Over the past few years, the terms artificial intelligence and machine learning have become more and more common in tech news and websites. But many experts say there are subtle but real differences.
And of course, experts sometimes disagree about what those differences are.
In general, however, two things stand out: First, the term artificial intelligence (AI) is older than machine learning (ML), and second, most people consider machine learning to be a subset of artificial intelligence. For more details, you can check Masters in AI and Machine Learning in Berlin.
Artificial Intelligence vs. Machine Learning
Although AI has many definitions. The one in “the field of computer science is to solve scientific problems. Problems related to human intelligence, such as learning, problem solving, and Pattern identification “, in short, idea. That machines can be intelligent.
The heart of the artificial intelligence system is its model. A model is nothing more than a program that improves its knowledge through the process of learning by observing its environment. This type of learning-based is supervise learning. There are other models that fall into the category of untrained learning models.
The phrase “machine learning” is also from the middle of the last century. In 1959, Arthur Samuel defined ML as “the ability to learn without explicitly programmed.” And they created the Computer Checkers application, one of the first programs that could learn from its errors and advance its presentation over time.
Like AI research, ML was out of popularity for a long time, but it became popular again around the 1990’s when the concept of data mining began. Data mining uses algorithms to find patterns in a given set of information. ML does the same thing, but then goes one step further – it changes its program behavior based on what it learns.
One of the most popular ML applications recently is Image Recognition. In other words, humans must look at the set of images and tell the system what is in the image. After thousands and thousands of repetitions. The software learns which pixel patterns are typically associated with horses, dogs, cats, flowers, trees, houses, etc., and can make a very good idea of the content of the images. Is.
Many web-based companies also use ML to power their referral engines. For example, when Facebook decides what to show in your news feed. When Amazon highlights the products you want to buy. And when Netflix recommends the movies you want to watch. They are all based on recommendations.
Artificial Intelligence and Machine Learning Frontiers
- Deep Learning,
- Neural Nets
- Cognitive Computing
Of course, “ML” and “AI” are not the only terms associated. IBM often uses the word “cognitive computing”, that is to some extent synonymous with artificial intelligence.
However, some other words have very unique meanings. For example, an artificial neural network or neural network is a system to process information. It is similar to the way the biological brain works. So, the two terms are sometimes confusing.
In addition, neural nets provide the basis for deep learning, a special type of machine learning. Deep learning machine uses a fixed set of learning algorithms that run in multiple layers.
Computer scientists continue to debate their exact definitions, and perhaps for some time to come. And as companies continue to invest in artificial intelligence and machine learning research. It is likely that some more situations will arise to complicate matters further.