AI is increasingly being used in everything and everywhere, most notably data centers for machine learning, where Nvidia's GPUs have a tremendous lead over the competition, including anything from AMD (AMD) and Intel (INTC). In reacting to the blow-out earnings results, Jefferies analyst Mark Lipacis called Nvidia essentially the only publicly traded company solely focused on AI. Of the aforementioned areas, perhaps the one that is most tangible to show consumers the AI revolution is autonomous driving. Tesla (TSLA) and CEO Elon Musk have pioneered autonomous driving in the Model S (though to which extent is up for debate), but Tesla has chosen to use Nvidia's chipsets to not only help it with its infotainment unit, but more importantly, the software that helps with autonomy, using the NVIDIA DRIVE PX 2 AI computing platform.
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Many are made with developer boards like Nvidia's Jetson TX1, which provides the smarts for auto-navigation and collision avoidance. TX1 has the horsepower to process live image feeds, and software tools to instantly analyze and provide context to visuals. The board relies on a new machine-learning engine called TensorRT to analyze pixels and provide the right context to images. It's possible to make robots with developer boards like Raspberry Pi, but most don't have the AI software tools to support machine learning.
The devices, designed to give a significant boost to artificial intelligence, deep learning and advanced data analytics, were picked up by the Chinese telecommunications company Tencent, and IBM claims the results are basically out of this world. The first of the three, and obviously the flagship server, is the IBM Power System S822LC for High Performance Computing. "The user insights and the business value you can deliver with advanced analytics, machine learning and artificial intelligence is increasingly gated by performance. All three servers, Power System S822LC for High Performance Computing, IBM Power System S821LC and the IBM Power System S822LC for Big Data are available today, with the starting price of 5,999 ( 4,513).
Anthem -- In that same report from Jefferies, Anthem was downgraded to "hold" from "buy." Kate Spade -- The fashion accessories maker was upgraded to "outperform" from "market perform" at Wells Fargo, which thinks recent stock weakness has created an attractive entry point and that it sees a pickup in coming quarters. Boeing -- Boeing increased its long-term sales outlook for the China market to 1.025 trillion over the next 20 years, with estimated aircraft purchases of 6,810. NRG Energy -- NRG won a bankruptcy auction for SunEdison's wind and solar projects in Texas and other states.
As dominant as Intel is in the PC CPU market, it faces essentially no real competition in the server CPU market, leading to operating margins in the data center segment in excess of 40%. While Intel's future earnings growth is uncertain, even slow dividend growth makes Intel a far more attractive dividend stock than NVIDIA. Qualcomm pays a 0.53 quarterly dividend, good for a dividend yield of about 3.4%. However, with an attractive yield, even slow dividend growth makes Qualcomm a superior dividend stock.
As the cloud industry continues to show significant growth led by Amazon ( AMZN), Microsoft ( MSFT), IBM ( IBM) and Google ( GOOG), the race for artificial intelligence supremacy is now heating up. GuruFocus has detected 3 Warning Signs with TSE:4519. GuruFocus has detected 7 Warning Signs with NUS. GuruFocus has detected 5 Warning Signs with GOOG.
IDF16 Intel is working on a powerful Xeon Phi processor for servers and workstations that is "optimized" for artificial-intelligence software – and it's codenamed Knights Mill. According to Intel, Knights Mill "is optimized for scale-out analytics implementations, and will include key enhancements for deep learning training. For today's machine learning applications, the large memory size of the Intel Xeon Phi processor family helps customers like Baidu make it easier to train their models efficiently." Meanwhile, Knights Landing, the second-generation 14nm Phi announced in 2013, went on sale in June.
Technology giant Nvidia has revealed how one of its engineers has used machine learning at his home to keep cats off his prized lawn. Robert Bond, who has previously used the firm's Jetson TX1 platform to build a laser to take out the ants that appeared on his kitchen floor, used the same machine learning technology to turn his sprinklers into a smart identification system that could spot cats that appeared on or near the lawn before triggering the sprinklers to shoo the feline visitors away. Once it is detected, a camera aimed at Robert's front garden captures an image. Intelligent software running on Jetson then scans the image for any signs of shapes it can identify as cats.
Microsoft has been using a type of programmable chip called Field Programmable Gate Arrays to improve its hardware for machine learning, which typically requires a large amount of computing power. Last year, Google released Tensor Flow, the software engine that powers its machine learning systems, free to the public via an open-source license. But while Google's chip is helping improve its machine learning tools, the company likely isn't in a position to abandon GPUs and processors made by other companies entirely, Patrick Moorhead, an analyst at Moore Insights & Strategy, told PCWorld. It began using the TPU last April to help its StreetView software better process images, Jouppi told the Journal, speeding up the processing time for all of its images to just five days.