ML technologies that are already having a powerful impact on business

Technologies fascinate, repel, cause bouts of skepticism… People have different attitudes towards any new product, and this is normal. But as we know, the true value of any innovation lies in its ability to solve real problems and meet customer needs. Therefore, only by applying new technologies in practice to solve specific business needs can we easily distinguish truly useful things from those that still need to be developed and cannot yet bring tangible results.

Nowadays, machine learning (ML) is experiencing rapid changes and constant development. In this article, we'll look at the latest trends in ML and explain how they're already taking businesses to the next level without the expense of new employees.

Models for generating text and images

Creating high-quality images using artificial intelligence is quite comparable to art. Various models, such as GANS, VAEs, and diffusion models, strive for this. Each in its own way. But despite the achievements, their use does not always guarantee an ideal result.

Today in the world of generative art, we see leaders like GLIDE, OpenAI's DALL.E-3, Google's Ideogram, and Stable Diffusion setting new standards for excellence in image creation.

Large language models (LLMs) could change the game in a very different way. They are a real breakthrough that not only helps us process huge amounts of information, but also stimulates our human creativity and ability to think deeper. Just imagine the possibilities available to us thanks to models such as GPT-4 from Open AI or its competitor – Grok 1.5 from xAI. They already help in creating texts, generating ideas for content, writing code… Now imagine what they can do in the future!

But you may be asking the question: “Why create new models when you can improve existing ones?”

Creating new LLMs and image generation models has enormous potential for business growth. After all, this allows companies to remain competitive and respond to rapidly changing market demands.

For example, a marketing agency. Using new LLMs can significantly improve the quality of content and advertising, making them more attractive and unique to audiences. Increase customer engagement = increase conversions.

If your business is in manufacturing or design, new imaging models can significantly reduce the time and cost of developing new products and product concepts. And this will lead to faster entry into the market, which will increase the company’s competitiveness and meet customer needs.

So it turns out that investing in the development of new models is a strategic step for business growth. This will help you stay ahead of competitors, improve the quality of products and services, and expand your audience and increase profits.

Cloud data and industrial cloud platforms

Cloud computing has already become part of modern technology initiatives, simplifying and improving machine learning processes. They offer companies a wide range of capabilities, allowing them to quickly deploy ML algorithms and leverage data.

Take GPUs in the cloud for example. With them, you no longer need expensive equipment to train models. With cloud services, you can access powerful GPUs and ready-to-use models.

And that is not all. With cloud storage solutions, you and your team can work anywhere, anytime. There is no longer any need to be tied to an office or computer to access the information you need.

What about industry cloud platforms? After all, they do a much better job than living personal assistants for your business, solving the most complex problems.

For example, industry cloud platforms can analyze sales data and help companies predict which products will be in high demand in the future, thereby increasing sales profits. In addition, cloud platforms make marketing more personalized by analyzing customer data and helping to create campaigns that specifically target their interests. They also simplify financial planning and supply chain management, simplifying logistics and reducing costs.

In the medical field, cloud platforms are used to analyze medical data, helping doctors make more accurate diagnoses and choose effective treatments.

And, by the way, according to forecasts, by 2027 more than half of companies will use cloud platforms to optimize their work.

TinyML

The literal translation of “tiny” means “tiny.” This name refers us to where TinyML is used. Namely in IoT (Internet of Things) and mobile devices. Because the world is increasingly being rebuilt under this vector.

While there are major ML applications out there, they often have their limitations. This is where small apps come into play. They become real heroes in different scenarios, providing solutions that previously seemed out of reach.

Let's say you send data to a remote server for processing by an algorithm, and then receive it back through a web request. This can take a lot of time and is also accompanied by colossal energy consumption. A more efficient approach is to use ML programs directly on peripheral devices.

Implementing such programs on IoT devices brings many benefits. They reduce latency, reduce power consumption and improve user privacy. And in addition, the approach eliminates the need to send data to a central server for processing, which speeds up the entire process (you must agree that there is nothing worse than waiting).

The most important thing: TinyML has found its application, from industry to healthcare and agriculture. Using IoT with TinyML algorithms, farmers can monitor plant health or predict the weather. One caveat is that the implementation of such projects requires qualified personnel with knowledge in the field of ML and embedded systems.

