Introduction to AI and Reinforcement Learning
Introduction:

Recently, I’ve been going down an AI Rabbit Hole. It started out by just watching Youtube videos and reading articles, but eventually, I came accross a facet of AI that really, really intrigued, not just because of how interesting the concept is, but also because of the potential uses it may have. It’s known as Reinforcement Learning.

Overview of Artificial Intelligence:

To start, we need to understand what exactly Artificial Intelligence (AI) is. AI is a pretty blanket term — it’s the science of making intelligent machines, especially computer programs. We’re going to go over a subset of AI, machine learning.

Machine learning is basically using a computer system to make accurate and reliable predictions/readings/interpretations of data that the system has been given. The first two types of machine learning were supervised learning and unsupervised learning. (*This is simply an overview of each system — I don’t go too in depth when it comes to the tech used for such methods)

Supervised Learning

Supervised learning is a task-driven form of machine learning. Labelled data is used to train the system, and when new data is presented, the system will be able to make a prediction.

For example, you can feed a system images of cows and chickens, and over time, it will be able to make distinctions. The system creates a function on a graph based on the data in order to predict what output a given input will lead to — you can learn more about how it actually works here. Supervised learning is used for pattern recognition, spam detection, object recognition, etc.

Unsupervised Learning

Unsupervised learning, unlike supervised learning, is data-driven. The system isn’t set out to recognise anything, but to observe. In unsupervised learning, the system is given raw data, and needs to figure out patterns in the data. Essentially, it finds patterns in data and groups said data accordingly.

For example, say there are five red data points and five green data points. The system will recognize that the data points differ in color, and will group them accordingly. Say we add five blue points, and give each of the now 15 points a random number from 1–3. The system will recognize all of these patterns.

Unsupervised learning is mainly for clustering problems, or grouping data. It is also used for anomaly detection — that is, noticing inconsistencies in datasets. This is especially useful for things such as bank transactions.

These were the first two types of machine learning, and they’re both well and good, and pretty useful — but, as Grand Master Yoda said, “There is another.” The third type of machine learning, and perhaps the most interesting (to me, at least), Reinforcement Learning.

Introduction to Reinforcement Learning:
['Imran Iftikar']
['Artificial Intelligence', 'Reinforcement Learning', 'Technology']