You’re scrolling past your Facebook feed. You come across an advertisement “Our AI-powered solutions will change your world”. You pay no heed to it and continue scrolling, you come across another article; “Nvidia celebrates breakthrough in AI”, you keep scrolling, however soon you realize no matter how much you try to avoid it, AI follows you everywhere. But what is this AI that everyone talks about? What is this rocket science that is out there to change the world?
If you’ve ever been in a situation similar to the one above, then you’ve come to the right place. This article will be clearing all your doubts about the buzzword that is AI.
For those completely indifferent to the technological world around them, AI is an acronym for Artificial Intelligence. The name itself is the best explanation for what the AI industry does.
Let’s start by focusing on the individual terms;
Intelligence is a characteristic found in many living organisms. Intelligence is the ability of a species to make decisions of their own by using their judgments from past experience. These decisions can be wrong, learning from these wrong decisions is the real essence of intelligence.
In the simplest of terms, anything that is a copy /mimicry is called artificial. One thing that must be noted here is that an artificial can try to come close to the original but it can never reach the status of the original.
Both these words, put together, give us Artificial Intelligence. Now that we’ve defined both the keywords, it would be simpler to find a single definition for this term. Putting both these definitions together, we say AI is
“the act of mimicking human intelligence in a non-intelligent entity”
In our case the non-intelligent entity is our computer, we train it to make human-like decisions and do our everyday tasks for us.
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 perform a task 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.
And that's a wrap. That is all you — as a layman — need to know about AI. There are many MANY more technicalities involved in building these complicated networks than what has been discussed but the aim of this article was to introduce you to AI, not teach it.