When you hear machine learning, the first thing that comes to your mind has to be scary sci-fi robots. Or maybe Big Hero 6?
Well, robots are just there for the effect. Making them more “intelligent” is one of the applications of machine learning.
Scientists apply machine learning concepts to many industry segments today, from social media to space exploration. Just look at machine learning statistics. Most of the things you use today, from your work tools to some everyday gadgets, are powered by machine learning algorithms.
To put it in simple terms, machine learning is there to teach computers how “to think.”
To check how it’s working so far, dive into these incredible stats.
Top Machine Learning Statistics: Editor’s Pick
- Machine learning is a required skill for almost 45,000 jobs in the US listed on LinkedIn.
- The deep learning market will grow to about $935 million in the US in 2025.
- AI technologies and machine learning could create up to $33 trillion in value annually in 2025.
- Approximately 73% of surveyed organizations stated they plan to spend more on cybersecurity machine learning applications.
- Using a machine learning algorithm, Amazon has decreased “click to ship” time by 15 minutes.
- Tesla accumulated approximately 780 million miles of driving data according to the last accessible information.
Foundations and Trends in Machine Learning
Automation of processes has been a goal of the business world for some time now.
With the development of machine learning algorithms and programs, this has become achievable much faster. Lately, we’ve seen its practical adoption by many industries.
The newest machine learning trends show that we develop hundreds of new algorithms every year. Imagine that amount of code and its practical applications in our everyday life.
But how did it all start? What direction is it going toward?
1. The CDC’s supercomputer 7600, the fastest supercomputer in 1975, cost $5 million at the time (about $32 million in today’s money).
According to the McKinsey Global Institute report, iPhone 4 was already in the same range as the mentioned computer. They had the same performance characteristics, with only one key difference. iPhone 4 cost only $400.
2. The two fastest-growing artificial intelligence platforms in 2018 were Microsoft and Amazon Web Services.
IDC’s stats from 2018 show that these two giants have experienced the highest growth rate. To illustrate, their 2017–2018 year-on-year expansion amounted to 139.4% (Microsoft) and 106.3% (Amazon Web Services).
Despite the rapid development of these two companies, IBM kept the market domination with a 9.3% share and $240.6 million in revenue in 2018, AI and machine learning trends show.
3. The average salary for statisticians in the US in 2020 was $93,290.
The profession will reach a 33% growth rate until 2030, according to the US Bureau of Labor Statistics.
This proliferation was essentially driven by the growing need for machine learning professionals. Given the importance of probability and statistics for machine learning, the job outlook for statisticians is much more encouraging than the average for other occupations.
4. Machine learning is a required skill for almost 45,000 jobs in the US listed on LinkedIn.
Further, the number of US jobs with machine learning criteria on this network is over188,000.
Machine learning is simplifying many business processes today, hence the increased demand for people with ML expertise. Their primary job is to leverage big data and create structured models that will simplify processes.
Machine Learning Statistics You Need to Know
Machine learning programs based on computing methods have evolved significantly over the years.
Today, we have a vast field of ML experts, data scientists, and engineers creating software deployed to dramatically change every aspect of our lives.
5. The average annual salary for computer scientists in the US was $126,830 in 2020.
With growth projections of 22% by 2030, this seems to be the field to be in. Judging by machine learning trends in 2022, the number of computer scientists is already growing. Data science statistics confirm that the need for this profession will have an upward trajectory.
The average salary for a machine learning engineer in 2019 was $142,858.
Despite their profession being a relatively new trend in the ML field, deep learning engineers ranked right behind.
6. In Q1 of 2019, organizations invested more than $28.5 billion in machine learning applications.
Compared to all other AI applications, machine learning has overshadowed its peers by the size of the investment. For instance, the accumulated amount of investment for both machine learning platforms and applications was around $43 billion, while other AI programs racked up $39.5 billion. Total investment in AI-powered solutions in the first quarter was $82.4 billion.
7. The deep learning market will grow to about $935 million in the US in 2025.
The expansion of the DL market in the US will increase nine times according to the deep learning statistics. For reference, the size of the market in 2018 was around $100 million.