Another use case for TinyML is video surveillance systems in the city. Similar technology can be implemented in them to detect suspicious individuals on the street or dangerous situations in public places or inside buildings. This will help reduce the risk of crime and increase the level of security, which is extremely important now. Especially in shopping centers, schools, kindergartens, stadiums – anywhere where a large number of adults and children gather.

AutoML

AutoML, or automatic machine learning, is a new technology that makes data scientists' jobs easier. Here's how it works: Imagine you need to solve a complex data analysis problem, but you're not an expert in the field. But AutoML is, in a way, an expert. The technology offers you tools that allow you to automatically process data, create models and analyze results without the need for in-depth knowledge in the field.

Of course, AutoML doesn't always work perfectly, and its accuracy may not be as high as that of a human (although humans make mistakes). In any case, the process is automated, tasks are completed faster, and the risk of human error is reduced. This allows companies to focus on analyzing the data they have already acquired, instead of wasting time and hiring new employees on routine work.

Examples of such platforms: Google Cloud AutoML, H2O.ai and DataRobot. They have many built-in tools for automatic data processing and creation of ML models.

MLOps

In simple terms, MLOps is a set of tools and processes that are used to manage and automate the life cycle of an ML project, from developing and training models to deploying and monitoring them in a production environment. It's like an engineering approach that helps companies effectively manage ML projects so that the models work stably and efficiently in real processes. And, of course, MLOps helps to launch the model into production faster.

We have already noted that in MLOps the key component is the system life cycle, which is based on the DevOps methodology. Understanding this cycle is important to understanding the importance of MLOps.

In general, this cycle looks like this:

1. Development of a model that meets business goals.

2. Collection, processing and preparation of data for the machine learning model.

3. Training and setting up the machine learning model.

4. Validation of the machine learning model.

5. Deployment of a software solution with a built-in model.

6. Monitor and restart the process to improve the model: if the model produces worse results or there are changes in the data that are not for the better, a predefined training process is launched taking into account the new data.

If learning outcomes still deteriorate, human intervention is required.

Basically, MLOps allows you to manage large systems better. This means the team has a better chance of quickly solving problems that arise when working with large amounts of data and complex models. But let's not forget that sometimes using MLOps can be difficult due to limited resources, communication problems between your team and other teams, and constantly changing project goals. The latter is generally a special type of pain, but, nevertheless, it needs to be dealt with and something needs to be resolved quickly. Critical and creative thinking is everything.

No-code ML

Based on the name, it becomes clear that this is about developing models without having to write complex code. Or use it to a minimum.

Low-code machine learning (LCNC) frameworks are a handy thing for people who are not AI pros but want to build applications in this area using ready-made tools. Such platforms, by the way, already in 2023 accounted for the majority of income in the global market.

But there is a problem – sometimes these platforms can limit users who want to customize their applications. This may be due to certain business policies or specific design requirements.

Another problem is scaling. While many of these platforms can scale, building large-scale applications may require more complex programming. Scaling options may then be limited by platform infrastructure or resources.

Hence the conclusion: low-code or no-code ML platforms are suitable for simple projects. For example, forecasting housing prices, dynamic pricing or analyzing employee retention rates. But to create more complex and important projects, the participation of ML engineers is always required. Because for now, truly successful data analysis and creation of advanced models require people, not machines.

Reinforcement learning to optimize decision making

Reinforcement learning is the process of teaching a model through exposure to an environment where it receives a reward or punishment for its actions.

Although reinforcement learning is often used in game development, its use in robotics requires caution, especially when safety is key. And this is almost always the case.

Since the algorithm may make random decisions during the learning process, this can lead to unsafe behavior if left unchecked. Therefore, more accurate reinforcement learning systems are being developed, which accordingly puts a premium on thoughtful decision-making to ensure safety.

The full potential of reinforcement learning will be realized when the method can solve real-world problems without endangering people.

In robotics, reinforcement learning is like robot school. They learn by interacting with the world around them and draw conclusions. For example, a robot vacuum cleaner learns how to avoid furniture, pets, and small children playing on the floor. Anything can happen, but there is a risk that the robot will misinterpret the situation and do something unexpected or dangerous.

