Huang


AI everywhere

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"We invented a computing model called GPU accelerated computing and we introduced it almost slightly over 10 years ago," Huang said, noting that while AI is only recently dominating tech news headlines, the company was working on the foundation long before that. Nvidia's tech now resides in many of the world's most powerful supercomputers, and the applications include fields that were once considered beyond the realm of modern computing capabilities. Now, Nvidia's graphics hardware occupies a more pivotal role, according to Huang – and the company's long list of high-profile partners, including Microsoft, Facebook and others, bears him out. GTC, in other words, has evolved into arguably the biggest developer event focused on artificial intelligence in the world.


The New Intel: How Nvidia Went From Powering Video Games To Revolutionizing Artificial Intelligence

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It was in this same dingy diner in April 1993 that three young electrical engineers--Malachowsky, Curtis Priem and Nvidia's current CEO, Jen-Hsun Huang--started a company devoted to making specialized chips that would generate faster and more realistic graphics for video games. "We've been investing in a lot of startups applying deep learning to many areas, and every single one effectively comes in building on Nvidia's platform," says Marc Andreessen of venture capital firm Andreessen Horowitz. Starting in 2006, Nvidia released a programming tool kit called CUDA that allowed coders to easily program each individual pixel on a screen. From his bedroom, Krizhevsky had plugged 1.2 million images into a deep learning neural network powered by two Nvidia GeForce gaming cards.


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According to the keynotes delivered during several developer conferences over the past year, three key areas companies are looking to lead the technology industry in the future include a focus on machine learning, artificial intelligence and speech recognition. Ideally, the avenues of machine learning, speech recognition, and artificial intelligence will intersect and create a seamless experience for users who opt to communicate through burgeoning digital assistants or applications that rely heavily on cloud-connected data. Once again, CNTK allowed researchers to make use of sophisticated optimizations by way of learning algorithms that helped users and computers tap into quickened learning algorithms. One component of that AI strategy is conversation as a platform (CaaP); Microsoft outlined its CaaP strategy at the company's annual developer conference earlier this year."


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Classics (Collection 2)

When an observer moves relative to the environment, the two-dimensional (2-D) image that is projected onto the eye undergoes complex changes. These changes however, contain information regarding the relative 3-D motion and the structure of the scene in view. There exist several representations for the pattern of movement of features in the image, containing different amounts of information related to 3-D motion and shape. The ones most studied are optical flow, normal optical flow, and discrete displacements. OPTICAL FLOW Optical flow (Gibson, 1950) can be represented by a 2-D field of velocity vectors as shown in Figure 1.