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Adopting machine learning (ML) in business
1. Business analytics is an important area for the use of machine learning, with one in three IT managers wanting to use this technology.
(Statista)
The use of machine learning in businesses has become essential as they generate terabytes of data every second.
That's why numerous applications have emerged that can use machine learning algorithms to help people better understand what specific data means in business terms.
2. There doesn't seem to be a lot of cost savings when using ML. In contrast, revenue increases are widely reported, with 80% of respondents saying that AI and ML contribute to them.
(McKinsey)
One would expect that the use of machine learning algorithms would lead to a fundamental reduction in costs. Instead, the truth is that revenue has ended up increasing thanks to this technology.
While that's not bad, some might be surprised that the two factors don't happen simultaneously.
The good news is that 80% of organizations that have used machine learning say that the technology has helped increase their revenue.
3. Security is a major concern in any business, and 25% of IT managers want to use machine learning for this purpose. Additionally, 16% say that machine learning is great for marketing and sales.
(Statista)
Security is a top concern for any business, and the same goes for IT managers.
As machines get smarter, hackers are finding new ways to outsmart them.
However, it seems that machine learning algorithms can help solve this problem.
When it comes to marketing and sales, many businesses have started using ML algorithms for targeted marketing, which has proven to be much more effective than general advertising.
Machine Learning Market Assessment
4. By 2025, the American machine learning and deep learning market will reach $80 million.
(Statista)
The value of the American machine learning and deep learning market will exceed $80 million by 2025.
This number is already significant, but it is expected to increase even more as businesses start to exploit these algorithms to their advantage.
Deep learning is the most advanced machine learning algorithm possible. It is currently being used to improve businesses, and this trend shows no signs of slowing down.
5. A CAGR of 37.60% from 2019 to 2026 is expected in the AI hardware market, bringing the final valuation to $87.68 billion.
(WebSJ neighbor)
The compound annual growth rate (CAGR) of the computer hardware market is expected to be 37.60% every year for these seven years.
While most people associate AI with software, the hardware aspect is just as important.
Current applications of AI (ranging from chatbots to factory machines) rely heavily on computing power, and this hardware is expected to become even more important in the future.
6. COVID-19 was responsible for a 12% decrease in microchip manufacturing activity.
(Market Data Forecast, 2020)
The global pandemic has slowed the growth of machine learning chip manufacturing business to some extent.
With a 13% drop in overall sales, some could have predicted a bigger drop in the AI market than that.
At the same time, larger cuts have been observed in many other markets, so it is not surprising that some downturn has occurred.
machine learning in large organizations
7. The use of AI has increased the productivity of top-tier businesses by up to 54%.
(Oberlo)
Business productivity is an area that has a lot to gain from machine learning algorithms.
According to a study by Oberlo, the use of AI optimizes the productivity of high-level companies by up to 54%.
In other words, machine learning can be said to help employees in these organizations become more efficient, which leads to increased profits and increased revenue generation.
8. Senior managers personally oversee 75% of all IT projects in their organization.
(Wealth)
Those in the most senior positions (and responsible for enterprise-wide decision-making) personally oversee 75% of all AI projects in their organization.
In the past, many senior managers barely knew what machine learning was, let alone what was going on with machine learning algorithms in their organizations.
With all the hype surrounding this technology, the situation has changed dramatically.
Today's top managers can't afford to ignore what's going on with AI and machine learning.
9. A.I. Business Investment is an activity in which 91.5% of large companies are involved.
(Businesswire)
AI investment software is becoming more and more popular every day.
According to a BusinessWire study, over 91% of large businesses are involved in this type of investment.
Even more impressive, the number of organizations seems to be increasing every year.
Many investors in major companies can see the benefits of machine learning algorithms, and they are allocating more resources to this emerging technology.
Machine learning in different departments
10. Customer service and IT go hand in hand - over 80% of businesses will use IT in this area.
(B2C)
With customers demanding more value from every business interaction, it's clear that businesses will need to adapt if they want to stay ahead of the curve.
Over 80% of businesses plan to eventually use AI in customer service, allowing them to provide better service and deliver a top-notch product that customers will appreciate.
AI can also help businesses improve the customer experience by automating aspects of this process.
The way businesses interact with their customers is changing, and machine learning algorithms seem to be an important part of that evolution.
11. Less than 15% of all businesses said they would use AI in widespread production.
(Businesswire)
When it comes to manufactured products, only a small percentage of businesses in the world will apparently use AI.
