Please note that in this article I will mainly talk about Supervised Learning.

Introduction

Artificial Intelligence is becoming more and more widespread. There are many people wondering what field AI will take over next. It is said that in the next 50 years AI will take over most jobs. There is AI being developed for nearly every field in existence. There is AI being developed for healthcare, writing, transportation, and many more! Yet, many people don't know much about this "AI". So let's dive in and see the basics.

What is AI?

AI or artificial intelligence is exactly what its name says it is. It is human-made intelligence (for computers). Unlike regular programs that need to be explicitly told what to do in any situation with code, AI will learn from past experiences and training data.

Uses for AI

AI can be used in a lot of places.

For example, there are people training AI to classify if a tumor is malignant or benign.

Malignant Definition:
(of a tumor) tending to invade normal tissue or to recur after removal; cancerous.

Benign Definition:
(of a disease) not harmful in effect.

As many people know, Tesla is using AI to develop their full self-driving that can respond to other cars, traffic lights, traffic cones, and other driving signals. There are hopes that in the future AI can be used in trucks to make shipping more efficient and quicker.

AI is also used in social media. It tracks your activity and recommends you your feed based on that.

AI is also used in voice assistants like Alexa, Bixby, Google Assistant, and Siri. AI is used to try and interpret what you are saying. For example, Google Assistant needs to give you the weather regardless of if you say "Hey Google, what's the weather?", "Ok Google, what is the temperature?", or "Hey Google, is it going to rain today?". So to interpret your actual intentions behind the sentence is where AI comes in.

How does AI work?

I won't get into the math or specifics on how AI works, but I'll give you an idea of how it works.

After an AI is built, it needs to be trained. Like humans in a new job, we need to be trained and have the experience to perform well. Same for AI. But unlike humans, AI just needs data and time to train.

For example, let's say you built an AI to determine, from an image of a hand, if the hand was opened or a fist (closed). Well, first you need to build said AI. Then you need to find or make some training data. In this situation, you would need data that has pictures of open and closed hands. Generally, AI needs thousands of examples and hours to train itself. This is why data scientists generally use large datasets already filled with related data.

Data is the fuel for AI. The less data an AI has to train off of, the worse it is. This is why high-quality, accurately labeled data is very important.

Biased Data

This is where a bad problem arises. This problem won't directly make the AI stop functioning, but will cause a bunch of problems.

So let's go back to our open-hand VS closed-hand AI. Let's say we only gave it open-hand and closed-hand images where the inside of the hand is facing the camera. The AI would be amazing at recognizing open and closed hands only if the inside of the hand is facing the camera. Suddenly, if we closed our hand into a fist and turn it around such that the camera sees our knuckles and the outside of our hand. The AI's confidence would plummet.

Confidence Definition:
(in AI) How sure an AI Model is of its prediction.

This is why when we give the AI data, we need to be sure we give it images of open and closed hands from all angles to ensure it can detect an open or closed hand from any image.

Types of AI

There are many types of AI. We will cover the three main ones today. They are Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Supervised Learning

Supervised Learning is what we've been talking about today. It requires pre-labeled data and will train itself by making predictions and seeing if it was right. It takes in new data and uses what it learned from the training data to make predictions.

Unsupervised Learning

Unsupervised Learning will take in data and will cluster them into groups based on similarity. It will then take in new data and try putting it into existing groups.

Reinforcement Learning

Reinforcement Learning doesn't need much data and mainly learns based on what actions receive penalties and what actions receive rewards. Think about Reinforcement Learning as learning a new video game without watching YouTube videos beforehand. You may start off with X action, but that caused you to lose a life (penalty). So you do Y action instead and you passed the level (reward).

Risks

So as you can see AI has a lot of potential to help the world. But it can also be harmful.

We have already seen that AI can be used to improve health care, transportation, entertainment, etc. But it can also harm those same fields.

Let's take self-driving cars. If we use AI in cars, the roads will be a lot safer as the AI cars will follow traffic laws and sense dangers. But let's say a self-driving car crashes into another car, this will injure people. It also arises the question of who is responsible for AI's mistakes. Like if an AI car crashes, is it the AI car's driver's fault, the car company's fault, or the opposing driver's fault?

Let's look at healthcare. What if we have an AI to see if a tumor is malignant or benign and its prediction is a false negative (the tumor is predicted benign while it is actually malignant)? Then the patient's tumor will go untreated and can cause major body problems. All because an AI misclassified a tumor.

These are some of the risks of AI. What if AI misclassifies? Who is responsible?

Conclusion

In the end, AI is a very helpful tool and can save lives. But it also needs to be heavily tested and monitored to make sure it doesn't hurt lives.

Thank you for reading (or listening)!