Machine learning is, by far, one of the prevalent AI-related technologies. Why is it so popular? In short, because it works brilliantly. Today, machine learning can be found almost everywhere, from movie recommendations on Netflix, up to intelligent assistants like Amazon Alexa. Like every AI-fueled technology, machine learning constantly evolves and develops. With every month, this technology is more accurate and efficient. Since it is December 2020, we decided to take a closer look into the future and see what uses of machine learning will look like in the coming months.

machine learning in business

In short, we rather don’t expect a massive revolution in this sphere. We can expect that uses of machine learning will be more or less the same. Although, there are some interesting trends in the uses of machine learning that you should definitely pay attention to. And now, it’s the perfect time to analyze them!

Uses of machine learning: Hyperautomation

Let’s start with the first exciting trend. Surely, you already know what automation is. We frequently mention this technology on our blog, as it’s one of many that are boosted by machine learning. In the near future, we will probably speak of hyper-automation. Supposedly, this term was used for the first time exactly a year ago. It appeared in December 2019 on Gartner’s blog[1]:

Hyperautomation deals with the application of advanced technologies, including artificial intelligence and machine learning, to increasingly automate processes and augment humans. Hyper-automation extends across a range of tools that can be automated, but also refers to the sophistication of the process.

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This approach assumes that almost all processes and elements functioning within a company can be (and should be!) automated. Naturally, the current COVID-19 pandemic significantly boosts this trend. People work from home. Many companies operate only halfway. Automation is now needed more than ever. One of the indispensable technologies that are necessary to pull hyper-automation off is, naturally, machine learning. The more complex processes you want to automate, the more computing and learning power you need. As an example, let’s consider law processes. The very agreement preparation takes a lot of effort, knowledge, and experience. Eventually, we can expect that these processes will also be at least partly automated.

Read more about machine learning solutions for retail and e-commerce. 

Uses of machine learning: Hyperautomation

Uses of machine learning: Development of the Internet of Things

The number of IoT platforms grows dynamically. In 2015, there were just 260 publicly known IoT platforms. In 2019, there were 620 of them[2]. This technology has established itself very quickly, primarily because it really makes a difference. Today, many sectors and industries utilize this technology in their production processes, commerce, and maintenance. These sectors are, for example:

  • Retail
  • Manufacturing
  • Industry facilities
  • Transportation and logistics
  • Fleet management

And many more. But why are we talking about IoT while this article should be about machine learning? Again, both these technologies are tightly connected. Machine learning makes IoT devices and services smarter and more secure. It’s machine learning that enables further development of the Internet of Things. And we can expect that this technology will be more and more advanced in the coming months and years, and the number of IoT-connected devices will go sky-high. After all, keep in mind that you need technology to manage these devices and analyze data coming from them. And this is where machine learning enters the game.

Uses of machine learning: Development of the Internet of Things

Uses of machine learning: Reinforcement Learning (RL)

Possibly you remember that we mentioned RL in our blog post in mid-2020. Reinforcement learning comes in handy when you have little to no historical data, and learning has to happen in real-time. You can think of RL as a more advanced machine learning algorithm. While traditional ML algorithms do need historical data to learn, the RL algorithms don’t need any information in advance. In other words, they learn from data during the process. Reinforcement learning is actually a combination of traditional machine learning and deep learning. It’s without a doubt a top-of-the-line technology.

We can expect to see more RL applications and algorithms in such areas as:

  • Chatbots
  • Robotics (for example, robot motion control)
  • Industrial automation
  • Aircraft control

Most likely, in the coming months and years, we’ll see even more implementations of this technology.