So, global deep learning market growth predictions hover around $10.2 billion by 2025, with an estimated CAGR of 52.1%.
8. The neural networks market will reach $296 million by 2024 globally.
According to the Markets and Markets report, the global neural networks market will flourish by 2024, growing at a 20.5% CAGR. For instance, the size of the market in 2019 was $117 million.
The largest market size, based on these estimations in this period, will be North America, while the APAC region will have the uppermost CAGR.
9. The most popular use for machine learning in 2020 according to the machine learning statistics was reducing company costs (38%).
As indicated by a recent survey, reducing company costs was a key driver for ML in companies with over 10,000 employees.
The second on the list was generating customer intelligence with 37%, while number three on the list was improving customer experience, with 34%.
10. The key challenge to deploying machine learning for companies in 2021 was IT governance and security, with a 56% score.
As reported by Statista, this became the fundamental challenge for companies in 2021, when in 2020 reproducibility in models took precedence.
Further, the second most prominent challenge marked by the respondents was programming language and framework support at 49%. Overcoming this hurdle is important for more mature ML software solutions.
11. According to the newest machine learning trends, one-third of IT leaders showed they plan to use machine learning mostly for business analytics.
As stated in the report from 451 Research, advanced analytics with a core in machine learning principles are the key differentiator for companies.
In the second place, 25% of those surveyed listed security reasons. Finally, only 10% of IT leaders plan to apply machine learning in customer service.
Fascinating AI & Machine Learning Facts and Figures
Science fiction or reality? One thing is certain, artificial intelligence was a major driver of the world’s transformation in the last 20 years. It will most definitely continue to be a crucial factor of change in the future.
Machine learning as a branch of AI introduced the idea of teaching computers to use data and “learn.” When it comes to machine learning, amazing facts show the significance of this method for building more complex AI.
12. AI technologies and machine learning have the potential to create up to $33 trillion in value annually in 2025.
These estimations were based on the data from the McKinsey Global Institute’s Report 2013. According to the report, companies that embrace digital transformation will find a faster solution to their business challenges.
Take, for example, advanced robotics. The analysis shows that it will help decrease up to $6.3 trillion in labor costs.
13. By 2022, artificial intelligence will add $3.9 trillion of business value.
According to artificial intelligence statistics, global AI-derived business value will reach nearly $3.9 trillion by 2022. It will shape more than one field of business operations, from failure recognition to predictive analytics. Moreover, it will enable IT to perform faster, especially in growing infrastructure without the headcount increase.
14. One in 10 companies is using 10 and more AI-powered solutions.
Fun facts about machine learning in 2019 and findings from The State of 2019 AI Divergence report by MMC Ventures indicate that AI applications are automating the workflow of companies across industries a great deal.
In 2019, about 26% of respondents to the survey revealed that the most common use of AI solutions in their company was chatbots. The same percentage indicated that the process automation solutions were commonplace in their organization. Fraud analysis came in third with 21%.
15. The installation rate of cars powered by AI and machine learning systems will grow by 109% by 2025.
For instance, this rate in 2015 was just 8%, according to machine learning facts.
So-called driverless cars have been in the strategies of the automobile industry since the 1970s. With the proliferation of deep learning techniques and machine learning, the industry’s still exploring these ideas.
16. AI software and related technologies will push labor productivity to 40% by 2035.
Employing AI solutions will almost double economic growth in the same period, machine learning statistics suggest.
The biggest winner of this boost of AI on economic growth will be the United States, growing from 2.6% to 4.6% in the forecasted period.
Among other things, this technology will change the form in which workers process their tasks or interact with consumers.
17. By 2022, 70% of customer service will be conducted through AI.
A great deal of investments will be related to improving customer support. Different machine learning applications, like mobile messaging, will become part of 70% of customer interactions in this period. For reference, it’s a 15% increase compared to 2018.
Interesting Machine Learning Facts About Cybersecurity
18. 73% of surveyed organizations stated they plan to spend more on cybersecurity machine learning applications.
Numerous organizations are demonstrating the readiness to invest more in cybersecurity. Most of them consider it a vital part of their companies’ protection system against attacks in cyberspace.
Moreover, 34% of the organizations reported having suffered from a harmful cyber attack in the previous 12 months.
