Machine learning

This branch of artificial intelligence uses algorithms to look for patterns and make predictions, deciding things like what recommendations to show you.

Arthur Samuel, the inventor of the checkers playing program “Beaver”, is credited for coining the term “machine learning.” He was also an early computer expert at IBM and Carnegie Mellon University.

Machine learning utilizes computers to solve practical problems without necessarily having to write explicit code. Machine learning is what made self-driving cars, practical speech recognition, effective web search, and a better understanding of the human genome possible.

This branch of artificial intelligence uses algorithms to look for patterns and make predictions, deciding on things like what recommendations to show you on Netflix.

Automation and machine learning are ideal solutions in cases where we can apply a data-defined pattern or set of rules. By now, you probably use machine learning so many times a day that you don’t even notice it anymore.

There are two main techniques used in machine learning;

  • Supervised learning: The idea behind supervised learning is to mimic the way humans learn. Supervised learning uses dynamic adjustments to the model, much like humans build knowledge on what they’ve learned in the past.
  • Unsupervised learning: When you’re exploring a large amount of data, unsupervised machine learning can be very helpful. One way to think of unsupervised learning is to imagine that a computer has been placed in a room full of data and is being asked to discover patterns on its own.

Many researchers think the best way for computers to approach human-level intelligence is through a series of stepwise refinements in the development of AI.

Machine translation engines also evolved over time to use machine learning principles instead of a simple word-by-word approach to provide more accurate translations. Localazy includes suggestions from the most popular machine translation engines such as DeepL that utilize machine learning in the background so translators can deliver their work more efficiently.

Further reading #️⃣