Machine Learning Algorithms - Top 5 Real Life Examples (2023)

Machine learning has brought about significant changes to the business processes of many companies. And we're not just talking about futuristic products like Siri and Amazon Echo or companies that allocate considerable budgets to R&D programs like Google and Microsoft. In fact, almost every Fortune 500 company is making more money and working more efficiently thanks to machine learning.

What is machine learning (ML)?

Machine Learning (ML) is a data analysis technique that enables an analytical system to learn while solving many similar problems. Machine learning is based on the idea that analytics systems can learn to spot patterns and make decisions with minimal human input.

Let's imagine there is a program that can analyze last week's weather, as well as readings from a thermometer, barometer, and anemometer to make a forecast. Ten years ago, they would have written an algorithm with lots of conditional (if) constructs to do this:

• If a strong wind is blowing, there are likely to be clouds.
• If there are clouds, it will be cloudy.
• If the temperature has dropped but is above freezing, it will rain; if it's below freezing, it's going to snow.

The programmer had to describe an incredible number of conditions for the code to be able to predict the change in weather. At best, a multivariate analysis of the data was used, but all regularities were manually specified, even in this case. And even if that program was called artificial intelligence, it was just an imitation.

On the other hand, machine learning allows the program to build cause and effect relationships independently. The AI ​​is given a task and learns to solve it. The computer can analyze indicators over several months or even years to determine what factors influenced climate change.

Here's another great example from Google's DeepMind:

Google's DeepMind taught a model to walk. The program received information from virtual receivers and its objective was to move the animated avatar from point A to point B. There were no instructions for this. The developers have just created an algorithm that the program learned. As a result, he was able to complete the task on his own.

The AI, like a child, tried different methods to find the one that best achieved the result. The characteristics of the physical models of the avatar were also taken into account, making the quadruped jump and the humanoid run. The AI ​​was also able to balance on moving slabs, navigate around obstacles, and navigate off-road.

What is machine learning for?

In the example above, the walk was described. This will help humanity create trainable robots that can adapt to the tasks at hand. For example, putting out fires, clearing debris, mining, etc. Machine learning is much more effective than a regular program in these cases. A human being can make a mistake while writing the code. And that can cause the robot to go into a stupor because it doesn't know how to interact with a rock in a way that the developer didn't prescribe.

Machine learning is widely used in data science. And a significant part of the time, those tasks are marketing.

Amazon uses AI with machine learning to offer users the product they are most likely to buy. To do this, the program analyzes the experiences of other users to apply them to new users. Still, the system has its drawbacks; For example, when a user buys a hat, he sees offers to buy more. The show will conclude that a few hundred more won't hurt, as a hat was needed.

Google uses a similar system to select relevant ads and has the same problem. It is enough to search for information about what kind of bicycles there are, and Google will decide that the user wants to delve into this topic.

You can also work with voice assistants like Siri. They use ML-based speech recognition systems. In the future, they will be able to replace secretaries and call center operators.

ML applications can be many different things. It can even help createquantum resistant blockchainIn the future. And you can use it in your applications. But you will need to buy, configure, and maintain a machine learning infrastructure.

Machine learning in the factory

Minimize production downtime.Due to breakdowns, malfunctions, or raw material shortages, downtime can cost a factory millions of dollars. Machine learning can help prevent them. This is done by collecting data from sensors on the computer and seeing which metrics fail. In the future, this information can be used to predict when and why downtime will occur and how to avoid it.

For example, it may be that the temperature in a store always rises before a machine breaks down. So if the temperature rises, the system will alert the engineers and they will prevent the problem in time.

To prevent downtime in mining operations, the oil and gas equipment manufacturerGE Oil and Gasuses the Industrial Internet of Things and machine learning. The company's platform collects data on the state of oil production and schedules diagnostic checks and helps identify failures before they happen. The same rig helped the Kuwait Oil Company increase gas production by 2-5%, and Malaysia's Petronas cut maintenance costs by 10%.

