AI and Machine Learning are two correlated areas that would be somewhat worthless without each other. Their existence makes our life better in many ways. But what are their key differences?
Read on to find out!
Before we proceed to the main part of the article, it is necessary that you understand the meaning of data science. In a nutshell, this is a field of study that specifies in extracting structured and unstructured data. The raw information is processed and formed in a way that is comprehensible enough to work with.
As everyone already knows, the main idea of artificial intelligence is to attempt to grant machines the “human mind”. It can be implemented in literally any field: games, banking systems, security, etc.
Artificial intelligence focuses on the creation of smart devices that would be capable of doing things significantly better than humans. The best example of the achievements of this field is Tesla cars that can drive on their own. Like, why not?
To develop AI, programmers use different frameworks like TensorFlow, Chainer, and an enormous number of others. In fact, there are hundreds of technologies that are used.
If artificial intelligence is a totally separate field, then machine learning is simply one of the major fields within AI. The core task of this area is to “teach” computers and devices to act like real people.
Programmers do not have to code. Here, the data is sent within an algorithm that analyzes the information and tries to build a logical chain. Basically, machines are taught to self-program and learn autonomously.
For example, in many shops and services, machine learning is used to form a better recommendation list. The computer sees what you search for and what you like, then it attempts to find suitable products in accordance with your previous interests.
First of all, let us look at artificial intelligence. It tries to act human-like, and it often may be very difficult to distinguish it from a real person.
Now, let’s check out machine learning. As we already know, it is a part of AI that specializes in the way machines acquire new knowledge, form conclusions, and make decisions.
It is not necessary for both to be separated from each other. In most cases, these two options work together to provide the best results. For example, Siri is AI-based but uses machine learning for better recommendations and specified results.
In the case of data science, it requires strict analytical evidence and processes literally all kinds of information. With the help of data science, people can solve various mistakes that occur during the work of artificial intelligence.
For example, imagine a situation where an AI-powered car does not react to a stop sign. This could be a one-time error but it can happen again. Data scientists analyze the information they have and see that the mistake happens at the same time period.
After that, machine learning is supposed to help solve this problem. It shows photos of signs that were made at this specific time period for AI to identify them. Then, artificial intelligence should start recognizing them.
The labor of AI developers is respected both in terms of words and payment.
According to Glassdoor, an average salary of an artificial intelligence developer in the United States is $76,526. This is very close to what software developers make.
However, there are many companies that pay over $130,000 per year. These include Electronic Arts, Boston Consulting Group, Target, IBM, and others.
Speaking of ML experts, their average salary per year appeared to be $114,121.
Companies like Roku, Hover, Rise Technologies, Stipe, Arrivalist, Target, Glassdoor, and Roblox are willing to pay over $200,000 per year for a high-qualified specialist.
As you can see, AI and machine learning are very close. The main thing that connects both is data and their need for it to operate properly. Therefore, if you want to become a specialist in one of these fields, you will have to learn about all the correlated areas.