Video Transcript (English)

With all the buzz around Artificial Intelligence, it’s easy to get lost in the technical jargon. Let’s take a moment to step back and learn about some of the components in the AI ecosystem.

In this lesson, we’ll demystify Artificial Intelligence by defining the following components: Machine Learning, Deep Learning, Generative AI, and Natural Language Processing.

What is the first thing that comes to mind when you hear the word AI?

Is it a computer learning a task?

Maybe it’s prompting a computer to answer a question you have?

Or perhaps it’s killer robots taking over the world?

All of those are correct answers. Well, except maybe the killer robots, but they do make for a great movie.

To be more precise, Artificial Intelligence is a field in computer science that trains computers to simulate human intelligence.

We can use AI to do all kinds of things, such as: Enhanced healthcare diagnostics, Autonomous driving, Media generation, Repetitive tasks, Locating precious resources, and much more.

Ideas that once seemed like science fiction are now not so far out of reach.

Maybe one day we’ll walk side by side with robots and make things better together.

But to enable computers to emulate human intelligence, we must train them to do so. This is where Machine Learning and Deep Learning come in.

Machine Learning is a field in AI that creates algorithms and models to help computers digest vast amounts of data, which can be structured or unstructured.

That sounds cool but what are algorithms and models?

You can think of algorithms as a finite sequence of well-defined, computer-implementable instructions.

Learning happens when we provide the computer with a set of instructions and large amounts of data so it can identify patterns.

An algorithm dictates how the learning process happens, and the outcome of this learning process is a model.

The model encapsulates what the computer has learned so it can use that knowledge to make predictions about new data.

We see examples of machine learning all the time in our everyday life, such as: Email spam filtering, facial recognition, and predictive text on your phone.

Next, we have deep learning which teaches computers to process data like a human.

The algorithms used for deep learning are artificial neural networks.

Neural networks are made up of multiple layers of nodes and their connections which are used to run different calculations.

Deep learning uses multiple layers of neural networks, which is why it is called “deep”.

The neural networks are designed to be like your own brain.

Your brain has billions of neurons with trillions of connections all sending signals to each other. The connections enable you to make complex decisions with very little effort.

Deep Learning algorithms are used on vast amounts of unstructured data such as video, audio, pictures, and much more.

Deep learning produces a variety of models, including large language models, or LLMs, which specialize in processing text data and generating human-like text.

These models can recognize complex patterns, understand context, make predictions, and more.

Deep learning also creates other types of models that are used for tasks beyond language processing, such as image recognition, object detection, and playing complex games.

Like machine learning, we can see examples of deep learning in our everyday life, such as: Improved facial recognition, anomaly detection, and digital assistants.

Speaking of digital assistants, that brings us to generative AI.

Generative AI leverages large language models to create text, images, video, and audio based on a given prompt.

Some examples are ChatGPT, DALL-E, and Llama live.

When using these tools it can often feel like we’re interacting with another human.

This is where Natural Language processing or NLP comes in.

NLP gives computers the ability to understand and work with human languages.

This has always been a huge hurdle for AI since computers and humans don’t naturally speak the same language.

Newer chatbots are a great example of NLP. We can interact with chatbots as if they were another human. You can ask the bot a question in whatever language you speak. It will then interpret that question and try its best to give a relevant response.

As you can see, Artificial intelligence is made up of many different fields. This was only a small portion of them.

To recap, we learned that artificial Intelligence is a field of computer science that trains computers to simulate human intelligence.

And there are many fields within artificial intelligence.

Machine learning uses algorithms to train models based on data.

Deep learning uses artificial neural networks to simulate how the human brain works.

Generative AI leverages machine learning models to create text, images, video, and audio based on a given prompt.

Finally, we learned that natural language processing gives computers the ability to understand human language.

Next up, let's take a closer look at neural networks and how they led to the modern transformer model. See you soon!