Deep learning is a technique of machine learning in the field of artificial intelligence (AI) that is seeing explosive growth. Every major tech company globally is jumping on board, and the segment is booming.

While there is agreement that the size of the deep-learning market will be huge, there is no consensus on just how big it could become. According to a report by Persistence Market Research, deep learning will generate an estimated $4.8 billion by the end of 2017, and it will skyrocket to a massive $261 billion by 2027 -- a compound annual growth rate of 49%. Stratistics MRC calculated a market worth of $1.95 billion in 2016, which it expects to grow to $72.10 billion by 2023.

A market opportunity of that magnitude has attracted some of the foremost companies in technology, who are acquiring AI start-ups and ramping up R&D spending in order to increase their competitive position in a technology that is still in its infancy. Here's a list of some of the largest players in deep learning:


Market Cap (billions)

Principle Use

Apple Inc. 





Advertising, search

Microsoft Corporation 


Cloud computing, Inc. (NASDAQ:AMZN)


Cloud computing

Facebook, Inc.


Advertising, text analysis

Intel Corporation 



International Business Machines 


Business services




Baidu, Inc. 


Advertising, search, Inc. 



With so many companies to choose from, which ones have the greatest opportunity to capitalize on this nascent trend? Below, I'll tell you why my favorites in the field are Google, Amazon, and NVIDIA.

Human brain overlaid with a circuit board.

Deep learning is a technique of machine learning that creates intelligent machines. Image source: Getty Images.

What is deep learning?

Before we examine the key players, it might first help to define what this technology is. Deep learning is a specific technique within the field of artificial intelligence (AI). Data scientists build computer models inspired by the structure and function of the human brain, called artificial neural networks. These systems use a combination of sophisticated algorithms and millions of data points in an attempt to reproduce our capacity to learn. The network is fed a multitude of examples and "learns" to distinguish differences and discover relationships and similarities among the examples. In its simplest form, the program learns to recognize patterns and make associations from the massive quantities of data it ingests. It can then make decisions or predictions based on what it has learned. 

The concept of deep learning has been around for decades, but it was the recent convergence of three trends that finally made it practical. The advent of big data, faster processors, and better algorithms combined to enable the technological advances that are occurring today. This includes such breakthroughs as voice recognition, natural language translation, image recognition, and user profiling.

It's still early days in the application of deep learning, but examples of the advances that have occurred abound. Digital assistants, voice-controlled smart speakers, and self-driving cars are all the result of recent developments in deep learning.

NVIDIA DGX-1 AI supercomputer.

The NVIDIA DGX-1 is the latest offering geared specifically for AI applications. Image source: NVIDIA. 


NVIDIA is best known as the pioneer of the graphics processing unit (GPU) back in 1999. The development of this chip revolutionized gaming, providing players with more lifelike images then were previously possible. In a feat known as parallel processing, the GPU could perform many complex mathematical calculations at the same time by dividing the work among a number or processors and executing it simultaneously. This provided the key to quickly rendering realistic graphics.

Several years ago, researchers discovered that the speed and mathematical precision of parallel processing could also accelerate the training of deep-learning systems.

Much of NVIDIA's growth over the last several years is the direct result of its success in deep learning. In its 2017 third quarter, NVIDIA posted record revenue of $2.64 billion, which grew 32% year over year. The strongest growth came from its data center segment, where deep-learning sales are reported, which grew to $501 million, up 109% over the prior-year quarter. This is the sixth consecutive quarter of triple-digit year-over-year gains and the ninth consecutive quarter of sequential increases.

While there are potential contenders, there is currently no better tool for accelerating the training of deep-learning systems than the GPU, and NVIDIA will likely continue to reap the benefits.

A black Amazon Echo placed on a kitchen counter near a coffee cup and blueberries.

The Echo family of smart speakers is central to Amazon's consumer-facing deep-learning technology. Image source: Amazon.


