There is often confusion between artificial intelligence (AI) and machine learning (ML), but there are differences.
AI, for example, is a broad description of the category of technologies that allow for the simulation of human-like capabilities in machines.
Machine learning, meanwhile, is a subset of AI. It generally involves the processing of large amounts of data, which is then applied to algorithms. By doing this, ML makes it possible for a computer system to recognize objects, predict when a machine will fail, or even drive a car. In other words, it allows systems to learn and make choices with little human interaction.
Consider that machine learning is not new. The roots of this technology go back to the 1950s, when it was developed to help with such things as playing chess.
But it was not until the past decade that machine learning has become transformative. Some of the reasons for this include the development of new theories like deep learning – which is a subset of ML – as well as the explosion of data and the growth in cloud computing.
So how big is this opportunity for machine learning stocks?
It's definitely massive. According to International Data Corporation (IDC), worldwide spending on AI technologies is expected to see a five-year compound annual growth rate (CAGR) of 17.4% by 2024, with revenues hitting $554.3 billion.
That said, here are five machine learning stocks that could benefit from substantial growth in the global AI market.
Data is as of June 3.
- Market value: $1.6 trillion
- Year-to-date performance: 34.0%
Machine learning has been a core part of focus for Alphabet (GOOGL, $2,347.58) since its early days. Keep in mind that the original PageRank – which made it possible to effectively search web pages at scale – was based on sophisticated algorithms.
But GOOGL's investments in machine learning have accelerated during the past decade. Alphabet has retooled its infrastructure, hired thousands of data scientists and struck a myriad of acquisitions.
In 2017, Alphabet CEO Sundar Pichai said that the tech giant's investments in machine learning were "fueling innovations across Google," and that he was happy with how they were transitioning to an "AI-first company."
The technology has been critical for many of its applications, such as for optimizing ad targeting, powering the language translation system and allowing for Google Assistant. Machine learning has also become a key to building its cloud platform.
Alphabet created one of the first development platforms for AI, called TensorFlow. The company open-sourced the software library for machine learning in 2015, which helped to make it a global standard. Some of its marquee customers include Intel (INTC), General Electric (GE) and Coca-Cola (KO).
Of course, Alphabet is one of the leaders in the development of self-driving cars, TOO. At the heart of this is its Waymo division, which raised $3 billion in capital last year. The buzz is that this division of GOOGL will be spun off in an initial public offering (IPO) within the next year or two, which could be a nice value driver for the company.
It's certainly worthwhile to keep an eye on this machine learning stock moving forward.
- Market value: $422.9 billion
- Year-to-date performance: 30.0%
Founded in 1993, Nvidia (NVDA, $678.79) is the pioneer of GPUs (graphics processing units) for more powerful gaming experiences. This was made possible by sophisticated parallel processing of large amounts of data.
Yet GPUs have become the main computer platforms for data scientists to create machine learning models. As a result, Nvidia has seen significant growth in its data center business.
Note that its A100 chip has become a must-have for hyperscale and major cloud customers. It allows for both AI training and inference at high speeds – though market adoption is still in the early stages.
Now, it's true that NVDA's self-driving car division has seen choppy growth, but the company does have an advanced platform that is starting to get buy-in from large customers like Nio (NIO), SAIC (SAIC), Li Auto (LI), Zoox, Mercedes-Benz and Xpeng (XPEV).
To bolster its dominant position with AI-based chip systems, Nvidia has made a bold play to acquire U.K. chip designer Arm for $40 billon. This will help NVDA penetrate categories like the Internet of Things (IoT), smartphones and edge computing.
Then there was the purchase of Mellanox, which is a developer of advanced networking systems.The M&A deal will help to boost Nvidia's data center business.
As a result, the machine learning stock's growth has remained impressive. In the latest quarter, NVDA revenues spiked 84% year-over-year to a record $5.7 billion, while adjusted earnings more than doubled to arrive at $3.66 per share.
- Market value: $70.6 billion
- Year-to-date performance: -15.3%
Quality data is essential for effective machine learning, but this is not an easy process.
For large enterprises, data is fragmented across silos. Additionally, there are the nagging issues of cleaning up data sets, which, for the most part, are usually unstructured. What's more, traditional databases – such as those from Oracle (ORCL) – were not built for machine learning use cases, which are usually quite costly.
So what to do?
Well, the cloud has been a way to help mitigate these problems, and one of the leaders in the category is Snowflake (SNOW, $238.43). The company has built a cloud-native platform that makes it easy to spin-up databases. There are also the advantages of seemingly endless scale, a large number of integrations and built-in systems for machine learning.
BlackRock (BLK), which is one of the world's largest money managers, is a customer of Snowflake. The firm has a system called Aladdin, which is used to help predict and optimize portfolios. SNOW has also been important for integrating non-Aladdin data sources, which has allowed for notable increases in performance and investment results.
And yes, it's true that the machine learning stock has struggled on the charts this year. However, off the charts, Snowflake is one of the fastest-growing enterprise software companies.
In its first quarter, product revenues shot up by 110% year-over-year and the net revenue retention rate was up an impressive 168%. There are also 104 SNOW customers that generate revenues of more than $1 million on an annual basis.
- Market value: $5.9 billion
- Year-to-date performance: -20.9%
Founded in 2015, Lemonade (LMND, $96.88) is an insurance company that has been built on a machine learning foundation. The company currently offers policies for homeowners, renters, pets and life insurance.
Lemonade has three main parts. There is AI Maya, which is a virtual assistant that collects information from customers, provides quotes and manages payments.
Then there is AI Jim, which is a bot that handles insurance claims, and has been able to completely automate a third of them. And for those claims that need a human, the process is much easier since AI Jim has done much of the heavy lifting.
Finally, Lemonade has CX.AI. This is a system to handle routine customer questions.
With these technologies, Lemonade has gotten lots of traction with younger generations. This is certainly a tough market to reach – but can be critical for long-term growth.
It's true that this machine learning stock is not cheap, with the valuation at a hefty $5.9 billion, but the market opportunity is massive. After all, Lemonade is now moving into the lucrative auto insurance segment, which is estimated to bring in about $300 billion in premiums in the U.S. this year.
- Market value: $177.7 billion
- Year-to-date performance: 7.1%
While more and more companies have been investing in machine learning projects, the results have often been far from encouraging. It is common for these ideas to not extend beyond the proof-of-concept stage for several reasons, including the complexities of algorithms, the challenges with data and the issues with recruiting data scientists.
Because of this, companies will rely on the help of consulting firms – and one of the leaders in this market is Accenture (ACN, $279.63). The company has a thriving AI practice and it has become a major source of growth.
The company's scale is certainly a major factor in its leadership, as Accenture has a workforce of 537,000 and operations that span the globe. The firm also has experience across most industries.
As an example of how ACN improves machine learning capabilities for companies, it was tapped by U.K.-based telecommunications firm Vodafone (VOD) to help boost customer service.
Accenture developed a system to route customer calls to the most appropriate channels to handle their issues. It also works to predict when customers are most likely to call and will send out proactive messages to try and address concerns ahead of time. This has helped to reduce VOD's inbound calls by 1.5 million and increase digital channel usage by 26%
While Accenture did experience a slowdown in growth during the COVID-19 pandemic, the firm has been able to get back on track.
In the latest quarter, revenues increased by 8% year-over-year to $12.1 billion and adjusted profits rose 10% to $2.03 per share. And the current quarter is expected to see revenue growth of 10% to 13%.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.