Blog

Artificial Intelligence

The Future of Machine Learning

From Apple to Google to Toyota, companies worldwide are investing heavily in creating AI systems with machine learning. Let’s look into the bright future of Machine Learning.

Ostap Zabolotnyy
Ostap ZabolotnyyMarketing Manager
The Future of Machine Learning

Machine Learning has evolved from an out-of-this-world concept to one that is widely used and understood by millions of computer-savvy technologists who are raving about it and putting it at the heart of their business and technological processes being and in the foreseeable future. 

Machine learning, a subset of artificial intelligence, has frequently shown to be advantageous to businesses. Machine learning is assisting companies, whether startups or multinational corporations become more efficient, smart, and successful. 

Unsurprisingly, Artificial Intelligence (AI) and Machine Learning (ML) are two of today's technological buzzwords. But how will these two technologies foster innovation and change in the near future? 

Do you have an answer? 

If not, continue reading to learn why machine learning and AI are two of the most exciting technologies that will spur innovation in the upcoming years. 

What is Machine Learning? 

This is arguably the most exciting time in technology history due to the increased use of mobile phones, the Internet, and emerging technologies. Computers have altered our perception of the world. They are faster than us when it comes to solving mathematical problems. 

However, the intelligent human mind cannot process data at the speed of a computer. Computers, on the other hand, are not 'intelligent' because they cannot do anything for themselves. Despite having access to all of the world's information, a computer has yet to put it to use on its own. What if we could teach it to a computer? That is precisely where Machine Learning comes in. The process of teaching computers to be "smarter" or "intelligent" is the most basic definition of Machine Learning. 

Machine learning solutions are incorporating changes into core business processes and becoming more prevalent in our daily lives. 

"The global machine learning market is expected to grow from $8.43 billion in 2019 to $117.19 billion by 2027," - according to forecasts. 

How Machine Learning is Becoming the Technology of Future 

Machine Learning tools provide potent capabilities in a variety of industrial sectors. Among the most important are the following: 

Fast Processing of Large Databases 

Have you heard of the term "Big Data"? It refers to the massive amounts of data generated and accessed by almost every organization today. The data flow around us is so massive that traditional tools cannot handle it. How are we supposed to analyze and comprehend it? 

Machine Learning is the answer. It automates data generation, storage, retrieval, and analysis. ML models extract meaningful insights from large amounts of structured or unstructured data at high speeds. 

Round the Clock Operation 

Machine Learning algorithms are robots that do your work without tiring or needing to sleep to recharge, allowing them to work 24 hours a day, seven days a week, 365 days a year. 

With tools like MLOps and AutoML, ML plays a significant role in automating business operations. Many routine processes such as network monitoring, database management, data integration, and so on are eliminated, allowing businesses to focus on more specialized tasks. 

Ability to Improve and Learn 

Most machine learning algorithms are designed to improve performance as they process more data. Recommendation Engines, for example, are intended to recommend 'products' to users by predicting what is most likely to interest them. 

Netflix, YouTube, Tinder, and Amazon are companies that use recommender systems. According to Forbes, 75% of Netflix users select movies/shows recommended by the company's machine learning algorithms. 

Widespread Applications 

AI and machine learning solutions have numerous applications in a variety of fields. The Machine Learning space is growing faster than any other technology. Let us examine some of its most popular applications. 

Applications of Machine Learning 

The future of machine learning is promising. Machine learning applications are now used in almost every common domain. Let us look at some of the top use cases that are emerging today and will help broaden the future scope of machine learning. 

Machine Learning in Education 

AI advancements allow teachers to understand better how their students are progressing in their learning. 

AI will make significant and positive educational changes, allowing students to enjoy learning and communicate more effectively with their teachers. Students will not be intimidated by their teachers or fear being judged. 

Teachers can use machine learning to determine how much of a lesson students can consume, how they cope with the lessons taught, and whether it is too much for them to consume. Of course, this allows teachers to assist their students in understanding the lessons. 

Moreover, keep at-risk students from falling behind or dropping out. 

Optimizing Operations 

Document management is the most common use case for optimizing operations. Today, many robotic process automation and computer vision companies, such as UIPath, Xtracta, ABBYY, and others, enable this. However, the future of machine learning will aim higher. 

  • Emerging ML technologies enable retail stores to monitor body temperatures and mask-wearing using thermal imaging and computer vision technology, paving the way for a safer transition from COVID-19 to normalcy. 
  • Sensors and Internet of Things (IoT) technologies assist manufacturing operations in optimizing granularly across the supply chain. 
  • The renewable energy industry is utilizing artificial intelligence to mitigate the unpredictability of sources. 

Machine Learning in Search Engine 

Search engines' use of machine learning to enhance their offerings is no longer a secret. Google has introduced some fantastic services as a result of implementing these. Voice recognition, image search, and many other applications are examples. 

Through the use of sophisticated machine learning, Google services like its image search and translation tools enable computers to determine, listen, and speak in a manner similar to that of humans. 

Machine learning is the term for today's cutting-edge AI applications. Time will tell how they come up with more exciting features. 

