AI, Machine Learning, Deep Learning and Generative AI Explained

Summary

This video script delves into the distinctions between artificial intelligence (AI), machine learning, and deep learning, clarifying misconceptions and exploring their interrelations. It highlights the evolution of AI from its early days to the rise of generative AI, including large language models and deepfakes. The script emphasizes the exponential growth in AI adoption due to foundation models, which have transformed our interaction with technology, generating new content and summarizing existing information for easier consumption.

Takeaways

• 🧠 Artificial Intelligence (AI) aims to simulate human intelligence with a computer, encompassing the ability to learn, infer, and reason.
• 📚 The concept of AI has been around since the early days of computer science, with early work in languages like Lisp and Prolog leading to the development of expert systems.
• 🤖 Machine Learning is a subset of AI where machines learn from data without being explicitly programmed, becoming adept at pattern recognition and prediction.
• 🔍 Machine Learning became more popularized in the 2010s, with its ability to spot outliers making it particularly useful in fields like cybersecurity.
• 🧠🔽 Deep Learning involves neural networks with multiple layers to simulate the human brain's operation, becoming a significant advancement in the 2010s.
• 🌐 Generative AI is the latest advancement, focusing on creating new content through technologies like large language models, audio models, and video models.
• 🏗️ Foundation Models, such as large language models, form the basis for technologies that predict and generate text, sentences, and even entire documents.
• 🎶 The script uses the analogy of music composition to explain how Generative AI can create new content from existing information.
• 🎭 Deepfakes are a part of Generative AI, capable of recreating voices and faces, with potential uses in entertainment and potential for abuse.
• 📈 The adoption of AI has accelerated with the rise of Machine Learning, Deep Learning, and Generative AI, shifting from a slow start to widespread application.
• 📊 Foundation Models have significantly altered the adoption curve of AI, leading to its integration and application across various industries.

Q & A

What is the primary goal of artificial intelligence (AI)?

The primary goal of AI is to simulate with a computer something that would match or exceed human intelligence, which generally includes the ability to learn, infer, and reason.

How are machine learning and AI different?

Machine learning is a subset of AI where the machine learns from data without being explicitly programmed, whereas AI is a broader field that includes any attempt to simulate human intelligence using computers.

What does the term 'deep learning' refer to in the context of AI?

Deep learning refers to a subset of machine learning that uses neural networks with multiple layers to simulate the human brain's operations, allowing the system to learn and make decisions based on complex patterns in data.

What is a generative AI and how does it relate to foundation models?

Generative AI is a type of AI that can generate new content, such as text, audio, or video. It is related to foundation models, which are underlying models like large language models that enable the generation of content by making predictions based on learned patterns.

Can you explain the concept of 'foundation models' in AI?

Foundation models are the basic models that underpin advanced AI technologies. An example is a large language model that can predict and generate text based on the input it receives, serving as a foundation for technologies like chatbots and content generation.

How do deepfakes fit into the realm of AI?

Deepfakes are a product of generative AI, specifically using models that can manipulate audio and visual data to create realistic but fake representations of individuals saying or doing things they never did.

What is the significance of the term 'generative' in the context of AI technologies?

The term 'generative' in AI refers to the ability of these technologies to create new content rather than just processing or analyzing existing data. This includes generating text, music, images, or even videos.

How did the adoption of AI technologies evolve over time?

The adoption of AI technologies started slowly with early AI research projects. It gained more traction with the rise of machine learning in the 2010s, and then skyrocketed with the advent of deep learning, foundation models, and generative AI.

What role does machine learning play in cybersecurity?

In cybersecurity, machine learning is used to spot outliers and anomalies in data, helping to identify and prevent unauthorized or malicious activities by detecting patterns that deviate from the norm.

How does the script simplify complex AI concepts for a broader audience?

The script simplifies AI concepts by using analogies, such as comparing generative AI to music composition, and by making generalizations to avoid overly technical explanations that could confuse non-experts.

What is the purpose of the video script in addressing frequently asked questions about AI?

The purpose of the video script is to clear up myths and misconceptions around AI, machine learning, and deep learning by addressing common questions and providing straightforward explanations of these technologies.

