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AI in Computer Science
Enough with the layman terms, let’s look at AI in a little more depth. AI has 3 sub-categories which are as follows;
- Machine Learning;
This form of AI works by exposing the machine to data along with its labels. The labels associated with the data tell the machine what the data represents. When we — humans — have to learn something, our best approach is to revise that certain thing again and again until our brain masters it. Similarly in Machine learning, data is shown to the machine in several iterations until the machine understands the data and is able to associate labels with similar data without any external help.
2. Deep Learning;
At its core, Deep Learning is very similar to Machine Learning however, Machine Learning is only effective when we have a simple problem at hand — with simple data features and moderate data points. For complex problems, we have to shift towards neural networks. Neural Networks are designed to mimic the neuron activity inside our brain. Our brain stores information in the form of electrical signals in networks formed by these neurons.
Similarly Neural Networks store information in the form of weights and each neuron stores a different aspect of information.
3. Reinforced Learning;
This method is very similar to how we learn stuff in our younger years. We do something and then receive an award or punishment depending on what we did was correct or not. Computers can also learn in the same way; on a correct action, the machine is notified of the correct action in the form of a positive or negative notification.
Although these are the official categories for artificial intelligence techniques, we can unofficially span the definition of AI and include a few more cases. Since we have established that AI is aimed and reducing or removing human effort, therefore any software and coding practice that reduces this effort — including but not limited to conditional statements — can be encompassed within the bounds of AI.
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