Intel’s Nervana DevCloud Offers Free Access To AI Compute Power For 200,000 People
The chipmaker appears to be fully committed to the powering the rise of artificial intelligence
Intel is offering developers, academics, data scientists, and startups free access to cloud-based compute power to help kick-start work on artificial intelligence (AI) projects.
Some 200,000 people, including startups from Intel’s Nervana AI Academy, will have access to the chipmaker’s new Nervana DevCloud.
The cloud service will pipe out processing power courtesy of Intel’s latest Xeon processors, which can be used to power machine learning algorithms, deep leaning neural networks, and the inference processes of AI systems.
Intel is championing the free access to its Nervana DevCloud as a way to help people working on AI get their projects underway without investing in expensive hardware.
However, it could be argued that Intel’s motivations are not purely altruistic, as it would have developers and researchers working on AIs fulled by the chipmakers processing chops, arguably laying the foundations for an AI product and software ecosystem reliant on Intel’s chips and server processor power.
And appears to be more than committed in putting itself forward as the company to fuel the development and adoption of AI technology.
“As the world becomes smart and connected, the premise for AI strengthens. AI is based on the ability of machines to sense, reason, act and adapt based on learned experience,” said Intel’s chief executive Brian Krzanich.
“Across this AI spectrum, Intel is using data analytics, machine learning and deep learning to enable holistic machine intelligence.”
However, Intel has a lot of competition in the AI world when it comes to providing the cloud and compute power to accelerate smart tech development; Google has a significant interest in promoting Ai and its widespread and accessible use.
And the search giant has already touted its own AI chips which it claims can trounce processors and graphics accelerators from other companies when it comes to powering machine learning-based workloads.
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