When introducing its monster 72-core Xeon Phi chip, Intel couldn't help but take a swipe at graphics processors for being sluggish for some tasks. Ironically, Xeon Phi is a byproduct of Larrabee, which was supposed to be Intel's first major GPU but was abandoned in 2009 after multiple delays. The swipe was a shot at Nvidia, whose GPUs are flourishing in the gaming and machine learning areas. But Nvidia's success has also raised questions about whether Intel should've been patient and pursued Larrabee. Nevertheless, Xeon Phi has been successfully used in supercomputing, and now Intel wants to challenge Nvidia's GPU by bringing the chip to machine learning.

Silicon Valley is full of chatter about artificial intelligence, deep learning neural networks, and machine learning. And Intel, the world's biggest chip maker, is becoming a lot more conversant in that chatter today. Intel executive Diane Bryant announced today that the company is working on a next-generation version of its high-end server chip, the Xeon Phi, for A.I. applications. Baidu will use the upcoming Xeon Phi chips in the data centers it is building for its Deep Speech platform, where its networks will be able to parse natural language speech as quickly and accurately as possible. By 2020, there will be more servers handling data analytics than any other workload, Bryant said.

Intel has pulled open the curtain on a secretly developed mega-chip called Knights Mill, a key component in its artificial-intelligence strategy. The chip -- which belongs to the family of high-performance Xeon Phi processors -- gives Intel a legitimate opportunity to tackle machine learning. It is targeted at servers and workstations, and will be available in 2017. Intel was caught off-guard with the emergence of artificial intelligence as a way to analyze and present data. Knights Mill, introduced on Wednesday at the ongoing Intel Developer Forum, will fill a big hole in company's chip lineup.

Intel has pulled open the curtain on a secretly developed mega-chip called Knights Mill, a key component in its artificial-intelligence strategy. The chip -- which belongs to the family of high-performance Xeon Phi processors -- gives Intel a legitimate opportunity to tackle machine learning. It is targeted at servers and workstations, and will be available in 2017. Intel was caught off-guard with the emergence of artificial intelligence as a way to analyze and present data. Knights Mill, introduced on Wednesday at the ongoing Intel Developer Forum, will fill a big hole in company's chip lineup.

Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel. Machine learning, a trendy technology, allows software to be trained to do tasks like image recognition or data analysis more efficiently. Intel didn't disclose when the new features will be added, but the next version of Xeon Phi will come by 2018. Intel's already behind chip rivals in machine learning, so it may have to speed up the next Xeon Phi release.

Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel. Machine learning, a trendy technology, allows software to be trained to do tasks like image recognition or data analysis more efficiently. Intel didn't disclose when the new features will be added, but the next version of Xeon Phi will come by 2018. Intel's already behind chip rivals in machine learning, so it may have to speed up the next Xeon Phi release.

Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel. Machine learning, a trendy technology, allows software to be trained to do tasks like image recognition or data analysis more efficiently. Intel didn't disclose when the new features will be added, but the next version of Xeon Phi will come by 2018. Intel's already behind chip rivals in machine learning, so it may have to speed up the next Xeon Phi release.

After abandoning its own GPU for supercomputers, machine learning, and video games in 2009, Intel has returned to the market with a new 72-core Xeon Phi, to compete with NVIDIA's growing portfolio of GPUs. The Xeon Phi'Knights Landing' chip, announced at the International Supercomputing Conference in Frankfurt, Germany last week, is Intel's most powerful and expensive chip to date and is aimed at machine learning and supercomputers, two areas where Nvidia's GPUs have flourished. Inside the chip there is 72-cores running at 1.5GHz, alongside 16GB of integrated stacked memory. The chip supports up to 384GB of DDR4 memory, making it immensely scalable for machine learning programs. At the conference, Intel mentioned some of the issues with GPUs for complex machine learning programs.

Intel's chip arsenal appears to have some glaring weaknesses. One of them is the lack of a high-end graphics processor, which is important for gaming, virtual reality and machine learning. However, the company does have powerful alternatives: two monster chips that will be ammunition to take on GPUs and rival chips in the areas of machine learning and supercomputing, which are important to the company. In 2018, Intel will likely release a faster and more power-efficient Xeon Phi, a supercomputing chip that is already used in some of the world's fastest computers. Intel is also looking beyond CPUs to FPGAs (field programmable gate arrays), which can be faster at key tasks.

Intel's chip arsenal appears to have some glaring weaknesses. One of them is the lack of a high-end graphics processor, which is important for gaming, virtual reality and machine learning. However, the company does have powerful alternatives: two monster chips that will be ammunition to take on GPUs and rival chips in the areas of machine learning and supercomputing, which are important to the company. In 2018, Intel will likely release a faster and more power-efficient Xeon Phi, a supercomputing chip that is already used in some of the world's fastest computers. Intel is also looking beyond CPUs to FPGAs (field programmable gate arrays), which can be faster at key tasks.