Amazon is primarily known for its gargantuan e-commerce operation, its Prime member loyalty program, and its Amazon Web Services (AWS) cloud computing business. The company has been hard at work developing what could potentially be the fourth pillar of its business -- artificial intelligence.

For more than four years, Amazon has been using deep learning to enhance various aspects of its core business. The company has been able to improve search results, make better product recommendations, and produce more accurate forecasting for its inventory management.

The company made deep-learning capabilities available to its AWS customers about a year ago, providing text-to-speech, image recognition, and the ability to create sophisticated chatbots. Amazon recently doubled down on that strategy, deploying a number of new services based on the science, including a "deep learning-enabled wireless video camera that can run real-time computer vision models." 

Consumers are becoming increasingly enamored with Amazon's Echo smart speaker, which has evolved from a single device to a full line of products, powered by its AI-infused Alexa digital assistant. During the recent holiday sale that concluded on Cyber Monday, the Echo Dot was the best-selling product worldwide on Amazon's website. The company is tight-lipped about how exactly many have been sold. Consumer Intelligence Research Partners estimates that Amazon commands a 76% market share, with an installed base of 15 million Echo devices in the U.S. RBC Capital markets estimates that the combination of device sales and voice-activated shopping on its website could generate an additional $10 billion in sales by 2020. 

That may just be the tip of the iceberg, as Amazon recently expanded Echo's market potential by introducing Alexa for Business. At the company's re:Invent conference, Amazon announced a number tools and capabilities specifically designed to facilitate business uses.

A minivan outfitted with self-driving technology.

Google's Waymo is the widely acknowledged leader in self-driving technology. Image source: Waymo.


No discussion of deep learning would be complete without including Google, one of the early pioneers of deep learning and AI research. As far back as 2011, the company began work on a neural network that would eventually be called the Google Brain. Noted deep-learning researcher and Stanford adjunct professor Andrew Ng began collaborating with Google scientists, who had their first major breakthrough in 2012.

By connecting 16,000 processors, and feeding the system more than 10 million random images culled from YouTube videos, the system learned to recognize images of cats. This seemingly innocuous bit of image recognition has led to other advances in the areas of language translation, voice recognition, and computer vision.

Google acquired deep-learning start-up DeepMind in 2014, and the company has gone on to make headlines. Its AlphaGo computer was able to defeat the world champion at the ancient Chinese game of Go, one of the most difficult and sophisticated games ever played. DeepMind technology was used to arrange Google servers more efficiently and to improve energy efficiency by reducing the power necessary to run them. These efforts resulted in a 15% reduction in power usage, which saved hundreds of millions of dollars.

Google generates most of its revenue from advertising, which grew to $27 billion, up 24% year over year in its 2017 third quarter. The company was able to design a deep-learning system to more accurately match advertising with users by better predicting the click-through rate. Due to the scale of its operations, even small, incremental improvements can result in gains of hundreds of millions of dollars in additional revenue.

One of the most game-changing applications of deep learning is in the field of autonomous driving. Google began its research in the field back in 2009, which resulted in its self-driving car unit Waymo. The company is widely regarded as the leader in the field, and Waymo recently removed the drivers from its pilot program shuttling consumers around Phoenix, Arizona, achieving full autonomy. When testing completes and a full deployment begins, the company's lead in the space could be insurmountable. Analysts from Morgan Stanley estimate that the business generated by Waymo could conservatively be worth $70 billion. 

Final thoughts

Because deep learning can be deployed across so many facets of a company's business, it is often difficult to quantify the revenue generated and the savings resulting from its use. There is no denying, though, the transformational nature of the technology. Each company listed above has established an early foothold and will likely benefit from these advances for years to come. This provides investors a good starting point.

This article represents the opinion of the writer, who may disagree with the “official” recommendation position of a Motley Fool premium advisory service. We’re motley! Questioning an investing thesis -- even one of our own -- helps us all think critically about investing and make decisions that help us become smarter, happier, and richer.