Truly excellent explanation, it will definitely make you somewhat AI savy in regard to understanding how it came about and how it now works. Note also that this is my own and AI-based summary and not this video’s transcript. A must watch!
Exploring the differences and connections between AI, machine learning, and generative AI technologies—and how they are changing the world around us.
In today’s world, it seems like everyone is talking about Artificial Intelligence (AI). But what exactly is AI, and how is it different from terms like machine learning and generative AI? These technologies are often used in everyday life, from the chatbots we interact with online to the autocorrect that finishes our sentences. Let’s break down these concepts in simple terms, so you can understand how these technologies are shaping the future.
What is Artificial Intelligence (AI)?
AI, or Artificial Intelligence, is a broad term that refers to machines or computers that are designed to perform tasks that would normally require human intelligence. Some examples of these tasks include understanding language, recognizing patterns, and making decisions. Think of AI as a robot brain—a computer that tries to “think” like a human. However, AI doesn’t necessarily mean a robot that walks and talks. It can be a simple program, like the system that recommends movies on Netflix or detects spam in your email.
The idea of AI isn’t new—it’s been around since the 1950s, though back then, it was mostly theoretical. Early AI researchers hoped to create computers that could solve complex problems, like playing chess or solving math equations. Over time, AI technology has improved and evolved. Now, it’s everywhere, from our smartphones to medical diagnostics.
Machine Learning: A Subset of AI
Machine learning is a special type of AI that lets computers learn from data. Instead of programming a computer with specific rules, machine learning allows the computer to “learn” from examples. Here’s an easy way to understand it: Imagine you’re teaching a computer to recognize pictures of cats. Instead of telling the computer, “A cat has whiskers, pointy ears, and a tail,” you show it thousands of pictures of cats and non-cats. The computer studies these images and, over time, learns to recognize patterns that tell it what a cat looks like.
The more examples the computer sees, the better it gets at recognizing cats (or anything else). This is how machine learning works. It’s all about feeding the computer lots of data so it can make predictions, spot patterns, or even identify things that are different or unusual (like when it detects spam emails that don’t look like regular ones).
A Real-Life Example of Machine Learning
Let’s say a credit card company is trying to spot fraudulent purchases. Every time you swipe your card, the company’s machine learning algorithms analyze the transaction. The system knows what your normal spending habits look like. For instance, you usually buy groceries at the local store. But what if your card is suddenly used to buy expensive jewelry in another country? The machine learning system recognizes this as unusual (an “outlier”) and might flag the transaction as fraud, preventing the card from being used further until you confirm it’s really you.
This kind of pattern recognition makes machine learning incredibly useful for businesses like banks, social media platforms, and even healthcare providers. Whether it’s detecting fraud, recommending a new song, or finding an abnormality in a medical scan, machine learning helps machines “learn” without being explicitly programmed to make every decision.
Deep Learning: A More Complex Type of Machine Learning
Now, let’s talk about deep learning. While machine learning is about teaching computers through data and patterns, deep learning takes this a step further. Deep learning uses something called neural networks, which are inspired by how the human brain works. These neural networks have multiple layers, and each layer processes information in a way that helps the computer understand complex patterns better than regular machine learning.
For example, let’s say you’re trying to teach a deep learning system to recognize faces. The first layer of the neural network might focus on recognizing simple features like edges or shapes. The next layer could look for eyes, noses, and mouths. Finally, the deeper layers would combine all these features to recognize a full face.
Because deep learning models are more advanced, they can tackle more difficult tasks—like identifying objects in videos or understanding speech. But the downside is that deep learning models are often harder to explain. Even the engineers who build these systems can’t always tell exactly why a deep learning model makes a specific decision because the network is so complex.
Generative AI: Creating Something New
One of the most exciting and talked-about advancements in AI is generative AI. Unlike regular AI, which typically makes predictions or identifies patterns, generative AI can create entirely new content. This means it can generate text, music, images, or even deepfake videos (where someone’s face is altered in a video to make it look like they said something they didn’t).
A famous example of generative AI is ChatGPT. ChatGPT is a type of large language model, which means it can generate text by predicting the next word, sentence, or paragraph based on what it has learned from vast amounts of data. For instance, if you ask ChatGPT to write a poem, it can generate one that sounds pretty original. Although it’s technically combining patterns it has learned from other texts, the result feels new.
Generative AI isn’t just for fun. It’s being used in industries like marketing, where AI tools can write product descriptions or create advertisements. It’s also being used in education to help students with writing assignments or to summarize long articles.
Foundation Models: The Backbone of Generative AI
Generative AI is powered by something called foundation models. These models are trained on a large amount of data, which allows them to understand and generate different kinds of content. For example, a large language model (like ChatGPT) is a type of foundation model that focuses on understanding and generating text. There are also foundation models for other types of content, like images, music, and videos.
Foundation models have transformed AI adoption. A few years ago, AI was mostly limited to research labs or big tech companies. But now, because of foundation models, AI is more accessible than ever. People and businesses of all sizes are using AI to solve problems, generate new content, and even entertain.
The Risks and Benefits of AI
AI has tremendous benefits, but it also comes with some risks. For instance, deepfake technology, which uses AI to manipulate videos, can be both entertaining and dangerous. While it can be used for creating realistic characters in movies, it can also be used to create fake videos of people saying things they never said. This has raised concerns about the spread of misinformation.
There’s also the issue of bias. Since AI systems learn from data, they can sometimes pick up biases present in that data. For example, if an AI is trained on biased data, it might make unfair decisions, like recommending fewer loans to certain groups of people.
On the other hand, AI has the power to revolutionize industries. It can help doctors diagnose diseases, create more personalized learning experiences for students, and even reduce waste in manufacturing. In the environmental field, AI is being used to track wildlife populations, predict weather patterns, and reduce energy consumption.
Conclusion: AI Is Here to Stay
In just a few decades, AI has gone from a futuristic concept to a part of our everyday lives. Whether we’re interacting with chatbots, getting movie recommendations, or benefiting from improved healthcare, AI is all around us. As these technologies continue to develop, we will see even more exciting innovations that will change how we live and work.
However, it’s important to remember that with great power comes great responsibility. As AI becomes more advanced, we must ensure it’s used ethically and for the benefit of everyone.
Content summarized from video in post!
