- Everyone is using large language models
or LLMs nowadays, pretty much everywhere, right?
Whether you're using chatbots like ChatGPT, or cloud
or grok, or applications that are now using AI
for many different implementations.
So from search engines like perplexity
or traditional ones like Google
or even your operating system, right?
AI is used everywhere, right?
So these are transforming the way
that we also interact with computers.
These advanced network networks,
they are trained on very large amount of data
and have definitely revolutionize
natural language processing or NLP,
and definitely open up for new possibilities
across pretty much all industries in there,
whether it is medicine, cybersecurity, programming,
software development, and so on.
LLMs represent a significant leap forward
in AI capabilities,
unlike their predecessors, which are often limited
to a specific task, definitely LLMs can understand
and generate human-like text and images.
And, of course, it's transforming the way
that we're creating audio, images, many other,
you know, elements, even movies nowadays, right?
So this versatility has made them
definitely invaluable tools in areas like content creation,
customer service, data analysis,
and, of course, software development.
The way that we are creating software nowadays
has accelerated significantly
because of AI-driven tools like GitHub copilot, Cursor,
and many others out there.
One of the most notable aspects of LLMs is their capability
for a few shot and zero-shot learning.
This means that they basically can perform different tasks
with minimal or no specific training,
and that adapts them, you know, under versatility
with many different situations.
And you can, of course, you know, have this flexibility
to dramatically reduce the time
and resources that are needed
to deploy these AI solutions in many different domains.
That's one of the most crucial elements
of today's environment, is that in order for you
to not only use AI,
but create AI applications, you know,
AI-driven applications, you don't have to, you know,
be a triple, you know, PhD in data science
and have a data science lab
that is extremely, you know, costly.
You actually can use AI
and a lot of libraries that allows you
to actually create AI-driven applications nowadays.
Now, I personally author a few books
about artificial intelligence
and how it's impacting things like cybersecurity,
networking, and also programming,
and also how the rise of LLMs and SLMs,
and we're gonna talk about SLMs in a second,
but also agentic implementation.
So agents can also bring new challenges
and considerations, especially in the world of security.
And we're gonna be covering that of course,
later in the course.
Now, as these models
and implementations become more powerful
and widely adopted, issues with security, with bias,
misinformation, and ethical, you know,
elements are extremely important nowadays,
and that's why you see a lot of activity
for many regulations around the world.
Now, let's switch gears a little bit
and introduce the concept of small language models
or SLMs basically as you learn in just a second ago, right?
The large language models are, you know, trained on a very,
very large amount of data, right?
So both SLMs, so small language models
and large language models,
or LLMs, are both types of artificial intelligence systems
that are designed to process
and generate human-like language code and so on.
But they differ in scale, and
they're very different in the capabilities
that they have and they applications.
So, for example, LLMs, as the name suggests of course,
are massive neural networks trained
on many different types of data, right?
Whether it's code, different books, you know,
online resources, et cetera.
And these are models like GPT, Gemini, Grok,
Claude from philanthropic and many others, right?
So they contain billions
or even trillions of parameters, right?
So their size allow them to capture a lot
of complex language patterns and understand context
and generate, you know, of course,
those human-like text across a wide range of topics,
not only text, of course, you know, images and code
and audio and so on, right?
So now these LLMs,
they excel in task requiring broad knowledge
and flexibility.
However, they also require
substantial computational resources
in order for you to run them,
and they can be very challenging to deploy
in resource constrained environments, right?
The small, especially in edge environments or on-premise.
So small language models are way more compact.
And there many people, you know,
many companies also in the industry
are creating these models.
A prime example of that is Microsoft, five models
on where they are designed.
So these SLMs are designed to be efficient
and more specialized when focusing on a specific task
or a specific domain.
So the advantage of SLMs, of course,
include faster inference times, lower memory requirements,
and also easier deployment on edge devices
or environments with limited resources even, you know,
you can deploy them at home, right?
They're very well-suited for domain specific tasks,
as I mentioned to you.
So in the landscape of AI, both LLMs
and SLMs, you know, have their role, you know,
the important role to play,
and they also play an important role
in the world of security, right?
Later in the course, you will learn about how
to secure them, both SLMs and LLMs
and their applications, their underlying applications
that we are nowadays, you know,
developing on a permanent basis.
You're gonna be learning
about retrieval-augmented generation,
you're gonna be learning about vector databases, how
to secure these environments, you know,
LLM caching many different techniques throughout the course
and how to secure those implementations.