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How AI is decommoditizing the chip industry

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Since the early days of computing, there has always been this idea that artificial intelligence would one day change the world. We've seen this future depicted in countless pop culture references and by futurist thinkers for decades, yet the technology itself remained elusive. Incremental progress was mostly relegated to fringe academic circles and expendable corporate research departments. That all changed five years ago. With the advent of modern deep learning, we've seen a real glimpse of this technology in action: Computers are beginning to see, hear, and talk.


5 Key Advantages of AI Data Storage NetApp Blog

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In this blog series, I've focused on how NetApp can help you streamline your artificial intelligence projects. With technologies and services for managing data everywhere, NetApp is well positioned to solve your AI data challenges. Built on our partnership with NVIDIA and powered by NVIDIA DGX supercomputers and NetApp all-flash storage, ONTAP AI lets you simplify, accelerate, and scale your AI data pipeline to gain deeper understanding in less time. Combining Data Fabric enabled NetApp storage with GPU-accelerated NVIDIA computing systems results in capabilities that aren't available from other turnkey AI solutions, on-premises or in the cloud. Here are five of the key advantages of ONTAP AI.


Intel buys deep-learning startup Vertex.AI to join its Movidius unit

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Intel has an ambition to bring more artificial intelligence technology into all aspects of its business, and today is stepping up its game a little in the area with an acquisition. The computer processing giant has acquired Vertex.AI, a startup that had a mission of making it possible to develop "deep learning for every platform", and had built a deep learning engine called PlaidML to do this. Terms of the deal have not been disclosed but Intel has provided us with the following statement, confirming the deal and that the whole team -- including founders Choong Ng and Brian Retford -- will be joining Intel. "Intel has acquired Vertex.AI, a Seattle-based startup focused on deep learning compilation tools and associated technology. The seven-person Vertex.AI team joined the Movidius team in Intel's Artificial Intelligence Products Group.


Deep Learning Stretches Up to Scientific Supercomputers

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The team achieved a peak rate between 11.73 and 15.07 petaflops (single-precision) when running its data set on the Cori supercomputer. Machine learning, a form of artificial intelligence, enjoys unprecedented success in commercial applications. However, the use of machine learning in high performance computing for science has been limited. Why? Advanced machine learning tools weren't designed for big data sets, like those used to study stars and planets. A team from Intel, National Energy Research Scientific Computing Center (NERSC), and Stanford changed that.


Deep learning stretches up to scientific supercomputers

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Machine learning, a form of artificial intelligence, enjoys unprecedented success in commercial applications. However, the use of machine learning in high performance computing for science has been limited. Why? Advanced machine learning tools weren't designed for big data sets, like those used to study stars and planets. A team from Intel, National Energy Research Scientific Computing Center (NERSC), and Stanford changed that situation. They developed the first 15-petaflop deep-learning software.


Machine Learning on Edge Brings AI to IoT

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Machine learning can become a robust analytical tool for vast volumes of data. The combination of machine learning and edge computing can filter most of the noise collected by IoT devices and leave the relevant data to be analyzed by the edge and cloud analytic engines. The advances in Artificial Intelligence have allowed us to see self-driving cars, speech recognition, active web search, and facial and image recognition. Machine learning is the foundation of those systems. It is so pervasive today that we probably use it dozens of times a day without knowing it.


Why Google is betting big on AI

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The recent Google I/O conference saw the internet giant unveil is artificial intelligence intentions. From modest machine learning beginnings, Google has unveiled products that intend to revolutionise our daily lives and bring machine learning into all current applications. As the development of mobile applications hit the tech world by storm a few years ago, now is the time of AI and Google intends to capitalise with a regime change. In addition to improving the functionality of Google Home, Search and Photos, the media giant unveiled a new innovation called Google Lens. Home can recognise voices, Search recognises and recommends search results, and what Google Lens brings to the table, is interpreting the surroundings and taking actions based on that information.


A brain-inspired chip from IIT-Delhi could be the next big leap in AI hardware FactorDaily

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"The human brain has 100 billion neurons, each neuron connected to 10 thousand other neurons. Sitting on your shoulders is the most complicated object in the known universe," Michio Kaku, Physicist and Futurist The human brain, which not just stores but also computes, is by far the most powerful and complex computers in the world that occupies just 1.3 litres of space and consumes about 20 watts of power. In comparison, the finest supercomputers in the world require gigawatts of power, massive real estate, infrastructure, and dedicated cooling systems while attempting to perform brain-like tasks. Understanding how the human brain functions and replicating it has been a lifelong quest for the scientific and research community. Enter neuromorphic computing, a concept developed by American scientist and researcher Carver Andress Mead in the late 1980s – which tries to emulate certain functions of the human brain in silicon.


The rise of on-device AI begins with Qualcomm

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Although there is often lots of hype surrounding Artificial Intelligence (AI), once we strip away the marketing fluff, what is revealed is a rapidly developing technology that is already changing our lives. But to fully appreciate its potential, we need to understand what it is and what it is not! Defining "intelligence" is tricky, but key attributes include logic, reasoning, conceptualization, self-awareness, learning, emotional knowledge, planning, creativity, abstract thinking, and problem solving. From here we move onto the ideas of self, of sentience, and of being. Artificial Intelligence is therefore a machine which possesses one or many of these characteristics.


Artificial Neural Networks: How To Understand Them And Why They're Important

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If you dip even a toe into the realm of artificial intelligence, you'll come across artificial neural networks. Artificial neural networks are the systems that power artificial intelligence. It's a type of computer that doesn't just read code that it already understands. Neural networks process vast amounts of information to help create an understanding of what's already right in front of you. People think the key to understanding neural networks is calculus, but this system of computing has roots in biology.