19. MIT’s AI2 machine learning software can identify and prevent 86% of cyber attacks.
MIT Computer Science and Artificial Intelligence Laboratory have developed this system to prevent cyber attacks by analyzing 10 million logs each day and diagnosing threats.
The program works in perfect symbiosis with a human analyst. Where a human can’t examine a massive volume of data, AI can, thus directing the analyst to the suspicious activity. This balance generated better results than solely relying on transformative technology.
Cool Stats and Facts About Machine Learning
20. In 2019, Visa safeguarded $25 billion from fraud by using machine learning algorithms.
Using the Visa Advanced Authorization tool, the company managed to timely pinpoint fraud patterns and react.
This AI tool powered by ML managed to go over 100% of the transactions in 2018, according to artificial intelligence statistics from Visa. It’s important to have in mind that they had over 127 billion transactions.
21. Machine learning solutions deployed by one US-based police department reduced the murder rate by 35% and robberies by 20%.
More and more government institutions are using machine learning-powered solutions for the automation of everyday tasks.
This police department automated the analytics which extracted the insights from the bulks of data. Thus it was possible to develop a forecasting mechanism that optimized the deployment of police units.
22. Beth Israel Deaconess Medical Center managed to free up 30% of operating room capacity using machine learning.
The adoption of digital tools in healthcare is one of the emerging trends in machine learning.
For instance, this hospital had a problem with OR capacity. Usually, they assign one hour for each patient’s surgery, but not all patients undergo ultra-complicated procedures. Using historical data, the machine learning algorithm sets the times for each surgery.
Additionally, the center used the same tech to predict no-shows and re-adjust the OR capacity.
23. The Aravind Eye Care System hospitals in India are using an AI algorithm that has trained itself on above 120,000 diabetic eye photos.
This eye hospital chain is using progressive technology to spot diabetic retinopathy on time. Their vehicles and rural hospitals are all equipped to scan the back of the patient’s eye. The system sends all the data collected for assessment to the clinic’s limited number of ophthalmologists in its HQ.
As a result, doctors can treat all cases faster, and diagnose the potential retinopathy on time.
For reference, their hospital has handled over 5% of all eye surgeries in the country by mid-2016. These are some cool machine learning facts.
24. Hong Kong’s Mass Transit Railway saves around $1 million annually due to the deployment of AI software for scheduling.
The important fact is that today, the MTR has a 99.9% on-time rate.
Since 2004, the benefits of AI for scheduling have been multiple. By increasing efficiency, it increased the company’s reliability. According to officials, in over 10 years, not a single planning and scheduling mistake has occurred.
The second benefit it provides the company is regarding the adaptability of their business processes.
25. Using a machine learning algorithm, Amazon has decreased “click to ship” time by 15 minutes.
Amazon’s purchased Kiva, the robotics company, in 2012. This fostered a huge improvement on its average “click to ship” time, according to the company’s statistics for machine learning.
To illustrate, implementing its special ML algorithm, warehouse picking, and packing time accelerated. Correspondingly, this change affected the average time to move to 15 minutes, signifying a 225% drop compared to previous years.
The previous human “click to ship” time, for example, ranged from 60 to 75 minutes.
26. According to Google statistics, voice search usage in 2016 increased 35 times compared to 2008.
The exponential growth of the voice recognition function would not be possible without the progress made in machine learning and particularly deep learning methods.
Thanks to deep learning and natural language processing techniques, voice queries became more accurate and accessible.
27. Tesla accumulated approximately 780 million miles of driving data according to the last accessible info.
These are some cool machine learning facts, especially if we consider that Tesla was a pioneer when they launched the Model S sedan with a constant cellular Internet connection in 2012.
In the same way, they achieved a revolution in 2014 by equipping their vehicles with new sensors.
The company began collecting data using these sensors. Typically, every hour Tesla collects one million miles worth of data.
28. Following trends in neural networks, Google used new machine translation to enhance Google Translate, decreasing its translation errors between 55% and 85%.
Marking it a milestone in the development of Google Translate, the company pointed out that with the use of neural machine translation, it will be able to consider an entire sentence and translate it. This will replace its previous model, where each word was translated separately, causing many mistakes.