Although this figure seems relatively low, it is still higher than expected at this stage, especially considering that the machine learning industry is a fairly recent development.
12. In the fourth quarter of 2019, Tesla had driven more than 1.88 billion kilometers independently.
(Forbes)
Since Tesla is well known for its self-driving cars, it is interesting to see the global approach of machine learning applied to this sector.
According to a Forbes study, Tesla had driven more than 1.88 billion kilometers independently by the end of 2019.
This statistic, which dates from a few years ago, is all the more impressive as several years have passed since it was recorded.
The good news is that with so many kilometers already driven, these autonomous vehicles have been tested on a large scale.
All of this means that cars produced by Tesla are reducing the impact on the environment and, hopefully, making our roads safer.
Machine learning in voice assistants
13. No less than 50% of the world's population uses voice assistants.
(Review 42)
Voice assistants are becoming more and more well known and integrated into many homes.
As this technology becomes more advanced and the voice assistant market continues to grow, it seems that half of the world is already using a voice assistant.
Voice commands are becoming a popular choice for many people who want to improve productivity in various areas of their lives.
Companies that have bet on artificial intelligence are slowly starting to see the fruits of their efforts.
Surely those who have invested in voice assistants fall into this type of category.
14. The global COVID-19 pandemic has increased the use of vocal AI by 7%.
(AUM)
The global pandemic also seems to have had an impact on the use of voice assistants.
According to an AUM study, the COVID-19 pandemic has increased the use of voice AI by 7%.
This data is crucial for businesses that are hesitant to incorporate machine learning into their voice assistants (and to take voice assistance capabilities to the next level), as demand in this area is only expected to increase.
15. The use of voice assistance several times per day has increased by 5% in six months.
(Voicebot.ai)
In the recent past, many people did not seem to find much use for voice assistance in their daily lives, using it only once a day.
However, interviewees said that they started using it more over time, with an increase of 5% over a six-month period.
Instead of relying on traditional ways of interacting with products, various businesses need to incorporate voice assistants into their business plans.
Businesses should ideally do this through machine learning that adapts to customer behavior and needs over time to offer personalized service.
Other interesting machine learning statistics
16. For early adopters, machine learning improved 47% of their sales and marketing efforts.
(Deloitte)
In the business world, being at the forefront of technology can have a significant impact on how your business operates.
By implementing machine learning in their sales and marketing, 47% of businesses surveyed saw an increase in the number of customers and in the efficiency with which they delivered their products and services.
17. There are nearly 100,000 jobs in the world that require machine learning. Nearly half of these are in the United States.
(Forbes)
Knowing how machine learning works pays off — literally.
Nearly 100,000 jobs around the world that require machine learning knowledge can be found on LinkedIn.
Nearly half of these jobs are based in the United States.
This shows that it is crucial to have knowledge in this area, as having the correct information can help you with the various career paths you want to follow.
18. 62% of customers surveyed have no problem sending their usage data to an artificial intelligence platform to improve machine learning and, ultimately, the customer experience.
(Salesforce)
While privacy concerns are still present in many people's minds, it seems that they are ready to give up a portion of their privacy to improve various aspects of their lives.
62% of customers surveyed said they had no problem sending their usage data to an artificial intelligence platform to improve its machine learning algorithms.
As long as the company improves the final product based on this data, customers agree to share it.
What is machine learning?
Machine learning models are self-improving models that improve as they are run.
Their performance improves with knowledge, and their improvement can be accelerated by giving them access to large amounts of data, computing power, and advanced algorithms.
Machine learning models are very good at recognizing patterns, even when presented with a data set that contains noise or missing information.
LMe machine learning also excels at extrapolating from small amounts of input data - for example, generating high-quality recommendations in the absence of comprehensive user profiles.
The global machine learning market has continued to grow and will continue to grow over the next few years.
The good news is that machine learning methods are used in a variety of professional situations, including scientific research, engineering applications, machine translation, data mining, and more.
Most people also think that job growth and demand for this particular skill set will continue to increase over the next few years.
The difference between artificial intelligence and machine learning
While a machine learning model is very flexible in what it can do, an artificial intelligence model is more adaptable.
Artificial intelligence (AI) is a form of technology that is multiplying - many consider AI to be the next major technological advance.
In a way, an ML model is a subset of artificial intelligence because it allows a machine to learn how to improve at a specific task.
How a machine learning model improves
Data science is one of the main ways of machine learning.
An indispensable field in our time, data science has numerous advantages that can help make life easier for everyone.
