AI is a fascinating realm of technology and one that is getting better all the time. This isn’t just because of advances in technology, but also because an AI has the ability to learn. When people talk about “machine learning,” that is what they mean. The term is pretty self-explanatory, but there is more to the story than the meaning of the word.
How Does Machine Learning Work
In order for an AI to learn anything, it has to be fed with data. Obviously, the data should be relevant to the subject at hand and should be as extensive as possible. By analyzing that data, the machine can learn to recognize patterns. The human mind can do this as well, but a machine tends to do it much more quickly.
This is all possible through the use of special algorithms. These algorithms do not necessarily constitute an AI, but they are the basis of AI technology. For instance, most search engines will make recommendations to you. They base these recommendations on the patterns that they find in your search history. This isn’t exactly an AI, but the only difference is its level of complexity.
Using the patterns that they recognize, machine learning algorithms can be used to make decisions by looking for certain patterns and discriminating in favor of certain types. For instance, some companies use these algorithms to process resumes more efficiently. Obviously, a computer algorithm cannot make the subtle distinctions that a human brain can, but they do well at recognizing general trends.
Who Invented Machine Learning?
The first machine learning algorithm was created by a computer scientist named Arthur Samuel. He created a game of computerized checkers that was able to learn from its experiences and improve its skill. Using a respected book on the subject, he was able to train the program to select the best moves in any given situation. Amazingly, this program was able to beat a human who was an expert and a champion at the game.
Building upon this foundation, researchers began to create complex AI neural networks, building artificial neurons based on the known design of the human brain. The early results were not encouraging. However, the early researchers (like Ed Feigenbaum and Julian Feldman) limited their work to two layers of artificial neurons: an input layer and an output layer. In 1986, another computer scientist named Geoffrey Hinton created the first functioning neural net using many layers. His AI model is usually called “deep learning.”
The Three Types Of Machine Learning
There are three different ways in which machines can learn. These are supervised learning, unsupervised learning, and reinforcement learning. All of them have their pros and cons, but all of them have been shown to be effective.
Supervised Learning
Supervised learning is a precise and targeted form of teaching. The machine is fed all kinds of relevant data, and that data is labeled. The machine knows exactly what patterns it should be looking for, and it scours the data to find them. In a limited way, you are engaging in supervised learning every time you do a Google search. Each click of the search button tells the algorithm to find similar results for you, and it gradually builds a pattern.
This is, by far, the most common approach to AI training. In most cases, people want the machine to learn certain things so that it can perform a certain function. As they say, random actions tend to have random results. Thus, we could say that this form of machine learning is the most practical.
Unsupervised Learning
Unsupervised learning is a more wide-ranging approach. When things are done in this way, the algorithm or AI is given a huge set of data without labels. They are not given any specific instructions as to what patterns they should seek. Instead, they are turned loose with the information to make conclusions for themselves. This type of machine learning is far less popular, but it has a number of uses.
Unsupervised learning is very useful for situations in which the algorithm or AI has to deal with new and unknown problems. Obviously, this has applications for cybersecurity. Supervised learning can be used to create many cybersecurity tools. These tools look for known patterns of suspicious activity and act upon what they find. However, unsupervised learning allows the program to find any program of suspicious activity, known or unknown.
Reinforcement Learning
This might be described as a system of rewards and punishments. Obviously, you cannot physically “punish” a machine, but you can force it to learn from an unsuccessful result. The machine is given a certain goal, and it learns by trying many different things. The behaviors that help them to achieve the goal are “kept,” while those that hinder them are “discarded.”
This form of machine learning is even more precise and directed than supervised learning, but it limits the ability of the algorithm to make independent distinctions. That being said, it does offer much of the same versatility that you would get from an unsupervised approach, making it a viable middle-ground approach.
What Is The Turing Test?
This is a term for any test that is meant to answer the question: Does a computer have real intelligence? Essentially, the machine is considered to be intelligent if it can fool a human being into thinking that it is also human.
Conclusion
As you can see, this subject is not quite as complex as you might think. Because machine learning algorithms are based on the structure of the human brain, they function in ways that correlate with common human behavior. Whether you like all of this or not, it is definitely going to be a part of the future. We hope that we have helped to prepare you for that future. We also hope that you will fill out the contact form to receive more information.