Outlines

00:00

🤖 Introduction to AI and Its Subfields

The script begins by addressing the widespread interest in artificial intelligence (AI) and machine learning, questioning whether they are the same and highlighting the need to clarify their differences. It mentions deep learning and generative AI as part of the broader AI landscape. The speaker acknowledges the growth of large language models and chatbots, as well as the emergence of deepfakes, emphasizing their relevance within AI. A disclaimer about simplifying complex concepts for the sake of clarity is provided, before diving into the definition of AI as the simulation of human intelligence by computers. The historical development of AI is traced back to its early days, including the use of programming languages like Lisp and Prolog, which laid the groundwork for expert systems. The script sets the stage for a deeper exploration of AI's evolution and its various components.

05:00

📈 Evolution of AI: From Expert Systems to Machine Learning

This paragraph delves into the evolution of AI, starting with expert systems of the 1980s and 1990s, which utilized programming languages that were precursors to modern AI technologies. It then transitions to machine learning, emphasizing its ability to learn from data without explicit programming. The speaker uses an analogy to illustrate how machine learning algorithms can predict patterns and identify outliers, which is particularly valuable in fields like cybersecurity. The paragraph also notes the rise in popularity of machine learning in the 2010s and its foundational role in current AI applications. The discussion then moves to deep learning, introducing neural networks as a simulation of the human brain, characterized by multiple layers that can sometimes lead to unpredictable outcomes. The advancements in deep learning during the 2010s are acknowledged, setting the stage for the next wave of AI advancements, generative AI, and the concept of foundation models.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence, or AI, refers to the simulation of human intelligence in computers. It encompasses a wide range of technologies that aim to match or exceed human capabilities in learning, reasoning, and problem-solving. In the video, AI is presented as the overarching field that includes machine learning and deep learning. The script discusses the history of AI, its evolution, and its current state, highlighting its broad applications and the shift in public awareness and adoption.

💡Machine Learning

Machine Learning is a subset of AI that focuses on the development of algorithms that can learn from and make predictions or decisions based on data. The script explains that machine learning does not require explicit programming for every task but instead relies on the machine to observe patterns within the data provided. It is particularly highlighted for its applications in predicting outcomes and identifying outliers, such as in cybersecurity.

💡Deep Learning

Deep Learning is a further specialization within machine learning that utilizes neural networks with multiple layers to simulate the human brain's operations. The term 'deep' refers to the depth of these networks, allowing them to process complex patterns and data. The script mentions that deep learning models can sometimes be unpredictable and complex, making it difficult to understand the reasoning behind their outputs.

💡Neural Networks

Neural Networks are computational models inspired by the human brain's structure and function. They consist of interconnected nodes or 'neurons' that process information. In the context of the video, neural networks are the foundation of deep learning, allowing for the simulation of cognitive tasks such as perception, pattern recognition, and decision-making.

💡Generative AI

Generative AI refers to the subset of AI technologies that can create new content, such as text, images, or audio, based on learned patterns. The video discusses generative AI's recent advancements and its ability to generate content that was not explicitly programmed. Examples include large language models that predict and generate text, and deepfakes that manipulate audio and visual data.

💡Foundation Models

Foundation Models, as introduced in the script, are underlying models that form the basis for various AI applications, particularly in generative AI. They are trained on vast amounts of data and can be fine-tuned for specific tasks. The video uses the analogy of autocomplete in text to explain how foundation models work in generating new content.

💡Large Language Models

Large Language Models are a type of foundation model specifically designed to process and generate human-like text. They are capable of predicting the next set of words in a sequence, which can be used for tasks such as content creation, translation, and chatbots. The script illustrates their capabilities by likening them to an advanced autocomplete feature that can generate entire documents.

💡Deepfakes

Deepfakes are a controversial application of generative AI that involves creating realistic but fake audiovisual content, typically by superimposing existing images or videos onto source images or videos. The video mentions deepfakes as an example of how AI can be used to create content that appears real but is generated from existing data, with both positive and negative implications.

💡Cybersecurity

Cybersecurity is the practice of protecting systems, networks, and programs from digital attacks. In the video, machine learning is highlighted for its utility in cybersecurity, particularly in identifying outliers and unusual patterns that may indicate a security breach or malicious activity.

