,

Machine Learning Statistics 2022: Market Size & Industry Growth

Last Updated: May 15, 2022

Let’s get into this machine learning statistics article.
The [year] List of Machine Learning Statistics
EarthWeb is reader-supported. When you buy through links on our site, we may earn an affiliate commission.

Machine learning, also referred to as ML, allows computers to recognize patterns and make predictions based on how we use data to make adjustments with no extra or specific programming. 

ML is becoming increasingly popular, especially in the industrial and academic markets. These industries are developing and introducing several new and innovative products.

Due to this, stakeholders are talking about machine learning a lot. 

Some enterprise software and business intelligence tools are now equipped with machine learning algorithms designed to predict patterns and trends coming in the future.

This process is opening opportunities for data analysts by allowing them to locate some hidden insights and discover subtle variations and patterns in big data sets. 

The following information will address an important list of machine learning statistics you need to know to make this easier to understand why this is becoming such a prevalent subject. 

Let’s get into this guide for machine learning statistics in 2022.

Contents show

Key Machine Learning Statistics 2022

  • 48% of businesses are using machine learning, deep learning, data analysis, and natural language processing to effectively make use of large data sets. 
  • Security is naturally a priority for any business. About 25% of I.T. professionals want to use ML for this purpose.
  • The A.I. hardware market valuation is expected to climb up to $87.68 billion by 2026.
  • The COVID pandemic has created a reduction of 12% in A.I. chip-making companies. 
  • It’s estimated that the American deep learning and machine learning markets will reach a value of $80 million by 2025.
  • 91.5% of top companies are currently investing in artificial intelligence.
  • The use of A.I. has increased business productivity by about 54%.
  • 75% of all artificial intelligence projects are personally overseen by C-level executives. 
  • Tesla autonomous vehicles had traveled over 1.88 billion miles by the fourth quarter of 2019.
  • The use of voice assistance when up by 5% for multiple uses over six months.

Detailed Machine Learning Statistics 2022

1. 48% of Businesses Are Using Machine Learning, Deep Learning, Data Analysis, and Natural Language Processing to Effectively Make Use of Large Data Sets. 

One in every three I.T. professionals is using this technology. Today, machine learning in the business world is becoming an essential part of evaluating data.

Modern businesses are generating many terabytes of data every second.

That’s one reason many software programs are now equipped with machine learning algorithms.

It does help individuals to better understand what particular data means to a business. 

2. Security Is Naturally a Priority for Any Business. About 25% of I.T. Professionals Want to Use Ml for This Purpose.

Furthermore, 16% say machine learning is effective and efficient for the marketing and sales realm. 

While the rise in smart devices (computers, smartphones, tablets, etc.) is a good thing for the business world, security issues are becoming a higher priority due to how hackers are constantly seeking new ways to infiltrate new tech.

Machine learning algorithms seem to be a good answer to the growing concerns of security. 

As for marketing and sales, companies are using ML algorithms more for things like targeted marketing, which so far has shown to be more efficient over conventional, blanket advertising methods. 

3. Machine Learning Is Not yet Showing Much by Way of Cost Decreases, but It Is Showing Revenue Increases with 80% of Surveyed Respondents Reporting how Artificial Intelligence (A.I.)  and Machine Learning (ML) Are Boosting Revenues. 

You would think that with ML, there would be some decrease in costs, but it seems that instead, it’s increasing revenues. 

Though this is not a particularly bad thing, most I.T. professionals and business people are perplexed that these two data factors aren’t happening at the same time. 

So, perhaps the boost in revenues makes up for the lack of cost decreases. 

What is great news is the fact that 80% of companies using ML are saying that it’s helping to increase their revenue.

Machine Learning and Market Valuation 2022

A.I.

4. The A.I. Hardware Market Valuation Is Expected to Climb up To $87.68 Billion by 2026.

This is expected due to the current and forecasted compound annual growth rate (CAGR) of 37.60 percent between 2019 and 2026. 

Many people think A.I. is only software, but the hardware components are also just as important.

