Evolution of Machine Learning and How It Will Affect the Future

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Click to learn more about author Kateryna Boiko

Machine learning (ML) has been more than a buzzword lately. All the top companies have adopted innovative technologies, with Google making artificial intelligence a priority, Uber incorporating machine learning with Uber Eats, and countless research labs opening all over the world. 

How did machine learning become such a powerhouse? How does it change our daily lives, and what is in store for the future? This piece will be both a walk down memory lane and a peek behind the curtain of upcoming trends in machine learning. 

Evolution of Machine Learning

At times, it’s astonishing how creativity and a desire to add more convenience to our daily lives can kickstart such a powerful branch of human history. When designing the first mechanical calculator back in the 17th century, that French teenager probably never dreamed that moment would be the starting point in machine learning history. 

Later, more ambitious discoveries and devices led to machine learning in all its current power and glory. Here are the most important milestones in the history of machine learning:

  • In 1679, the binary system was born 
  • Ada Lovelace created the first computer algorithm in 1842, becoming the first-ever programmer 
  • In 1936, Turing designed the concept of a universal machine 
  • In 1947, the first neural network was created 
  • In 2006, the term “deep learning” was coined based on neural network research 
  • In 2011, a Watson computer won Jeopardy 
  • 2012 marked the year of the Google Brain release and significant improvements in human face reading 
  • In 2016, the concept of natural language processing (NLP) became a widely discussed development vector. Since then, chatbots can now answer messages the way humans would. 

This is certainly not a full list of all the exciting machine learning innovations developed over the last few decades. However, it’s clear that ML innovations are growing at an impressive pace.

Top Machine Learning Uses

Until lately, machine learning wasn’t used extensively outside the tech industry. However, by the end of the last decade, ML applications made their way into security, healthcare, education, and multiple other industries. 

Let’s examine the most popular machine learning uses:

1. Social Media

Facebook is among the chief adopters of promising machine learning innovations. The platform uses face recognition to allow people to tag friends in posts. Additionally, deep learning analyzes all pictures uploaded by users and tags them automatically.

2. Personalized Retail

Tools like Amazon product recommendations are heavily based on machine learning. Adopting deep learning was a big move for the corporation. According to recent statistics, product recommendations make up approximately 35 percent of the retailer’s revenue.

3. Fraud Detection

Credit card fraud is a major problem in the financial industry, causing over $32 billion in damage. Machine learning is a much more efficient way to detect fraud attempts than the traditional rule-based methods financial institutions use. 

Here’s how machine learning is adopted in fraud prevention:

  • Finding implicit data correlations that can be red flags of possible fraud
  • Detecting fraudulent scenarios with no human supervision 
  • Improving the speed of fraud detection and contingency plan implementation 

4. Creating Artwork

Machine learning allows computers to process music, literature, or art and create original samples based on millions of references. Despite the common misconception that machines lack creativity, the truth is, their artwork can be quite inspiring. 

Here are the most promising machine learning applications in art:

  • IBM’s Watson became an excellent kitchen chef and created recipes from scratch 
  • After analyzing hundreds of Gaudi’s sculptures, IBM’s Watson was able to create original works in the style of the artist 
  • Machine learning allows computers to write music 

5. Healthcare

For several years, doctors all over the world have used machine learning to detect cancer and other illnesses in their earliest stages. For instance, in China, one ML tool analyzes thousands of CT scans a year to spare physicians more time for surgical treatment and post-op care. 

In addition, Google’s research on human brains led to the development of the Deep Brain — an algorithm that mimics the way a human brain works. This advancement can shed more light on the nature of Alzheimer’s, dementia, and other obscure conditions. 

Future of Machine Learning

Considering the rapid development of machine learning, it’s no wonder that companies all over the world bet on these technologies. Now that dozens of corporations are becoming AI-first, we will likely see dozens of ML innovations in the next 5-7 years. 

What does the machine learning engineer community expect from the nearest future? These will be the pillars of future technological advancements.

1. Computer Vision

Over the last 3-5 years, the precision of face recognition has increased drastically, bringing scientists from the 26 percent error rate back in 2011 to about 3 percent today. In the future, the quality of facial recognition will definitely improve, making computers powerful substitutes for human eyes. 

The improved quality of computer vision will expand the reach of ML applications in healthcare, security, art, and retail. 

2. Natural Language Processing

Natural language processing (NLP) is another field that will progress at a tremendous pace in the future. The latest and one of most talked about natural language processing algorithms, BERT, helps Google improve the quality of search rankings, ensuring that every Internet user has firsthand access to relevant content. 

The progress scientists are making in NLP will impact the quality of real-time translation to such an extent that we might see human translation becoming obsolete in the nearest future. 

3. Multi-Functional Machine Learning Models

In 2020, most machine learning applications are dispersed and niche. There are separate devices for image recognition, data sorting, or natural language processing. However, similarly to how radio, TV, calculators, and other machines became one in a computer, scientists believe in the idea of multi-purpose machine learning models. 

Such a device will allow users to switch between different machine learning tools depending on the task on hand, similar to how we switch tabs on the web. 

Conclusion

Machine learning is playing a major role in all facets of our daily lives. We already use algorithms to pick our next Netflix binge, tag friends in Facebook posts, and find new Amazon product recommendations. 

From education to healthcare, machine learning algorithms like KNN, gradient boosting, or decision tree have proven to be reliable assistants to humans. In the future, we will feel the impact of innovative technologies on our lives to an even greater extent — and the world is definitely looking forward to these changes.

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