Intel Outside as Other Companies Prosper from AI Chips

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Back in 1997, Andy Grove, then chief executive officer of Intel, became one of the first corporate titans to embrace the teachings of Harvard Business School professor Clayton Christensen. Sensing that Intel might be undercut by PC chip rivals with cheaper wares, Grove invited Christensen to speak to his team about industrial leaders of the past who had waited too long to address emerging threats. Within a few quarters, Intel had brought out a line of lower-end Celeron chips for PCs, which pretty much smashed the dreams of Intel wannabes such as Advanced Micro Devices. Intel is no longer a case study in adaptability. On the contrary, it has whiffed in the market for mobile chips used in smartphones and tablets, by far the largest new opportunity for chip makers in the past 10 years.


Chipmakers Are Racing To Build Hardware For Artificial Intelligence

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In recent years, advanced machine learning techniques have enabled computers to recognize objects in images, understand commands from spoken sentences, and translate written language. But while consumer products like Apple's Siri and Google Translate might operate in real time, actually building the complex mathematical models these tools rely on can take traditional computers large amounts of time, energy, and processing power. As a result, chipmakers like Intel, graphics powerhouse Nvidia, mobile computing kingpin Qualcomm, and a number of startups are racing to develop specialized hardware to make modern deep learning significantly cheaper and faster. The importance of such chips for developing and training new AI algorithms quickly cannot be understated, according to some AI researchers. "Instead of months, it could be days," Nvidia CEO Jen-Hsun Huang said in a November earnings call, discussing the time required to train a computer to do a new task.


Chipmakers Are Racing To Build Hardware For Artificial Intelligence

#artificialintelligence

In recent years, advanced machine learning techniques have enabled computers to recognize objects in images, understand commands from spoken sentences, and translate written language. But while consumer products like Apple's Siri and Google Translate might operate in real time, actually building the complex mathematical models these tools rely on can take traditional computers large amounts of time, energy, and processing power. As a result, chipmakers like Intel, graphics powerhouse Nvidia, mobile computing kingpin Qualcomm, and a number of startups are racing to develop specialized hardware to make modern deep learning significantly cheaper and faster. The importance of such chips for developing and training new AI algorithms quickly cannot be understated, according to some AI researchers. "Instead of months, it could be days," Nvidia CEO Jen-Hsun Huang said in a November earnings call, discussing the time required to train a computer to do a new task.