Aren’t AI and machine learning basically the same thing? Even though they are often used in a similar context, they don’t have the same meaning. Let’s look at the difference between these two terms.
While AI is a vast scope of many different intelligent technologies which can do a various number of things, machine learning is actually one of the processes within AI. And you could say that machine learning is what makes modern AI so smart.
Since AI is made to model human intelligence, one important aspect of human intelligence is our ability to learn. The basic principle of machine learning is programming machines in a way that instead of having to be taught everything step by step, they can learn through experience. This means that they could figure out how to do different tasks alone by observing, trying and learning from their mistakes.
Machine learning can be described as a field of computer science that gives computers the ability to learn without being explicitly programmed, but based on data.
We can therefore say that AI learns through examples and lots of repetition and feedback loops. It is important to remember that AI is only as good as the data it learns from!
When being programmed for something, machines are generally given a certain algorithm that tells them how to do a task. They then follow this algorithm and don’t do other things or use the data in any different way.
Machine learning, in contrast, uses a different form of algorithm that allows machines to work outside of just one specific task, and learn how to do different things from the data they have access to, performing tasks they are not programmed to do, and improving their accuracy and effectiveness over time. The algorithms here are aimed at finding patterns in massive amounts of data and using them to make better decisions and predictions as time passes and more and more data is processed.
One very wide use of machine learning is image recognition, in order to process a big number of images and sort or classify the objects in them, learning and improving throughout the process, or any other wide range of uses. Another great practical example is the use of machine learning in medical diagnosis. It can help medical professionals in the analysis of symptoms and clinical parameters of diseases in order to make a possible prognosis and prediction for the patient, as well as therapy planning and patient monitoring.
Machine learning is not the same as AI, but it is a very important part of AI, which could lead to great innovations and improvements as it grows. As different models of machine learning develop and become stronger and more efficient, AI could move towards accomplishing more and more complex tasks independently.
https://www.roboticsbusinessreview.com/ai/3-basic-ai-concepts-explain-artificial-intelligence/
https://medium.com/swlh/ai-for-beginners-a-high-school-students-explanation-4014c65675c
https://www.ibm.com/cloud/learn/machine-learning
https://www.sas.com/en_us/insights/analytics/machine-learning.html
https://bigdata-madesimple.com/top-10-real-life-examples-of-machine-learning/