Today’s A.I. programs are heavily reliant upon computing power. This is one reason that A.I. hardware is expected to be more relevant in the coming years.

Right now, most A.I. programs are used in factory machines, chatbots, etc. 

5. The COVID Pandemic Has Created a Reduction of 12% in A.I. Chip-Making Companies. 

Machine learning relies on specific chips used in devices, but the worldwide pandemic has experienced a slow-down of the growth of ML chips.

The most recent numbers include a 13% decrease in all sales. There have been predictions of higher drops in the A.I. market. 

This is not surprising or unexpected since this has happened across many markets across the globe.

6. It’s Estimated that The American Deep Learning and Machine Learning Markets Will Reach a Value of $80 Million by 2025.

Both of these markets are expected to experience this growth by 2025. This is an enormous number, but it’s forecasted to go higher as more organizations begin utilizing machine learning algorithms to their benefit. 

You should know that deep learning is the most sophisticated ML algorithm currently developed. It’s being used now to enhance businesses. That trend is not expected to fade away any time soon.

Machine Learning Statistics In Leading Companies 2022

7. 91.5% of Top Companies Are Currently Investing in Artificial Intelligence.

Business A.I. investing is becoming more widespread every single day. Over 91% of leading organizations are currently involved in this area of investing. Also, the number of businesses getting involved in A.I. investing is growing each year.

The upside to ML algorithms has been gaining traction and getting the attention of many investors from top companies. The reason for this is that they are starting to recognize its benefits. 

8. The Use of A.I. Has Increased Business Productivity by About 54%.

Every business considers productivity a big priority. Machine learning statistics show that this kind of technology increases business productivity by up to 54%. 

So, you could say that ML helps workers in companies to be more efficient. As a result, the company experiences higher profits and revenue.

9. 75% of All Artificial Intelligence Projects Are Personally Overseen by C-Level Executives. 

According to machine learning statistics, people in the top roles of an organization (C-Level executives and those responsible for making decisions across the company) are personally overseeing 75% of all the artificial intelligence projects in their companies. 

Not too long ago, these top executives had no idea what ML even was much less what machine learning algorithms could mean to their organizations.

Due to a lot of hype regarding machine learning technology, this knowledge has been a game-changer for top-level executives. 

Machine Learning In Business Departments Statistics 2022

10. Tesla Autonomous Vehicles Had Traveled Over 1.88 Billion Miles by The Fourth Quarter of 2019.

Tesla is well-known for autonomous driving cars, so it’s fascinating to see how this kind of technology is working. Now that Tesla is a couple of years older, the 1.88 billion miles is likely a lot higher.

This means that there have been plenty of miles driven in autonomous cars already, so they have passed the testing period.

The potential for reducing the environmental impact of driving Tesla cars is only matched by the fact that they make driving safer. 

11. Since Artificial Intelligence and Customer Service Are Synonymous, Over 80% of Organizations Claim They Will Start Using A.I. in This Department.

Today’s customers are demanding when it comes to getting more value from business engagements, so companies need to adapt to keep up with competitors. 

Recent machine learning statistics tell us that 80% of companies are planning to use A.I. at some point in the near future for their customer service departments. 

This technology is believed to improve customer experiences through automation and faster response. 

12. Less than 15% of All Organizations Plan to Use Artificial Intelligence in Widespread [production.

Only a small percentage of businesses seem to want to use A.I. technology in their businesses. This may seem like a low percentage, but considering that ML is so new, it’s not surprising. 

As machine learning and artificial intelligence blossoms, more businesses are likely to start using it. 

Voice Assistants and Machine Learning Statistics 2022

13. the Use of Voice Assistance when Up by 5% for Multiple Uses Over Six Months.

Just a few years back, people didn’t think much about voice assistance, nor did they have much use for it. The most it was used was once per day. 

Now, a group of survey respondents says they have started using it more often with an increase of 5% over six months. 

As opposed to depending on conventional methods of engagement with products, different organizations should be using more voice assistants.

Businesses should be utilizing machine learning that learns the behaviors of customers over time and will provide personalized services. 

