Intel is taking a new direction in chip development as it looks to the future of artificial intelligence, with the company betting the technology will pervade applications and web services. The company on Thursday said it is developing new chips that will handle AI workloads, which will increasingly be a part of its chip future. For now, the AI chips will be released as specialized primary chips or co-processors in computers and separate from the major product lines. But over time, Intel could adapt and integrate the AI features into its mainstream server, IoT, and perhaps even PC chips. The AI features could be useful in servers, drones, robots, and autonomous cars.

The tuna industry is already big business, pulling in billions of dollars each year and now it's due to get another boost with thanks to deep learning techniques that aim to get computers to recognize tuna automatically. The next step up from this would be for the computers to be then able to distinguish between albacore and yellowfin tuna. This is the plan moving forward for the Nature Conservancy, who are a non-profit environmental organization that looks to conserve all lands and water on which life depends. By using AI deep learning techniques, their hope is that they can help fisherman reduce the amount of protected species that are accidentally being caught when fishing for tuna (including sharks and turtles). It will also help to prevent overfishing while allowing endangered species a chance to recover.

These three are the neural networks (or neural nets) that a team from Google Brain, Google's research division for machine deep learning, developed to see just how well artificial intelligence (AI) can keep secrets. It turns out, they can do it pretty well. In a study published on arXiv, researchers Martín Abadi and David Andersen feature how neural nets can develop their own simple encryption techniques in order to keep messages from eavesdroppers, even without being given special cryptographic algorithms. In theory, neural nets "are generally not meant to be great at cryptography," the researchers said. The experiment involved these three, affectionately named, neural nets.

Microsoft researchers recently built an artificially intelligent system that seems to recognize conversational speech as effectively as a human. Yes, this research comes with caveats, but it's part of a very real and very rapid leap in artificial intelligence over the past several years, a leap driven by deep neural networks. These sweepingly complex algorithms can teach themselves very particular tasks by analyzing vast amounts of data. Microsoft's system learned to recognize words by looking for patterns in old tech support calls. But it's not just the algorithms that are driving the recent revolution in AI. Microsoft's speech rec system relies on large farms of GPU processors, chips that were originally designed for rendering graphics but have proven remarkably adept at running artificial intelligence models.

We now experience life through an algorithmic lens. Whether we realize it or not, machine learning algorithms shape how we behave, engage, interact, and transact with each other and with the world around us. Deep learning is the next advance in machine learning. While machine learning has traditionally been applied to textual data, deep learning goes beyond that to find meaningful patterns within streaming media and other complex content types, including video, voice, music, images, and sensor data. Deep learning enables your smartphone's voice-activated virtual assistant to understand spoken intentions.

Like many technology companies nowadays, Intel is trying to capitalize on the rise and hype of artificial intelligence. At a media event Thursday in San Francisco, Intel CEO Bryan Krzanich made the case for why he believes his company's chip technologies and products are best suited to power the various forms of trendy, cutting-edge data crunching techniques popularized in recent years by companies like Google goog, Facebook fb, IBM ibm, and others. These technology companies have used A.I. techniques like deep learning to train their computers to perform feats like translating text into different languages and recognizing objects in pictures. Intel intc is betting that more companies beyond just these technology giants will incorporate advanced data analytics into their business--and will need to buy the chips to power the tasks. With Intel's core personal computer chip-making business declining (along with the rest of the PC market), the company has been trying to shift to selling beefier chips that run inside company data centers.

First impressions are notoriously subjective (and flawed), but now machines are being trained to make similar snap judgments based on human-generated data. AI-published books may not be too far away! Researchers at Kyushu University in Japan have trained a deep neural network to study book covers and determine their category. On a hunt for interesting and high-quality datasets to use for machine learning, I stumbled upon these 20 weird and wonderful sets. Neat! Check out this open source deep convolutional neural network that is trained on transformed audio signals to recognize "ahem" sounds.

Diane Bryant, Intel executive vice president and general manager of the Data Center Group, stands with Nervana co-founder Naveen Rao. Within just two months acquiring Nervana, Intel is integrating the deep learning startup's technology into its product roadmap, the company announced Thursday. The new Nervana platform will be the industry's most comprehensive portfolio for AI, the company contends, built for speed and ease of use. "We expect the Intel Nervana platform to produce breakthrough performance and dramatic reductions in the time to train complex neural networks," Diane Bryant, Intel executive vice president and general manager of the Data Center Group, said in a statement. "Before the end of the decade, Intel will deliver a 100-fold increase in performance that will turbocharge the pace of innovation in the emerging deep learning space."

But the newest artificial intelligence system from Google's DeepMind division does indeed dream, metaphorically at least, about finding apples in a maze. Researchers at DeepMind wrote in a paper published online Thursday that they had achieved a leap in the speed and performance of a machine learning system. It was accomplished by, among other things, imbuing technology with attributes that function in a way similar to how animals are thought to dream. The paper explains how DeepMind's new system -- named Unsupervised Reinforcement and Auxiliary Learning agent, or Unreal -- learned to master a three-dimensional maze game called Labyrinth 10 times faster than the existing best AI software. It can now play the game at 87 per cent the performance of expert human players, the DeepMind researchers said.