Chatbots

Uses of machine learning: Enhanced cybersecurity

Cybersecurity is a tough business, a never-ending battle between cybersecurity specialists and hackers who want to break into the corporate IT systems. Today, there are tons of various forms of cyberattacks:

  • Denial-of-service (DoS) and distributed denial-of-service (DDoS)
  • Man-in-the-middle (MitM)
  • Phishing
  • Password attack
  • SQL injection
  • Cross-site scripting (XSS)
  • Eavesdropping
  • Malware

And several others. The cybersecurity software and professionals try to prevent these attacks and, if possible, avoid them altogether. However, the more complex attacks become, the more complex shield you need. And this is where machine learning comes into play. As you know, this technology is based on analyzing historical data to improve current systems and devices. This way, ML algorithms analyze thousands of cyberattacks and help build even more resistant software and websites.

cybersecurity

In early 2018, we heard about a malware attack in an attempt to install malicious cryptocurrency miners. In just 12 short hours, cybercriminals attempted to infect nearly half a million systems. Microsoft Windows Defender stopped the attack, anti-virus software that, yes, uses machine learning to identify and block threats[3]. This technology simply works, and we can expect that it will be developed in the coming months.

Uses of machine learning: AI Engineering

Let’s go back to Gartner for a few moments. This company’s research shows that only 53% of AI-related projects make it from the prototype stage to production. In order to increase this value, AI companies will surely turn to AI engineering. In short, it’s a discipline that makes AI development more efficient and established. AI engineering is focused on the governance and life cycle management of AI models and applications. This, naturally, includes machine learning.

This way, we can expect that machine learning designed with the AI engineering approach will be more efficient, more accurate, and well-designed. In short, AI Engineering is what makes AI and ML algorithms better.

AI Engineering

Uses of ML: Healthcare

Healthcare is one of the fields that makes the most of machine learning. Although we don’t have one established technology or application in mind, ML changes healthcare for the better. In fact, machine learning is present in almost every sphere of healthcare. From drug development, where ML makes the entire process more accurate, quicker, and, on top of that, cheaper, up to outbreak predictions. Did you know that the Canadian company called BlueDot managed to spot the COVID-19 outbreak nine days before the World Health Organization released its statement alerting people about the emergence of a novel coronavirus? Read this article for more information about that!

Thanks to machine learning, we can expect that healthcare will be even more enhanced in the coming months and years. This technology improves the work of pharmacists, surgeons, radiologists, and other physicians. Today, thousands of patients worldwide benefit from it.

Uses of machine learning: Healthcare

Uses of ML: Augmented and Virtual Reality

That’s the last trend we want to tackle in this post. Augmented (AR) and virtual (VR) reality play a more and more critical role, especially in e-commerce and the gaming industry. These technologies don’t have that much in common at first glance, but it’s simply not true!

Machine learning can enhance AR and VR alike. For instance, machine learning enables much more advanced image or scene labeling. Modern ML models can help classify the location by labeling scenes where each frame of the video is considered a separate image. Additionally, machine learning can improve object detection. Machine learning models can estimate objects’ position and size within a scene that can be viewed through a VR device.

Virtual Reality

And what about AR? Augmented Reality allows you to combine aspects of the real world with computer-generated content. This technology is commonly used in e-commerce. For instance, it will enable you to verify if this new lamp you intend to buy will fit in your living room. We can soon expect this technology to be present in every larger online store, especially those with furniture and clothes.

Machine learning is a fascinating technology that even today “works miracles”. If you are interested in possible applications or wondering how this technology can help you in your everyday work–drop us a line! We are always keen to show you all the benefits machine learning offers. And with our help, you will be able to implement it into your company. Find out today what we can do for you!

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References

[1] Kasey Panetta. Gartner Top 10 Strategic Technology Trends for 2020. Oct 21, 2019. URL: https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020/. Accessed Dec 18, 2020.
[2] Shanhong Liu. Number of publicly known Internet of Things (IoT) platforms worldwide from 2015 to 2019. Jan 4, 2021. URL: https://www.statista.com/statistics/1101483/global-number-iot-platform/. Accessed Dec 18, 2020.
[3] Gordon Gottsegen. MACHINE LEARNING CYBERSECURITY: HOW IT WORKS AND COMPANIES TO KNOW. June 30, 2019. URL: https://builtin.com/artificial-intelligence/machine-learning-cybersecurity. Accessed Dec 18, 2020.

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