To improve machine learning algorithms, models used for data science need to have access to all available data to make predictions.
As a result, these models often end up predicting things accurately, even if they received information that was not part of what was used to train them.
To do this, machine learning models are trained using sample input data and the correct corresponding outputs.
The model can look at the difference between the values it predicted and those expected and will adjust accordingly so that its predictions are more accurate in the future.
Machine learning algorithms
A machine learning algorithm is a step-by-step procedure that uses input data to make predictions.
Machine learning algorithms can help make your work easier by relieving you of some of the burden on you or your business.
Computing helps machine learning algorithms improve by providing them with greater computing power and access to larger data sets.
These models evaluate statistical properties to identify models that are extrapolated to new situations as they receive this data.
Statistical methods
They are used in a machine learning system to help it better understand complex relationships in data.
For a machine learning system to work, it needs to draw conclusions from the input data to make accurate predictions in real world scenarios.
Examples of statistical methods include linear regression analysis, Bayesian analysis, and clustering.
These allow a model to better understand how different factors relate to each other.
Statistical models are also crucial in a machine learning method that uses previous examples to predict what will happen next.
Exploratory data analysis
Machine learning techniques involve training and testing to develop a predictive model using multiple samples.
The first step in this process is to examine data and make some assumptions about what it is.
One type of this analysis is called exploratory data analysis, which looks at the distribution of characteristics in your input data set.
This doesn't tell you anything definitive about what you can expect in the future, but it can help you make inferences about what type of model is most effective at predicting good outcomes.
Whether these predictions relate to supervised or unsupervised learning, all machine learning models require an exploratory data analysis stage at one time or another during the development of predictive and linear models.
The importance of machine learning in various workplaces
Having a basic knowledge of statistical modeling can help you make progress in the world of machine learning, which is why it is so important for job seekers to understand what this field is all about.
Because in many traditional jobs, repetitive tasks give way to tasks that are relatively more complex, this skill set is one of the best learning investments you can make.
In some cases, this may require a university education, so if this is an area that interests you, it may be beneficial for you to start by earning your bachelor's degree in data science.
This field has great potential and can prepare you for a future in a wide variety of different jobs.
Big Data and its impact on machine learning
I can't talk about machine learning without mentioning big data and the impact it has had on the field.
The amount of data created that is expected to reach more than 180 zettabytes by 2025, machine learning is one of the most promising applications of Big Data.
With the increase in data sets that make them more important than ever, there are plenty of opportunities for people interested in this fast-growing field.
Things like decision trees, cross-validation, and reinforcement learning all benefit from the availability of large, high-quality data sets.
Another concept that helps improve things in this area is called “overfitting.”
This concept in data science is used as a classifier when measuring the error rate and makes it possible to advance machine learning.
With universities like MIT and Harvard now offering data science certificates, this is the field that many students are pursuing.
Other machine learning considerations
Statisticians use statistical learning and supervised learning techniques to facilitate logistic regression and other prediction models.
These aspects of machine learning have benefited businesses by easing the burden on their employees, who are now free to take on more complex tasks that get things done in their business.
In addition, the low barrier to entry in computer languages such as Python helps learners and those who study neural networks to start their learning projects.
Numerous tools allow you to visualize algorithms in order to see what is happening behind the scenes.
In the education sector, graduates who follow statistics-based machine learning programs can become familiar with data mining, supervised and unsupervised learning, and the key metrics that define the success of prediction models.
All of this means that machine learning will impact sectors such as healthcare, computer vision, marketing, finance, marketing research, robotics, and more.
Summary.
The machine learning statistics above can help you better understand what machine learning is and how it impacts our lives.
In a nutshell, machine learning aims to allow people who use it to make intelligent predictions based on input data.
Through a variety of analyses, these models can help businesses better understand their customers and make more accurate decisions.
On the other hand, you can use these models in your personal life in a variety of situations.
They are beneficial in situations such as:
- Get advice on how to increase your wealth using a personalized tutorial created according to your skill level.
- Have a better understanding of your health and fitness
- Get suggestions for your next Netflix series based on your past choices
- Make decisions about how to spend your free time
As you can see, the possibilities are endless and the future is fascinating.
What do you think of this technology?
How do you think you will be able to use machine learning shortly?
Let me know your thoughts in the comments below.
Further reading: There are tons of uses for machine learning in businesses.
These include ERP software, of database software and even software from sales management.
If you want to understand the latest developments in the areas mentioned, the articles above are definitely worth consulting.
