based on sender address, as new spam addresses can easily be created. The second reason is that spam is often sent from legitimate email accounts hijacked by third parties. The best way to separate spam is to look at the content of the email messages. The most effective techniques to do this are based on machine learning.