By the way, a similar situation was nicely depicted in one of the episodes of the series “Love. Death. Robots,” only there everything was much tougher and, rather, threatened to become a bloody mess.

Episode entitled “Customer Support” at the beginning it depicts a utopian world in which robots do everything from walking with your ponytails to controlling traffic. Brave, very brave. People are as relaxed as possible. So even when one elderly lady's robot vacuum cleaner malfunctions, it becomes difficult to believe that this little one is capable of murder. Alas and ah. Still capable. But let's not spoil it, just watch the episode. About four minutes about the uprising of the machines, during which granny is ready to do anything to save herself and her dog.

However, not only in the imaginations of directors and screenwriters, robots mistook people for “non-humans”, trying to get rid of them. In 2023, there was a case when, as a result of a technical glitch, a robot in South Korea accidentally killed an employee of one of the robotics companies. This was reported by Yonhap Agency.

The incident occurred on November 8, 2023 at a pepper grading plant in Gyeongsangnam-do Province. A company employee checked the operation of the robot's sensors. The robot's job was to lift boxes of pepper and place them on pallets.

According to Yonhap, the robot's sensor may have mistaken the employee for a box, and the robot grabbed him, pinning his upper body against the conveyor belt, causing severe injuries to his face and chest.

The victim was taken to the hospital, but to no avail: the man died from his injuries.

A spokesman for the Donggoseong Agricultural Export Complex, which owned the plant, said the robot had been in use at the plant for five years and had never experienced any problems before.

Yonhap also notes that a similar incident has already occurred in South Korea, also in 2023: a 50-year-old man was seriously injured after being caught in the path of a robot. The details of this incident were not disclosed.

Naturally, this case is not the first and will not be the last. This is why optimizing decision making in reinforcement learning is a top priority for ML engineers.

Models for solving problems in a specific area

Machine learning models are already used not only to solve general problems, but also for specific industries, taking into account the specifics of the market and business needs. Knowledge in a particular area helps save time on creating ML models that already meet the needs of a particular area and therefore require fewer adjustments.

Here's how ML is changing traditional workflows in different industries:

Banking and Finance

ML is used to detect signs of fraud, as well as to automate routine tasks using bots. Further developments include customer sentiment analysis, investment modeling, trading and risk prevention. ML is expected to help traders reduce risks and prevent market crashes.

Healthcare

In this sector, ML technologies are used to analyze medical images, diagnose diseases, develop personalized treatment plans, drug discovery and predictive analytics. This helps provide more accurate diagnosis and more effective treatment.

One of the interesting areas is Human Pose Estimation (HPE). HPE uses CV (computer vision) to detect body movements, correct posture and monitor exercise.

Production

ML improves efficiency by improving processes and optimizing production. For example, ML algorithms analyze equipment sensor data to predict when a machine is likely to fail. This helps plan diagnostics and timely maintenance, reduces downtime and extends equipment life. ML is also used for visual quality control and real-time defect detection.

Retail

Retailers are using ML to improve store layouts, product placement, and marketing strategies. ML algorithms track the movement of customers and their choice of certain products.

What about solutions for virtual fitting in online clothing stores? They are also great at personalizing the customer experience.

Marketing

Machine learning improves marketing using chatbots and virtual assistants, as well as analyzing data to obtain useful information. This is how SocialMiningAI's machine learning algorithms can monitor millions of social media posts to find potential customers who are ready to buy. With their help, it is easy to determine who is interested in the company's product or service.

What will happen next?

The global machine learning market will grow rapidly, from $26.03 billion in 2023 to $225.91 billion by 2030.

This impressive growth of the global market in the field of ML demonstrates its significant impact on modern economies and industries. Advances in AI technology provide us with a unique opportunity to not only optimize processes and improve product quality, but also push beyond what was once thought impossible.

It is necessary to strive for continuous improvement and innovation in this area by exploring new approaches and technologies. This will allow businesses not only to remain competitive, but also to become leaders in their industries, bringing new, revolutionary solutions to the world.