Deep learning, probably the most advanced and challenging foundation of artificial intelligence (AI), is having a significant impact and influence on many applications, enabling products to behave intelligently like humans. Favored by the introduction of higher-performance computers and systems for parallel computing, deep learning has today become a reality, especially in the field of image recognition and classification, voice recognition, text analysis, and virtual assistants. In recent years, we have witnessed the development of numerous models and architectures of neural networks (the basic structure on which deep learning is built), which led to the definition of data sets, ready to be used in real applications. Compared to traditional machine learning, deep learning can provide superior accuracy, greater versatility and use of big data. Models used in deep learning are based on deep neural networks (DNNs), which in turn can use different architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The choice of which architecture to use depends on the specific application: CNNs are particularly suited to image classification, while RNNs are normally used for text or speech recognition.

Bangalore: Zendesk has developed and trained deep-learning applications on AWS to deliver new capabilities to customer service organizations and scale it to support current and future application demands. In doing so, it relied on AWS GPU instances and the TensorFlow deep-learning framework to create new applications for customers. It needed to respond to a growing trend: customers wanting to quickly find answers to questions on their own, without having to talk to a support agent. "We wanted to give customers more relevant answers as fast as possible, and we wanted to drive a self-service customer support model," said Soon-Ee Cheah, a data scientist at Zendesk. Zendesk met this challenge by leveraging deep learning – a popular branch of artificial intelligence (AI).

Back in 2008, Yair Lavi founded Tonara, an interactive app that "listens to" musicians and assists them as they play. He has transported the lessons learned there to his latest venture, 3DSignals, where the co-founder and head of algorithms uses ultrasonic sensors and deep-learning software to detect anomalies in machine sounds. "Industrial music" you might say. Smart Industry: How do you define "deep learning"? Yair: Deep learning is a method of artificial intelligence used to detect patterns in data, either independently or based on some type of training.

The first hugely successful consumer application of deep learning will come to market: I predict that deep learning's first avid embrace by the general public will come in 2017. And I predict that it will be to process the glut of photos that people are capturing with their smartphones and sharing on social media. In this regard, the golden deep-learning opportunities will be in apps that facilitate image search, auto-tagging, auto-correction, embellishment, photorealistic rendering, resolution enhancement, style transformation, and fanciful figure inception. Where audio processing is concern, deep learning's first mainstream success in 2017 may very well be in composing music that feels like it was created by an actual human musician. Deep learning may also enter our lives in the coming year as the intelligence that driving a new generation of wearables that helps disabled people to see, hear, and otherwise sense their surroundings.

Intel Corp. signaled it wants a bigger role in artificial intelligence, revealing plans to modify a line of chips to target a fast-growing market turning into a battleground for technology suppliers. The company told technology developers Wednesday that it plans next year to deliver a new version of the Xeon Phi processor--a product line previously targeted at scientific applications--with added features designed to accelerate tasks associated with what Silicon Valley calls artificial intelligence. Intel said the technology will help accelerate a technique called deep learning, increasingly used for tasks such as interpreting speech, identifying objects in photos and piloting autonomous vehicles. Intel's Xeon processors already are a fixture in data centers, and have a role in nearly all deep-learning tasks carried out there. But some users also install auxiliary processors for artificial-intelligence tasks, notably chips called GPUs that rival Nvidia Corp. has long sold for videogames.

Few companies enjoy the kind of dominance Intel Corp. INTC 0.11 % does in chips for the computers found in data centers. But competitors keep trying to pry open its server stronghold, with International Business Machines Corp. IBM, at a Silicon Valley technical conference on Tuesday, plans to reveal new details of Power9, the next addition to the line of microprocessors the technology giant uses in its own servers and--in a recent strategy shift--offers to other hardware companies. Advanced Micro Devices Inc., AMD 1.19 % meanwhile, is using the same event to discuss the inner workings of processor technology called Zen that it plans to use in chips targeting servers and other hardware. AMD, which uses the same x86 design as Intel, last week at a company event demonstrated a chip using Zen processor cores outpacing its larger rival's chips in one speed test. "We had let our performance slip versus the competition," said Mark Papermaster, AMD's senior vice president and chief technology officer.

Intel Corp. INTC 0.34 % signaled it wants a bigger role in artificial intelligence, revealing plans to modify a line of chips to target a fast-growing market turning into a battleground for technology suppliers. The company told technology developers Wednesday that it plans next year to deliver a new version of the Xeon Phi processor--a product line previously targeted at scientific applications--with added features designed to accelerate tasks associated with what Silicon Valley calls artificial intelligence. Intel said the technology will help accelerate a technique called deep learning, increasingly used for tasks such as interpreting speech, identifying objects in photos and piloting autonomous vehicles. Intel's Xeon processors already are a fixture in data centers, and have a role in nearly all deep-learning tasks carried out there. But some users also install auxiliary processors for artificial-intelligence tasks, notably chips called GPUs that rival Nvidia Corp. NVDA 0.42 % has long sold for videogames.

Intel Corp. INTC -0.54 % signaled it wants a bigger role in artificial intelligence, revealing plans to modify a line of chips to target a fast-growing market turning into a battleground for technology suppliers. The company told technology developers Wednesday that it plans next year to deliver a new version of the Xeon Phi processor--a product line previously targeted at scientific applications--with added features designed to accelerate tasks associated with what Silicon Valley calls artificial intelligence. Intel said the technology will help accelerate a technique called deep learning, increasingly used for tasks such as interpreting speech, identifying objects in photos and piloting autonomous vehicles. Intel's Xeon processors already are a fixture in data centers, and have a role in nearly all deep-learning tasks carried out there. But some users also install auxiliary processors for artificial-intelligence tasks, notably chips called GPUs that rival Nvidia Corp. NVDA -2.32 % has long sold for videogames.