29. Google’s Deep Learning program has 89% accuracy in detecting breast cancer, as shown by some machine learning applied statistics.
In 2017 Google released a study explaining how their state-of-the-art machine learning algorithm not only can diagnose metastasized breast cancer but also do it as well as the human pathologists.
Using large images of pathology slides, algorithms were able to recognize and mark cancer cells on nearby lymph nodes.
The level of accuracy the software has achieved looks even more astonishing. For instance, it diagnosed cancer with 89% accuracy, where the human doctor took 30 hours to reach 73%, AI statistics show.
Are We Moving Toward a Sci-Fi Scenario
So are robots going to steal our jobs and rule the world? The answer to this question is more complex than a simple yes or no.
Industry statistics about companies using machine learning today show an accelerated rate of implementations of ML and AI technology. Given the above statistics, it’s quite easy to say that they’re already taking over some parts of our lives.
Yet, humans will not become obsolete just like that, as we play an integral part in machine learning. Over 7 million jobs could indeed be displaced by AI and machine learning applications by 2037, according to some estimations. But at the same time, it will create 7.2 million jobs instead.
Ultimately, if we want to be ready for a robot-filled future, we’ll just need some serious upskilling.
Frequently Asked Questions
Why is machine learning important?
Without the propagation of the machine learning algorithms, companies wouldn’t be able to analyze ever-expanding amounts of data and put it to proper use.
Machine learning models have become must-use tools across industries, aiming to turn the vast amount of gathered data into quality results. Thus, companies gain a competitive advantage and can make insight-based decisions and improve their business operations.
Do you need statistics for machine learning?
Statistics is one of the main prerequisites for machine learning. Back at the starting point of ML, these two disciplines were often confused for each other. However, pitting machine learning vs traditional statistics is the wrong approach.
In the first place, machine learning engineers need solid knowledge of statistics to successfully transform large chunks of data into information that everyone can understand. This makes machine learning quite dependent on statistics.
What statistics is required for machine learning?
To better perform their tasks, every good machine learning practitioner needs to have strong knowledge of descriptive and inferential statistics. The first encompasses the methods used for summarizing raw data into comprehensive and coherent information. Without this, machine learning algorithms would lose their main purpose.
The second consists of various methods that help an engineer to quantify domain properties from a sample.
Is machine learning computer science or statistics?
The simple answer is that both disciplines are pretty intertwined. Namely, machine learning can’t be explained without both.
Machine learning is an integral part of computer science, but its roots are in data. Its main purpose is making predictions from raw data. Statistics is there to help you in the data processing methodology, and computer science will give it visualization, structure, and processing, and help you materialize the theoretical concepts.
What is a statistical model in machine learning?
Statistical modeling has been present for centuries. On the other hand, the machine learning concept first appeared at the end of the 20th century with the proliferation of computers and digitalization.
Despite the difference between statistics and machine learning in some segments, the latter is intertwined with the former. Both are looking to explain the data, but they use different approaches.
Machine learning doesn’t require assumptions on the correlation between the variables as long as the algorithm processes the data and provides patterns. Contrary to this, statistical modeling implies that we must understand how the data was collected and other similar properties.
What is interesting about machine learning?
The best machine learning tools are:
– Google Cloud AI Platform
– Azure Machine Learning
– Amazon Machine Learning
– IBM Machine Learning
– Neural Designers
How is machine learning different from statistics?
The difference between machine learning and statistics lies in three main factors: purpose, the way they look at data, and interpretability.
Statistics’ purpose is to make assumptions on the target based on a sample, while machine learning’s goal is to create predictions from patterns in data. For machine learning to do this, large amounts of data are necessary to build prediction models. Statistics relies more on the connection between the data and the outcome variable, as well as how the data was collected.
Given the above machine learning statistics, both are important ingredients for the age we live in.
- Big Data Made Simple
- Finance Online
- Grand View Research
- Health IT Analytics
- Hitachi Solutions
- Markets and Markets
- MIT Technology Review
- MMC Ventures
- Search Engine Watch
- The Economist
- The Enterprisers Project
- The Enterprisers Project
- ZD Net