14. Approximately 50% of People Across the Globe Use Voice Assistants. 

Using voice assistants seems to be growing quickly in households across the globe. As this tech gets more sophisticated and the market keeps evolving and growing, currently about half of the world’s population is using it. 

Using voice commands to make orders, turn off and on lights and appliances, and other tasks is perceived by most as convenient and useful. It’s seen as a technology that boosts productivity in the workplace on the home front. 

15. The Worldwide Covid Pandemic Created a 7% Rise in Artificial Intelligence Usage.

During the pandemic, it seems that A.I. usage went up by 7%. This data is vital for organizations considering implementing machine learning into their voice assistants. 

General Machine Learning Statistics 2022

Machine Learning

16. Almost Half of The 100,000 Jobs that Require Machine Learning Are in America. 

It literally pays to know about machine learning with nearly 100,000 jobs across the globe requiring it. These jobs can be found on LinkedIn.

Nearly half of the jobs are found in America.

This just proves that knowledge in this market is paying off for those with the right skills and education. This information can also help you decide what you want to do as a career. 

17. 62% of Survey Respondents Said They Would Send Their Usage Data to An A.I. Platform to Help  Improve the Customer Experience. 

Even though there are many people who still hold their privacy sacred, our machine learning statistics found that more than half of technology users don’t have an issue with sharing their data usage with A.I. technology. Perhaps they feel if it’s to better their lives, it’s okay.

18. Early Adopters of Machine Learning Enjoyed a 47% Improvement in Their Sales and Marketing. 

Businesses that adopt technology ahead of others lead the way and have a positive impact on how companies operate. 

Implementing machine learning into a company’s sales and marketing efforts show a 47% improvement according to those surveyed. Also, these companies enjoyed higher efficiency in services and products. 

FAQs 

What Is Machine Learning?

We briefly defined machine learning at the beginning of this article, but now we want to share a little more with our readers. 

Essentially, machine learning is a technology that improves as it’s used.

They can improve performance while offering access to big amounts of data, sophisticated algorithms, and computing power. 

This technology helps the systems on which they are used recognize patterns, which helps to improve the performance and can add or remove information without further programming.

The ML market is growing, evolving, and constantly improving. 

Machine learning methods are ideally used for various applications such as engineering, machine translation, scientific research, business applications, data mining, etc.

It’s believed that the demand in this job field is set to experience exponential growth in the coming years. 

What’s the Difference Between Machine Learning and Artificial Intelligence?

Machine learning is flexible, while artificial intelligence is adaptable. 

A.I. is considered a multiplying type of technology and is also expected to be the next big leap into technological advancements. 

In some ways, machine learning is a subset of A.I. since it allows the machine to learn to be better at specific tasks. 

How Does the Machine Learning Model Get Better?

Data science is one of the methods that make machine learning a thing. 

Data science is also a priority these days due to its potential for being beneficial in various aspects of life and business. 

In order to make enhancements and improvements in ML algorithms, models used for data science require access to all data to make accurate predictions.

Often, these models are relatively accurate in their predictions even when they get information that isn’t relevant to what was used for training them.

Machine learning models are usually trained with instances of input data and the proper interrelated outputs to make it happen. 

This model is able to audit the variations between predicted values and what was expected as it adjusts accordingly, so the predictions can come out more accurately next time.

Machine Learning Algorithms

The process of the machine learning algorithm is a step-by-step deal that utilizes input data to come up with predictions. 

These algorithms help make takes easier by taking some of the burdens off of employees and the company.

Computer science is what helps ML algorithms improve by offering up more computing power and access to greater data sets. 

These models appraise statistical fats to find patterns and extrapolate data and patterns into a new situation as data is received. 

Statistical Methods

Statistical methods are used in machine learning systems to help it understand complicated data relationships. 

In order for ML systems to work, they have to draw assumptions from the data to make more precise predictions for real-world scenarios.

The instances of statistical methods can include Bayesian analysis, clustering, linear regression analysis. This is how the model is able to understand how various elements can be rated. 

Statistical models are crucial in ML where the method takes prior examples to predict what will occur next. 

