Video Transcript (English)

Congratulations on completing this unit. In this unit, you were introduced to the Artificial Intelligence ecosystem. You also had the opportunity to learn about the transformer model which revolutionized AI.

Let’s recap what we covered.

First, we covered a few important components that make up AI.

Starting with Artificial Intelligence which can be defined as a field in computer science that trains computers to simulate human intelligence.

Next, we learned that Machine Learning is a field of AI that works on creating algorithms and models that allow computers to learn from vast amounts of structured data.

Then we talked about deep learning which tries to replicate how humans learn in machines.

This led us to generative AI which creates text, images, video, and audio based on a given prompt.

Finally, we learned that Natural Language processing gives computers the ability to understand and work with human languages.

Afterwards, we took a brief look at what neural networks are and how they have evolved over time.

We also learned that the transformer model consists of encoders and decoders. Both the encoder and decoder have multiple layers inside of each that are used to calculate predictions.

The encoder takes in an input and returns a sequence of numbers, often referred to as embeddings.

On the other hand, the decoder receives the embeddings from the encoder and the previously generated output embeddings. With this data, the decoder makes predictions based on the context provided by the encoder.

We also explored how we can use individual parts of the transformer model to achieve different solutions.

Sequence to sequence models use both encoders and decoders. They excel at taking input and generating a sentence based on that input.

Encoders take input and generate embeddings. They also have bidirectional attention, so they can examine text that comes before and after the target section.

Finally, decoders take embeddings and generate output based on the context provided by embeddings. They are unidirectional, so they can only examine text before the target section.

Now that you know the basics of AI, you might want to check out the units on Atlas Vector Search.

You can also visit MongoDB’s documentation on Atlas Vector Search.

Until next time, keep learning!