Machine Learning Fundamentals and AI (the 3-minute download)

More and more these days we are hearing buzz around ChatGPT, how to use AI responsibly and whether AI will be taking over the world. Companies are rushing to get ahead of the AI race by heavily investing in infrastructure and companies like NVIDIA.

But at the core of it what is Machine Learning and is the same as AI? I have often heard these terms used interchangeably but are they really the same?

Turns out, not really.

As programmers, we have to build specific instructions to produce a desired outcome. With machine learning, we feed a model lots of data so that it can understand the patterns that best describe our data, without explicitly programming those patterns. These algorithms can learn and improve as they receive more data.

Machine Learning is a subset of AI and can be divided into the following categories:

• Supervised Learning
• Unsupervised Learning

Supervised Learning

• Is a type of machine learning where labeled data is used to train a model. For example, face recognition tagging on various platforms trains a model to recognize the faces of different individuals, enabling it to tag photos accurately. The more we train this algorithm, the more accurate it becomes.
• Regression and Classification are two common types of supervised learning tasks. Regression predicts continuous values, such as housing prices based on factors like size and location, while Classification assigns labels or predicts discrete values (i.e. this is an alien or a human?).

Unsupervised Learning

• On the other hand, Unsupervised Learning doesn’t receive explicit information about the expected output or labels. Instead, it analyzes the data on its own and attempts to identify patterns, similarities, or structures within it. Clustering (which involves grouping patterns into clusters) and dimensionality reduction (technique that simplifies data by reducing features or variables) are common tasks in unsupervised learning.

As the buzz around AI continues to grow, it is important to understand the distinction between AI and machine learning. While AI is a broad field encompassing various technologies, machine learning is a subset that focuses on algorithms that learn patterns from data. I will be eagerly watching for new developments in this space.