Exploratory Data Analysis

Training and testing are involved in creating predictive models from multiple samples in machine learning. 

Initially, the process looks at the data and then makes applicable assumptions about the nature of said data. 

Exploratory data analysis is one of these analysis types. It looks at the distribution of the features found in the data set. 

This data analysis won’t give you information that is certain about what to expect, but it does help make inferences about the model that could be the most effective in forecasting the proper outputs. 

All machine learning models are required to go through the exploratory data analysis process whether predictions are needed for supervised or unsupervised learning. 

What Is the Importance of Machine Learning in This Career Path?

A background in statistical modeling is helpful for getting ahead in this career field.

That’s one reason that it’s important for job seekers to comprehend this technology.

Machine learning job skills is one of the best investments you’ll make in your education if you are interested in this career path. 

The focus on conventional jobs is now shifting from repetitive tasks to more complicated ones, so this skill set will be valuable to you.

You may require a college degree to enter into this career field. You can start by getting a bachelor’s degree in data science. 

There is a lot of potential and opportunity in this job. 

What Is Big Data’s Impact on ML?

One cannot talk about machine learning without bringing up the topic of big data and how it impacts this industry.

Machine learning is one of the biggest opportunities in the big data arena, which is important since the amount of data created is predicted to reach over 180 zettabytes by 2025.

Since the amount of data that will be created is set to increase, this career holds even more opportunities. 

Things like cross-validation, decision trees, and reinforcement learning can all profit from accessibility to large data sets. 

Overfitting is another concept that makes things better in this arena.

This is a concept that is part of data science, and it’s used as a classifier when gauging error rates and machine learning progress. 

Harvard and MIT currently offer data science certificates that you can look at.

More Machine Learning Factors to Consider

Supervised learning techniques and statistical learning is used by statisticians to aid in logistic regression along with other prediction modes.

These elements of ML have been advantageous to businesses by reducing the burden of employees who are now able to perform more complicated tasks in a business. 

Several tools can assist with the visualization of algorithms for viewing what’s behind the scenes. 

In the education sector, graduates who are studying machine learning applications with a focus on statistics are also learning data mining, the key measurements that define success in predictive models, and supervised and unsupervised learning. 

Therefore, machine learning is likely to affect industries like computer vision, finance, marketing healthcare, robotics, marketing research, and others. 

Conclusion

These machine learning statistics for 2022 that we gathered for our readers are intended to help you all understand better what machine learning is and how it impacts our lives. 

Basically, ML concentrates on enabling people to use it to make intelligent predictions based on the data input provided.

By utilizing various types of analyses, the ML models help companies understand their customers, build relationships, and make better decisions regarding the business. 

On the other hand, you can also use these same models in your own personal life for a variety of things. These models are helpful for the following:

  • Making decisions about how you should spend free time.
  • Advice for growing wealth by using custom tutorials designed with your skills in mind. 
  • Gain a better understanding of your fitness and health.
  • Getting recommendations for watching your next Netflix series. 

So, you should be able to see that the options are limitless, resulting in a fabulous future. 

These machine learning statistics for 2022 should be helpful for personal reasons, business purposes, and even for choosing a career path. 

You can use machine learning, deep learning, and A.I. in your personal life to make decisions, or to get advice or recommendations on various things. 

For business, you can better connect with your customers by gaining a deeper understanding of their needs and desires regarding the business’ specific offerings. 

If you’re looking for a career in the technology field, machine learning skills along with a data science degree and/or certificate from MIT or Harvard can give you the edge on this ground-breaking industry. 

Sources

Analytics InsightCIOCMS Wire
Data ProtExploding TopicsFinances Online
ForbesForbesFortune
G2 Learning HubMcKinsey and CompanyNASDAQ
Review 42SEM RushSerp Watch
True ListVoicebot

Written by Jason Wise

Hi! I’m Jason. I tend to gravitate towards business and technology topics, with a deep interest in social media, privacy and crypto. I enjoy testing and reviewing products, so you’ll see a lot of that by me here on EarthWeb.