Creation of a production management system.With the help of sensors and machine learning, it is possible not only to perform restricted tasks (such as avoiding breakdowns), but also to manage the entire production:

• Reduce the percentage of rejected parts: Analyze why rejects occur and how to avoid them.
• Streamline individual steps so they take less time.
• Use less material for production and therefore reduce costs.
• Monitor the health of the equipment and record its efficiency and workload.
• Automate individual production steps.

Microcontroller maker Simatic uses a platform based on IoT and machine learning. It helps to collect and analyze information from sensors on equipment in real time. It helped automate the production of thousands of products by 75%, increased production by 9 times with the same physical space and personnel, and reduced waste by almost 100%.

Identification of security threats.Machine learning helps make production safer by identifying small changes in equipment performance and giving early warning of potential disasters.

For example,shell energy companyuses machine learning, neural networks, and IoT to identify security threats and automatically alert employees. That way, they can react to a problem before disaster strikes. By the way, Shell also uses machine learning to optimize production and mining operations.

Exploration of new fields.One of the main problems in the oil, gas and mining industry is the difficulty in discovering new deposits. Machine learning helps speed up this process. Based on data from previous fields, artificial intelligence builds models that predict where to look for new gas or mineral deposits with high precision.

machine learning in finance

Credibility assessment.Usually, in banks, managers assess the creditworthiness of a client. Employees spend a lot of time evaluating and often make mistakes. They refuse loans to those who can pay and give loans to the insolvent.

The algorithm to assess the credibility of bank customers can be taught. To do this, it is loaded with information about previously issued loans: if they were repaid or not, if there were delays or early repayments. All this helps the bank to automate the issuance of loans.

Citibank has developed a robust system that assesses creditworthiness. In particular, the system is polished to detect fraudulent credit card transactions when shopping online.

Fight against fraud.Banks and their customers often lose money in fraudulent transactions. Machine learning helps identify these transactions. Unique algorithms learn to detect signs of fraudulent transactions and block them in time.

Machine learning can be used to detect fraudulent account attempts, identify the identity of a customer without a passport, recognize ATM camera scammers, and analyze customer credibility. Many banks have examples of machine learning to prevent fraud. For example,danish bankreduced the percentage of false fraud claims by 60%.

Examples of machine learning in medicine

Improving customer service. The faster the appointment process in the clinic, the shorter the queues, the more comfortable it is for doctors to work and the more loyal the patients. For example,studentuses ML to provide students with the most relevant documents.

The Invitro network of clinics has implemented a facial recognition system. As soon as a patient walks up to the counter, the administrator sees the correct card on the computer and sends it to the correct office. This helps avoid queues at peak times, simplifies the work of receptionists and serves more patients.

Disease diagnostics.If you upload examination and diagnostic data into the program, you can be taught to diagnose in the same way as doctors.

For example,Corti's artificial intelligencelistens for ambulance calls and recognizes cardiac arrest based on callers' responses, voices and breathing. In an experiment, the program recognized 93.1% of cardiac arrests. Humans normally recognize 72.9%. Also, Corti works faster. She diagnoses in 48 seconds, compared to 79 seconds for humans.

Now the system is being implemented in several European cities. You will work in the emergency services together with the dispatchers. In the video, the AI ​​can be heard talking to the person who called the ambulance.

Automated robotic surgery.Machine learning helps teach medical robots to operate on patients on their own, taking many factors into account.

At the University of California, thethe robot watched 78 surgical moviesto teach her to sew. Thanks to this training, the robot was able to sew fake wounds, but with an accuracy of around 85%. For actual work, this is still not enough. Perhaps in the future these robots can be used to automate some operations. The video shows the learning process of the robot.

Application of Machine Learning in Retail and Marketing

Predict shopper actions, personalized offers, and advertising. A trained algorithm can predict customer behavior in the following ways:

• Determine who will make a purchase soon.
• Understand who prefers what products and recommend them.
• Offer personalized discounts to encourage purchases.

