What Is the Future of Machine Learning?

The 365 Team 1 Feb 2023 7 min read

The future of machine learning

Nowadays, businesses generate an astonishing 2.5 quintillion bytes of data every single day. For those of you wondering how much that is – well, there are 18 zeroes at a quintillion. This is a huge number!

With people using social media platforms, digital communication channels, and various contactless services, it is no surprise that big data continues to grow at a colossal rate.

But how are we going to make the best out of this trove of information in the future?

As businesses move towards the era of cloud storage, they look for innovative approaches to leveraging data. Since it’s virtually impossible to analyze great data volumes manually, big organizations can now adopt machine learning to handle these for them. Fortune Business Insights has recently published an article, estimating that the machine learning industry will reach nearly \$153 billion by 2027 – a massive growth rate compared to the \$15.50 billion in 2021. And all that happening for less than a decade! We could barely imagine what it would all be like in 2050.

In this article, you’ll find out how we, at 365 Data Science, believe machine learning technology will transform the business world in the next few years and what emerging trends may be worth considering.

Machine Learning vs. Artificial Intelligence vs. Deep Learning

Many people wrongly use the terms machine learning (ML), deep learning (DL), and artificial intelligence (AI) interchangeably. To better understand the future of ML, one must be able to differentiate between these 3 concepts.

Here’s a simple graph to clear any confusion you might have:

The differences between machine learning (ML), deep learning (DL), and artificial intelligence (AI).

Artificial intelligence is an umbrella term, encompassing both machine learning and deep learning. It is inspired by the human brain and focuses on mimicking people’s behavior.

Machine learning is about creating an algorithm that a computer uses to provide valuable insights, with data being its key component. It is unique in developing algorithms that learn from data to solve problems without programming. Like a human, a model learns through experience and improves its accuracy over time.

At the core, you’ll find deep learning – an advanced feature of ML whose algorithm has its own learning mechanisms.

Evolution of Machine Learning

Although we can’t name a single person or event that made it all happen, the evolution of machine learning tells us just how multi-dimensional the field can be.

Some believe it all started back in 1943 when Walter Pitts and Warren McCulloch presented the world’s first mathematical model of neural networks. Here’s a simplified representation of the concept, consisting of 2 parts – g and f:

Representation of the first mathematical model of neural networks, the McCulloch-Pitts Neuron.

In a couple of years, the famous book The Organization of Behavior by Donald Hebb was released to later become a turning point in the field of ML.

It wasn’t until the 1990s that the very first machine learning program was introduced to the world. That’s how the spam filter came into existence, and people could now save time sorting out emails. This significant milestone represented the collective effort of scientists and marked the beginning of the contemporary ML era.

To learn more about how to classify spam messages yourself, check out our Machine Learning with Naïve Bayes course.

The Future is Now: Latest Advancements of Machine Learning

Over the last decade, many innovations in various fields have come to the forefront thanks to machine learning. Let’s briefly present 5 ML advancements that are currently trending and are here to stay:

Computer Vision

Computer Vision is a type of AI where a computer can identify objects in images and videos. With the advancement in ML, the error rate has now decreased from 26% to just 3% in less than a decade.

Along with better accuracy and methods such as cross-entropy loss, humans are also able to save time in performing some tasks. If I ask you to categorize 10,0000 pictures of dogs, will you be able to do it in a few minutes? Unlike a computer with a CPU, you’ll probably take weeks to perform the task, provided you are a dog expert. In practice, computer vision has a great potential in the medical field and airport security that companies are nowadays starting to explore!

Focused Personalization

Another beneficial ML advancement has to do with understanding target markets and their preferences. With the increased accuracy of a model, businesses can now tailor their products and services according to specific needs using recommender systems and algorithms. How does Netflix recommend shows? What is Spotify’s secret to playing your favorite songs? It’s machine learning that’s behind all these recent developments!

Improved Internet Search

Machine learning helps search engines optimize their output by analyzing past data, such as terms used, preferences, and interactions. To put it into perspective, 2 trillion Google searches have been registered in 2021 alone. With so much data at hand, Google algorithms continue to learn and get better at returning relevant results. For many of you, that’s the most familiar ML development of our time.


This is another ongoing trend businesses around the globe employ. Chatbot technologies contribute to improving marketing and customer service operations. You may have seen a chatbot prompting you to ask a question. This is how these technologies learn – the more you ask, the better they get.

In 2018, the South Korean car manufacturer KIA launched the Facebook Messenger and chatbot Kian to its customers, boosting social media conversion rates up to 21% – that is 3 times higher than KIA’s official website.

Transportation Advancements

Many logistics and aviation companies see adopting ML technologies as a way to increase efficiency, safety, and estimated time of arrival (ETA) accuracy.

You will be surprised to know that the actual flying of a plane is predominantly automated with the help of machine learning. Overall, businesses are largely interested to unearth ML’s potential within the transportation industry, so that’s something to look out for in the near future.

Key Problems of Machine Learning

Machine learning – as revolutionary as it may be – isn’t flawless. Its enormous potential comes with a number of challenges that are shaping up the digital world of tomorrow.

A visionary, however, will always try to turn a stumbling block into a steppingstone. We believe today’s problems trigger tomorrow’s solutions, so let’s find out what these hurdles are:

Data Acquisition

Machine learning can only produce relevant and high-quality results if we feed enough data into the model. The need for massive resources then raises a question as to how unbiased and accurate the training data can possibly be. In what way do we ensure flawless input and sound results? The “Garbage in, garbage out” principle is what drives the proper functioning of machine learning models, and that’s a real challenge in today’s data-rich environment.

