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What is (not) AI?

Explanation of the difference between AI and non-AI systems.
NEEL DAS: In this video, we’ll try to understand what AI is. We do so by first learning what AI is not. Let me show you an example from medicine, as this is a healthcare course. This device is called the spirometer, and it measures how well the lungs are functioning. The device measures a ratio the Tiffeneau index. If this ratio is lower than a certain cutoff value, the person is said to have a diagnosis of chronic obstructive pulmonary disease, or COPD. Do you think a software that checks the Tiffeneau index against the cutoff value, and provides a diagnosis of COPD makes use of AI? It does not.
Since it makes use of predefined rules, the value must be lower than x, and rules-based systems are not considered AI. However, in the early days, the consensus among researchers was that one could create AI by encoding more sophisticated rules into an algorithm. These were called expert systems, but they never really took off. The main reason was that getting hold of a knowledge expert was extremely costly. In addition, the systems were static, brittle, and could not deal with the ambiguous cases that humans are used to. Today, the term “AI” refers to any algorithm that can simulate intelligence for tasks that seems intuitive to us, such as visual cognition, language processing, planning, and physically manipulating our environment.
One of the most common forms of AI is called machine learning, in which the algorithm automatically learns to perform a task from data collected from prior events without any human specifications. Let us again consider the initial example. Suppose this spirometer provides a diagnosis of COPD for our patient. An X-ray of the patient’s chest has also been made. Now, if you are a trained radiologist, you can see patterns of emphysema in this X-ray. It would be very tricky to develop a rules based approach that could recognise these patterns as well. On the other hand, we could let a machine learning algorithm figure out the problem on its own by providing it with examples of COPD and non-COPD X-rays.
This is considered AI, as the system is trained to infer from these examples by itself. So to summarise, modern AI consists of algorithms that are able to autonomously figure out the solutions to problems without the developer having to define the rules to solving the problem. We already introduced one common way to do so, which is called machine learning.
In the previous step, you have learned about the ways in which artificial intelligence might already be present in your life. However, it might still be hard to distinguish what is AI from what it is not. In this video, educator Nilakash Das explains the difference between the two.

Can you think of more systems that might be considered AI, but are not? Do you still have doubts about some systems and whether they use AI or not? Use the discussion section to share your ideas or questions and to discuss them with fellow learners.

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