For example, the Rive Gauche cosmetics chain uses machine learning to send personalized offers to customers. The program determines which customers are likely to buy in the next two weeks, which products are best offered to them, and at which discounts. Customers who worked with the system had a 42% higher average check and repeat purchases were 47% higher.

Demand forecasting and purchasing automation.Machine learning helps analyze customer actions and inventory balances to understand what, when, and in what quantities to buy.

British supermarket chainMorrisons uses machine learningto predict what items will be purchased and when. The system takes many factors into account, such as holidays and weather. As a result, the network was able to reduce supply gaps by 30%.

Machine learning in logistics

Fuel savings and increased transport productivity. Fuel is one of the main cost elements in logistics. With the help of machine learning, it is possible to reduce your consumption: optimize routes or understand how to reduce the number of vehicles, preserving productivity.

Caterpillar Marine Divisionimplemented machine learning to save resources. The company installed sensors on the ships' equipment. More generators with smaller capacity have been found to run more efficiently than maximizing the use of multiple generators. This solution saved over $650,000 in one year.

Avoid supply interruptions. Delaying even one vehicle disrupts the entire supply chain: downtime, lost money, and customer dissatisfaction. Machine learning helps you avoid this by anticipating risks, preventing them early, and adjusting delivery times to account for all factors.

DHL uses artificial intelligence from Supply Watch. It monitors a variety of risks—weather, environmental factors, traffic congestion, and even crime rates—to proactively inform customers of potential delivery delays.

machine learning problems

Working with self-learning algorithms is complicated. Common programs are predictable. We can analyze them and understand how they work. With self-learning algorithms, the situation is more complex due to some problems:

• Businesses often run into complex machine learning problems that are difficult to break down into simple sub-problems.They jump from one experiment to the next as they try to solve these complex use cases. This makes it difficult to speed up AI development.
• Effective training of neural networks and complex algorithms requires a huge amount of data and technical resources: servers, special rooms for them, impeccable high-speed Internet, and plenty of electricity. It takes years and millions of dollars to get the data you need. Only a large IT corporation can afford such costs. There are not many open data sets, some can be bought but they are very expensive.
• As the capacity to collect and process data sets increases, the harmful emissions produced by major data centers also increase.
• Not only does the data need to be collected, but it also needs to be tagged so the machine knows exactly where the object is and what its attributes are. This applies to numeric data, text, and images. Again, you need millions of dollars in investment to do this manually.
• Even if there is a lot of data and it is updated regularly, it may happen that the algorithm does not work in the training process. The problem can be both in the data and in the approach itself: when a machine successfully solves a problem with some data but fails to scale the solution with new conditions.
• Despite all the advances in deep learning of neural networks, AI is still not capable of creating something completely new, going beyond the proposed conditions and exceeding its inherent capabilities. In other words, it is still not capable of surpassing humans. But they can perform tasks better than unskilled people. For example, a robot will stitch up a human wound better than an unskilled person. But a qualified surgeon will still do a better job.

Despite these issues, we will soon be able to significantly expand our capabilities with AI, transferring routine and expensive operations to it, communicating and controlling machines using neural interfaces.

Conclusion

The principle of machine learning makes it possible to create computers and programs that mimic human thinking. But unlike humans, they don't get tired, can make fewer mistakes, work with any amount of data, and evaluate it impartially. This opens up a vast field of possibilities for artificial intelligence.

Machine learning participates in the accelerated development of technologies. With it, we will be able to solve even tasks beyond the control of the human mind. And the examples of current ML applications are proof of this.

With machine learning, you can create many services and programs. From the simple ones we use every day – browsers, facial recognition cameras, recommendation services – to the complex tools we need in industry or security. Our IT team has in-depth knowledge of. Please feel free toContact Us, the Jelvix team will be happy to help you!