Your analysis is as good as your data.


Generally, machine learning requires a lot of resources, such as powerful computers, time for developing, perfecting, and revising a model, financing, and data collection. Businesses must be ready to take on considerable investments before reaping the harvest of adopting machine learning.

Data Transformation

Contrary to popular belief, machine learning isn’t made for identifying and modifying algorithms – it’s about transforming raw data into a set of features to capture the essence of that information. In its autonomy, ML can make some mistakes that affect its efficiency in the long run.

Error susceptibility is certainly a major thing to consider when transforming data with ML.

Result Interpretation

An ML model tends to make self-fulfilling predictions. When training data and identified patterns are wrong, the algorithms will still use this information as a basis for generating and processing new data. It may take some time before you realize that the model has been working in favor of the underlying bias. For this reason, result interpretation turns into a comprehensive task for the user.

Bias and Discrimination

How do businesses prevent bias and discrimination when the training data itself can be corrupted? They say the road to hell is paved with good intentions – a proverb that describes the ethical dilemmas of the ever-growing digital universe very well.

Although you mean good when building a model to automate processes, you may unintentionally ignore or misinterpret an important human factor, which you would have otherwise prioritized. That’s a major issue when incorporating ML within recruitment and hiring practices.

Future Machine Learning Trends

Dave Waters once said:

A baby learns to crawl, walk, and then run. We are still in the crawling stage when it comes to applying machine learning.

Here, we’ll outline 5 trends we believe will unfold in the next few decades. They all derive from the current developments and ongoing challenges within the industry.

Top 5 machine learning trends to watch in the future.

The Quantum Computing Effect

Industry experts have high hopes about optimizing machine learning speed through quantum computing. And rightfully so – it makes simultaneous multi-stage operations possible, which are then expected to reduce execution times in high-dimensional vector processing significantly.

Whether quantum computing will turn into the game-changer everyone’s talking about, we are yet to find out! Currently, there are no such models available on the market, but tech giants are working hard to make that happen.

The Big Model Creation

The next few years are expected to mark the beginning of something big – an all-purpose model that can perform various tasks at the same time.

You won’t have to worry about understanding the relevant applications of a framework. Instead, you’ll train a model on a number of domains according to your needs. How convenient would it be to have a system that covers all bases – from diagnosing cancer to classifying dog images by breed?

Of course, a well-designed quantum processor to enhance ML capabilities will certainly give that development a boost. That’s why great minds are now putting considerable effort into reinforcing the scalability and structure of such a model.

Distributed ML Portability

With the proliferation of databases and cloud storage, data teams want to have more flexibility when it comes to using datasets in various systems.

We foresee a great advancement in the field of distributed machine learning where scientists will no longer reinvent algorithms from scratch for each platform. Rather, they will be able to immediately integrate their work into the new systems, along with the user datasets.

In the coming years, we will likely experience some form of distributed ML portability by running the tools natively on various platforms and computer engines. In this way, we’ll eliminate the need for shifting to a new toolkit. Experts in the field are already talking about adding abstraction layers to make that technological leap.

No-Code Environment

As open-source frameworks like TensorFlow, scikit-learn, Caffe, and Torch continue to evolve, machine learning is likely to keep minimizing coding efforts for data teams.

In this way, non-programmers will have easy access to ML – no postgraduate degree is required; they can simply download several packages and attend an online course on how to work with these programs. Besides, automated ML will improve the quality of results and analysis. So, we expect machine learning to be classified as a major branch of software engineering in the next decade.

The Power of Reinforcement Learning

Reinforcement learning (RL) is revolutionary – it enables companies to make smart business decisions in a dynamic setting without being specifically taught for that.

With all that’s happening around us, unpredictability seems to have become the new normal. Thus, we expect ground-breaking leaps in RL to help us deal with unforeseen circumstances.

Everyone’s talking about optimization of resources, but it is reinforcement learning that can truly leverage data to maximize rewards, where no other model can. RL is still in its early days, so we will likely see several breakthroughs in the field within the next few years in industries like economics, biology, and astronomy.

When Man Meets Machine: Will ML Replace Humans in the Future?

With the latest advancements in technology, many of us can’t help but ask, “Will machines take over all the functions of a human?”

While the vision of robots ruling the world seems quite unrealistic, people are still worried about the stability of their jobs in the future. That’s when you should take a step back and reframe the whole image of machine learning.

Instead of wiping out the need for human labor, ML disruptions will lead to a job demand shift. The basic requirements for a certain role today will likely include a different set of competencies tomorrow. We like to see it this way – machine learning developments level up both machines and people. So, make sure you stay current and keep up with the latest trends!

For businesses, machine learning will remain a buzzword in the years to come. Its prominence is inevitable, even necessary, for the world to cope with the volumes of data we produce daily. Yet, we are far from discovering its true potential and reaching maturity.

Ultimately, machines can’t do it all by themselves. As the saying goes, it takes two to tango. Both businesses and technologies will be tirelessly working towards making the world a better place, not replacing humans.

The Future of Machine Learning: Next Steps

Machine learning is an exciting field with immense untapped potential. So, if you're an aspiring data scientist and ML technologies fascinate you, then you should start learning today. The industry is evolving every day, with new breakthroughs being discovered as we speak. Don’t let yourself fall behind on the trends!

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The 365 Team

The 365 Data Science team creates expert publications and learning resources on a wide range of topics, helping aspiring professionals improve their domain knowledge, acquire new skills, and make the first successful steps in their data science and analytics careers.