💡Adoption Curve

The Adoption Curve in the video refers to the progression of how quickly and widely a technology or innovation is embraced by society. The script discusses how AI's adoption started slowly but has accelerated with the advent of machine learning, deep learning, and generative AI, leading to widespread integration across various industries and applications.

Highlights

Artificial intelligence (AI) is the simulation of human intelligence by computers, encompassing learning, inference, and reasoning.

Machine learning is a subset of AI where machines learn from data without being explicitly programmed, identifying patterns and making predictions.

Deep learning involves neural networks with multiple layers, mimicking the human brain's operation, and is less predictable due to its complexity.

Generative AI, including large language models and chatbots, represents the latest advancements in AI, generating new content based on existing data.

Foundation models, such as large language models, are a key component of generative AI, predicting sequences of words to create new documents.

Generative AI technologies are compared to music composition, where new creations are formed from existing notes.

Deepfakes are a controversial application of generative AI, capable of creating realistic but false representations of people's voices or images.

Cybersecurity can benefit from machine learning's ability to spot outliers, identifying unusual system usage that may indicate threats.

Expert systems of the 1980s and 90s laid the groundwork for the development of AI, using programming languages like Lisp and Prolog.

Machine learning gained popularity in the 2010s, becoming integral to many AI applications and advancements.

The adoption of AI has accelerated with the rise of machine learning, deep learning, and generative AI, transforming various industries.

AI's early days were marked by slow adoption and a perception of being a futuristic technology always a few years away.

The term 'generative AI' has sparked debates on whether it truly creates new content or merely repackages existing information.

The video aims to clarify myths and misconceptions around AI, machine learning, and deep learning, simplifying complex concepts for a broader audience.

The presenter apologizes for simplifying concepts, acknowledging that deep experts in the field may find the explanations generalized.

The video discusses the evolution of AI, from early research projects to the widespread adoption seen today, driven by advancements in machine learning and deep learning.

The presenter's personal experience with AI during their undergraduate studies contrasts with the current ubiquity of AI in various fields.

Transcripts

Everybody's talking about artificial intelligence these days, AI.

Machine learning is another hot topic.

Are they the same thing or are they different?

And if so, what are those differences?

And deep learning is another one that comes into play.

I actually did a video on these three:

artificial intelligence, machine learning and deep learning

and talked about where they fit.

And there were a lot of comments on that.

And I read those comments,

and I'd like to address some of the most frequently asked questions

so that we can clear up some of the myths and misconceptions around this.

In addition, something else has happened since that video was recorded,

and that is the absolute explosion of this area of generative AI.

Things like large language models and chat bots

has seemed to be taking over the world.

We see them everywhere.

Really interesting technology.

And then also things like deepfakes.

These are all within the realm of AI, but how do they fit within each other?

How are they related to each other?

We're going to take a look at that in this video

and try to explain how all these technologies relate, and how we can use them.

First off, a little bit of a disclaimer.

I'm going to have to simplify some of these concepts

in order to not make this video last for a week.

So, those of you that are really deep experts in the field, apologies in advance,

but we're going to try to make this simple,

and that will involve some generalizations.

First of all, let's start with AI.

Artificial intelligence is basically trying to simulate with a computer

something that would match or exceed human intelligence.

What is intelligence?

Well, it could be a lot of different things, but generally we tend to think of it

as the ability to learn, to infer and to reason things like that.

So, that's what we're trying to do in the broad field of AI, of artificial intelligence.

And if we look at a timeline of AI, it really kind of started back around this time frame.

And in those days it was very premature.

Most people had not even heard of it.

And it basically was a research project.

But I can tell you, as an undergrad,

which for me was back during these times, we were doing AI work.

In fact, we would use programing languages like Lisp, or Prolog,

and these kinds of things, were kind of the predecessors

to what became, later, expert systems.

And this was a technology ... again, some of these things existed previous,

but that's when it really hit a kind of a critical mass

and became more popularized.

So expert systems of the 1980s, maybe in the 90s.

And and again, we used technologies like this.

All of this was something that we did

before we ever touched in to the next topic I'm going to talk about.

And that's the area of machine learning.

Machine learning is, as its name implies, the machine is learning.

I don't have to program it.

I give it lots of information and it observes things.

So, for instance, if I start doing this,

if I give you this

and then ask you to predict what's the next thing that's going to be there,

well, you might get it, you might not.

