Keeping up with technology trends can be exhausting and confusing. Many times, the terms used to describe technology investing opportunities aren't always defined and can leave investors with more questions than answers.
For example, there's a lot of talk about artificial intelligence, machine learning, and deep learning these days, and their potential for investors. So let's take a quick look at how NVIDIA Corporation (NVDA -2.48%), a graphics process maker with a leadership position in these spaces, defines each of them -- and what the company's potential is in these businesses.
Image source: Getty Images.
What is artificial intelligence?
Artificial intelligence (AI) is sometimes thought of as the intelligence we see from robots in movies or television shows. That level of AI isn't possible yet, and instead, tech companies that are working on artificial intelligence right now are usually doing what's called "narrow AI."
According to NVIDIA, narrow is AI when hardware and software work together to perform very specific tasks as well as, or even better than, humans do.
An example of this is Facebook's ability to suggest which friends to tag when you upload a photo to its website. This narrow AI is able to look at the image, identify people with 98% accuracy, and do it faster than humans can.
So when you think of artificial intelligence, remember that it's not the general AI we all usually think of, but rather computers doing very specific tasks.
What is machine learning?
Machine learning is another buzz phrase that's gained a lot of attention in the tech sector lately. Amazon.com CEO Jeff Bezos has told investors that they should "watch this space," and nearly every major tech company is focused on using this type of technology to make its businesses better. But what is it?
NVIDIA defines machine learning like this: "Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world."
Instead of preprogramming software to complete a specific task, as narrow AI does, machine learning uses algorithms that allow a computer to learn from the vast amounts of data it receives so it can complete a task on its own.
Intel puts it this way: "Machine learning is this idea of marrying algorithms and statistics" so that the machine can learn from new data.
NVIDIA uses machine learning to allow sensors, cameras, and computers to process the images they see and create a type of computer vision.
The hardware and software work together to understand what the edge of a road sign looks like, what letters of the alphabet are, and how to identify where an object begins and where it ends. When all of these algorithms work together and learn based on what they see, they identify things like stop signs for driverless car technology.
What is deep learning?
And last, but not least, is deep learning. This is where things can get a bit more difficult to understand but bear with me.
In our brains are neural networks that connect to each other and help us process lots of seemingly unconnected information. By taking in bits of information and making logical connections between other bits of information, we begin to understand the world around us.
Deep learning computers have their own artificial neural networks that are physically stacked on top of each other so that they too can make connections. So when deep learning software is looking at pictures of cats, some layers may focus on colors, while others are determining shapes, and another layer will gather the results and try to determine if what the computer is seeing is indeed a cat, and maybe even what kind of cat it is.
For example, last year Google used its own deep learning system to look at 10 million images from YouTube videos and pick out the cats in each one. Google's deep learning software was about two times more accurate at doing that task than any other image recognition system that preceded it.
But it's not just images that deep learning is used for. International Business Machines uses deep learning powered by NVIDIA's graphics processing units (GPUs) to comb through medical images to find cancer cells. IBM says its deep learning networks can be trained to find the cancer cells in images in just hours and retrained in just seconds.
Why does all of this matter for investors?
The worldwide artificial intelligence software market is expected to be worth $59.8 billion by 2025. Additionally, machine learning as a service (MLaaS) is expected to be worth $19.86 billion, and deep learning will hit $16 billion by that same year.
Many tech companies, just like the ones mentioned above, are pursuing these markets at a rapid pace, but NVIDIA may be in one of the best positions to benefit.
The company makes the graphics processors that are integral in AI, machine learning, and deep learning, and lots of companies already look to NVIDIA's hardware to make their AI software a reality.
NVIDIA's datacenter business is where most of the company's AI and deep learning revenue comes from right now, and that segment only accounts for 18.6% of the company's total revenue at the moment. But the company is currently a leader in the graphics processing space, and it's likely that as AI, machine learning, and deep learning grow, NVIDIA will continue to benefit.
