- [Instructor] It is my belief
that there's a lot of confusion around the term AI,
especially with all the buzz and hype
we're seeing in the tech world right now.
Let's clarify what AI actually means
before seeing it in action.
In this section, we'll focus on machine learning
and large language models,
two concepts that power most of the buzz.
By grounding ourselves here,
we'll cut through the hype and build a solid framework
to understand the rest of this course.
Much of today's excitement and funding
and press coverage
centers around one branch of AI,
large language models, which drive generative workflows.
Understanding LLMs helps us see
why chatbots, code assistance,
and creative tools have exploded onto the scene.
LLMs are a type of machine learning model.
The key feature of any machine learning model
is that it discovers patterns from data,
rather than being explicitly programmed.
These models can be trained on all sorts of data,
images, audio, tabular data, and text.
Training data comes from anywhere
there's a structure waiting to be learned.
What makes LLMs unique
is that they're trained on massive collections of texts,
like books, articles, scientific papers, or webpages.
This training allows them to understand,
generate, and manipulate human language
by predicting patterns in words and meaning.
When you prompt a model with new input,
it generates original content based on what it has learned.
Now, when I say original, it's important to clarify.
The model isn't inventing ideas from scratch
or thinking independently.
Instead, it's generating responses by identifying patterns
and relationships in its training data.
So while the output often feels new and creative,
it's really a sophisticated remix
of what the model has already seen.
LLMs don't create in the human sense.
They synthesize existing patterns into fresh combinations.
We still call them generative
because they produce novel outputs on demand,
but there's no independent thought behind the scenes.
That distinction helps set realistic expectations
around accuracy, creativity, and copyright.
This ability to understand
and generate natural language makes LLMs especially powerful
for text-based systems like chat.
Instead of rigid, rule-based responses,
LLMs can carry on fluid, context-aware conversations
that feel human-like.
They adapt to the flow of dialogue,
remember context within a session,
and generate replies that often seem relevant and nuanced.
That's why LLMs are the backbone of services like ChatGPT.
They provide the powerful models
enabling back-and-forth dialogues
that go far beyond canned replies.
That said, it's important
to be clear about what's happening under the hood.
When we interact with ChatGPT,
we provide text instructions and prompts,
and the model uses its training
to synthesize a text-based response.
It is generating answers by identifying
and combining patterns it has learned during training.
So while the conversation feels natural,
it's ultimately the result are prediction and synthesis,
not independent thought.
It doesn't know facts like a human.
It assembles plausible answers from patterns it has seen.
LLMs aren't just powering chat systems.
They're also transforming how we write and work with code,
because code is fundamentally structured text.
LLMs are uniquely suited to generate, refactor,
and even explain our code.
They treat programming languages like any other language,
learning syntax patterns and idioms
from vast amounts of open-source repositories,
documentation, and developer Q&A.
The result is a super-powered auto-complete,
creating complete functions, tests,
or inline comments at your fingertips.
A great example is GitHub Copilot,
which uses these capabilities
to act like an AI pair programmer.
And instead of simple auto-complete,
it provides context-aware suggestions and other assistance.
There are other development tools
following the same pattern,
embedding LLMs to help with bug detection,
code explanation, and refactoring.
Earlier I mentioned that LLMs generate answers
by recognizing and combining patterns.
That means we have to be cautious about originality
because the text isn't truly human-created.
The same idea applies to generated code.
While this course focuses on the amazing ways
we can use GitHub Copilot and Visual Studio,
we need to remember,
the code is generated from patterns in the training data,
it may not always be the exact solution we need,
we need to review, test, and adapt the output, as always.
This is essential.
So while AI tools are powerful,
we still have to apply our judgment as developers.
When most people hear AI today,
they immediately think of generative tools,
systems that can write code,
create images, or produce text on demand.
And while those tools are exciting,
there are only one piece of the story.
There are plenty of other developer tools
that rely on AI for analysis, prediction, and assistance,
rather than producing new code or content.
That's what we'll look at in the next video.