Safer Healthcare 

The use of machine learning to forecast and support COVID-19 strategies has significantly increased. Although the healthcare industry has long used machine learning for a variety of purposes, we believe that the future of machine learning will include more complex use cases. 

  • Robots precisely perform complex surgeries. 
  • To create personalized treatment plans, ML programs read patient histories, records, reports, and other data. The project IBM Watson Oncology is significant in this field. 
  • Wearable technology is also significantly advancing disease prevention and elder healthcare monitoring. 

Machine Learning in Digital Marketing 

This is where Machine learning can help. 

All over the world, machine learning is being used in digital marketing departments. It enables more targeted personalization. As a result, businesses can interact and engage with their customers. 

As consumer expectations for more personalized, relevant, and helpful experiences rise, machine learning is emerging as a valuable tool to help meet those expectations. 

Sophisticated segmentation also targets the right customer at the right time with the appropriate message. 

Companies have information that can be used to learn about their behavior. Nova employs machine learning to create personalized sales emails. It knows which emails performed better in the past and suggests changes to the sales emails accordingly. 

Fraud Prevention 

To prevent fraud, banks and other financial institutions employ machine-learning-based fraud detection technology. 

  • Banks are developing machine learning algorithms based on historical data to predict fraudulent transactions. 
  • Phishing emails are identified and filtered using classification and regression methods. 
  • Machine learning and computer vision algorithms check for identity matching across key databases in real-time to prevent identity theft. 
  • These pattern matching techniques also detect counterfeit documents and prevent forgery. 

Future of Machine Learning 

Machine learning is a revolutionary technology that is now a critical component of many emerging and established industries. 

This technology enables computers to access hidden insights and predict outcomes, resulting in significant business changes. 

"Machine learning is becoming more sophisticated with each passing year. And its full potential has yet to be realized—beyond self-driving cars, fraud detection devices, and retail trends analyses." 

According to Wei Lei, Vice President and General Manager at Intel. 

So, what does the future of machine learning look like? What effect will it have on our world in the future? 

Here are five significant predictions for the future of machine learning. 

Improved Unsupervised Algorithms 

Unsupervised algorithms are used in machine learning to make predictions from datasets when only input data is available but no corresponding output variables. 

Whereas the algorithm's output is already known in supervised learning, its unsupervised counterpart is closely associated with true artificial intelligence—the idea that a machine can learn to identify complex processes and patterns without direct human intervention. 

Hidden patterns or groupings can be discovered when algorithms are left alone to scour and present the exciting patterns in a dataset, which would have been difficult to obtain using supervised methods. 

Unsupervised machine learning algorithms are likely to improve in the coming years. Better algorithm development will result in faster and more accurate machine learning predictions. 

Enhanced Personalization 

Machine learning personalization algorithms provide recommendations to users and entice them to perform specific actions. 

Using such algorithms to synthesize information in a data set and draw appropriate conclusions, such as a person's interests. 

For instance, based on a user's online shopping behavior, an algorithm can infer that the user is considering buying a lawnmower for his garden. 

Without that knowledge, the buyer may have left the website without making a purchase. 

These recommendations are currently inaccurate and annoying, hampering users' experiences. On the other hand, personalization algorithms are likely to be fine-tuned in the future, resulting in far more beneficial and successful experiences. 

Increased Adoption of Quantum Computing 

The field of machine learning could be transformed by quantum machine learning algorithms. For example, these algorithms can use the benefits of quantum computation to improve the capabilities of classical machine learning techniques. 

Suppose quantum computers are integrated into machine learning. In that case, it could result in faster data processing, accelerating our ability to synthesize information and draw insights—which is what the future holds for us. 

Quantum-powered systems will accelerate and improve the performance of both supervised and unsupervised algorithms. 

The improved performance will enable fantastic machine learning capabilities that would not have been possible with traditional computers. 

Improved Cognitive Services 

Cognitive services comprise a collection of machine learning SDKs, APIs, and services that enable developers to incorporate intelligent capabilities into their applications. 

Developers can use such services to enable their applications to perform vision recognition, speech detection, and speech understanding tasks. 

We can anticipate the development of highly intelligent applications that can communicate with their environment by speaking, hearing, seeing, and even reasoning with it as this technology develops. 

As a result, developers can create more engaging and discoverable applications that use natural communication techniques to interpret users' needs effectively. 

Rise of Robots 

Robots will be used more as machine learning becomes more sophisticated. Machine learning is used in robotics for various purposes, including robot vision, self-supervised learning, and multi-agent learning. 

We anticipate that robots will become more intelligent at completing tasks in the near future. Drones, manufacturing robots, and other types of robots are likely to be used to make our lives more accessible in the future. 

Conclusion 

Scientists and experts have been trying to develop a computer that functions more like post-industrial humans. The thought machine is AI's greatest gift to civilization; the unexpected arrival of this self-propelled machine has rapidly altered operational business laws. Self-driving cars, automated assistants, autonomous factory workers, and smart cities have recently demonstrated the viability of smart devices. The machine learning revolution will last a long time, as will the future of machine learning. 

More advanced applications that stretch their capabilities to unimaginable levels are likely in the coming years.

Get our infopack

Best quick guide to start working with us.