Over the last several years, investors looking to benefit from the ongoing developments in the field of AI needed to look no further than graphics processing pioneer NVIDIA Corporation (NASDAQ:NVDA). The massive parallel computing capability that made GPU's the best choice for rendering images turned out to be just as effective for the training artificial intelligence (AI) systems. NVIDIA positioned itself to leverage that advantage and began optimizing processors specifically for that purpose. <\/p>\n
For a time, the GPU giant had the field to itself and financial results soared. In its most recent quarter, NVIDIA grew revenue to $1.937 billion, an increase of 48% over the prior-year quarter, while net income of $507 million jumped 144% year over year. The stock has tripled in the last year, and its valuation has jumped as well. NVIDIA now trades at an astonishing 49 times trailing earnings, with an only slightly less expensive forward multiple of 42. At these levels, any actual or perceived failure to execute could bring the stock crashing down. <\/p>\n
The good news is that investors looking to capitalize on the growing trend of AI can invest in a pioneer in the field that offers solid growth without the potential downside risk -- Google, a division of Alphabet Inc. (NASDAQ:GOOGL) (NASDAQ:GOOG). <\/p>\n
Alphabet is a way to invest in AI for more risk-averse investors. Image source: Pixabay. <\/p>\n
Google has been at the forefront of AI, and early research in deep learning, a specific discipline of AI, has led to advances in image recognition, language processing, and voice recognition. Suggesting the name of a friend to \"tag\" in a photo and the ability to ask questions of the virtual assistant on your smartphone are examples of the developments resulting from early successes in AI. <\/p>\n
Google developed TensorFlow, its open-source AI framework that developers use to more easily build their own AI systems. The company also created the tensor processing unit (TPU), a specialized chip that delivers optimized performance, while achieving significant improvements in energy efficiency. These were previously only used in the execution or \"inference\" phase of running AI systems that had previously been trained using GPU's. Google recently revealed that its second-generation TPU is now capable of both the inference and training phases of AI systems, putting it into direct competition with NVIDIA's GPU's. Google has not yet announced plans to market the chip but is currently using the processor internally. <\/p>\n
TPU's were instrumental in the historic win over a human champion in the ancient game of Go, one many thought too complex for a machine to master. These tools and technological advantages now underlie Google cloud computing systems and provide a catalyst for future growth. Market research company Gartner estimates that the cloud infrastructure-as-a-service (IaaS) market will top $34 billion in 2017, and grow to $71 billion by 2020. That market is currently dominated by Amazon.com,followed by Microsoft Corporation, but Google is third and closing fast. <\/p>\n
The use of cloud services is becoming particularly relevant to development in the field of AI. The ability to train these systems requires the intersection of big data and vast computing power, and many companies don't possess the financial resources to develop AI programs from scratch. The ability to piggyback off the systems offered by cloud providers has been key to advancing the research capability of smaller companies. <\/p>\n
AI will continue to revolutionize business. Image source: Getty Images. <\/p>\n
It is difficult to quantify the future revenue potential of AI, but certain anecdotal evidence can provide insight. In 2014, Google acquired AI start-up DeepMind in a deal estimated at $600 million.At the time, Google sought to eek further energy efficiency from its already miserly data centers and applied DeepMind's AI to the task. By regulating cooling systems, windows, and servers, and controlling 120 condition-based variables, the company was able to reduce the amount of energy used for cooling by 40%.This cut Google's total power consumption by 15%, saving the company hundreds of millions of dollars. <\/p>\n
While investors wait for the potential financial windfall that could result from AI, they can take heart that Google's principle business still thrives. In its most recent quarter, Alphabet increased revenue to $24.75 billion, up 22% over the prior-year quarter. Net income growth was similarly impressive at $5.4 billion, an increase of 29% year over year. <\/p>\n
Alphabet stock is up 33% over the last year, respectable by any measure, but nowhere near the blistering pace of NVIDIA's 200% rise. Still, as the old saying goes \"what goes up must come down.\" Google's development of the TPU illustrates a stark reality for NVIDIA. Should any processor or solution become generally available that improves the performance of the GPU, NVIDIA's future growth could slow considerably, and the stock will adjust to reflect that reality. Let the buyer beware. <\/p>\n
Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Teresa Kersten is an employee of LinkedIn and is a member of The Motley Fool's board of directors. LinkedIn is owned by Microsoft. Danny Vena owns shares of Alphabet (A shares) and Amazon. Danny Vena has the following options: long January 2018 $640 calls on Alphabet (C shares) and short January 2018 $650 calls on Alphabet (C shares). The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Amazon, and Nvidia. The Motley Fool has a disclosure policy. <\/p>\n
<\/p>\n
Go here to read the rest: <\/p>\n
Ignore NVIDIA Corporation: Here's a Better AI Stock - Motley Fool<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Over the last several years, investors looking to benefit from the ongoing developments in the field of AI needed to look no further than graphics processing pioneer NVIDIA Corporation (NASDAQ:NVDA). The massive parallel computing capability that made GPU's the best choice for rendering images turned out to be just as effective for the training artificial intelligence (AI) systems. NVIDIA positioned itself to leverage that advantage and began optimizing processors specifically for that purpose. Continue reading