You have very limited training data to base this on.

But if I gave you one of those

and then ask you what to predict, what will happen next?

Well, you're probably going to say this and then you're going to say it's this.

And then you think you got it all figured out.

And then you see one of these,

and then all of a sudden I give you one of those and throw you a curveball.

So this in fact and then maybe it goes on like this.

So a machine learning algorithm is really good at looking at patterns

and discovering patterns within data.

The more training data you can give it,

the more confident it can be in predicting.

So predictions are one of the things that machine learning is particularly good at.

Another thing is spotting outliers like this

and saying "oh, that doesn't belong in the - it looks different than all the other stuff because the sequence was broken."

So that's particularly useful in cybersecurity, the area that I work in

because we're looking for outliers.

We're looking for users who are using the system in ways that they shouldn't be,

or ways that they don't typically do.

So this technology, machine learning, is particularly useful for us.

And machine learning really came along, and became more popularized,

in this time frame, in the, the 2010s,

and again, back when I was an undergrad riding my dinosaur to class,

we were doing this kind of stuff.

We never once talked about machine learning.

It might have existed, but it really hadn't hit the popular, mindset yet.

But this technology has matured greatly over the last few decades,

and now it becomes the basis of a lot we do going forward.

The next layer of our Venn diagram involves deep learning.

Well, it's deep learning in the sense that

with deep learning we use these things called neural networks.

Neural networks are ways that in a computer, we simulate and mimic

the way the human brain works,

at least to the extent that we understand how the brain works.

And it's called deep because we have multiple layers of those neural networks.

And the interesting thing about these is

they will simulate the way a brain operates.

But I don't know if you know this, but human brains can be a little bit unpredictable.

You put certain things in, you don't always get the very same thing out.

And deep learning is the same way.

In some cases, we're not actually able to fully understand

why we get the results we do

because there are so many layers to the neural network,

it's a little bit hard to to decompose and figure out exactly what's in there.

But this has become a very important part and a very important advancement.

That also reached some popularity during the 2010s.

And as something that we use still today as the basis for our next area of AI.

The most recent advancements in the field of artificial intelligence

all really are in this space, the area of generative AI.

Now I'm going to introduce a term that you may not be familiar with.

It's the idea of foundation models.

Foundation models is where we get some of these kinds of things.

For instance, an example of a foundation model

would be a large language model.

Which is where we take language and we model it,

and we make predictions in this technology

where if I see certain types of of words,

then I can sort of predict what the next set of words will be.

I'm going to oversimplify here for the sake of simplicity,

but think about this is a little bit like the autocomplete

when you start typing something in, and then it predicts what your next word will be.

Except in this case, with large language models, they're not predicting the next word.

They're predicting the next sentence, the next paragraph, the next entire document.

So there's a really an amazing exponential leap in what these things are able to do.

And we call all of these technologies generative.

Because they are generating new content.

Some people have actually made the argument that

the generative AI isn't really generative, that

that these technologies are really just regurgitating existing information

and putting it in a different format.

Well, let me give you an analogy.

If you take music, for instance, then every note has already been invented.

So in a sense, every song is just a recombination,

some other permutation of all the notes that already exist already.

And just putting them in a different order.

Well, we don't say new music doesn't exist.

People are still composing and creating new songs from the existing information.

I'm going to say Gen AI is similar.

It's a it's an analogy, so there'll be some imperfections in it,

but you get the general idea.

Actually, new content can be generated out of these.

And there are a lot of different forms that this can take.

What other types of models are audio models, video models and things like that.

Well, in fact, these we can use to create deepfakes.

And deepfakes are examples where we're able to take, for instance, a person's voice

and recreate that and then have it seem like the person said things they never said.

Well, it's really useful in entertainment situations, in parodies and things like that.

Or if someone's losing their voice, then you could capture their voice,

and then they'd be able to type and you'd be able to hear it in their voice.

But there's also a lot of cases where this stuff could be abused.

The chat bots, again, come from this space.

The deepfakes come from this space,

but they're all part of generative AI and all part of these foundation models.

And this, again, is the area that has really caused all of us to really pay attention to AI.

The possibilities of generating new content, or in some cases,

summarizing existing content

and giving us something that is bite size and manageable.

This is what has gotten all of the attention.

This is where the chat bots and all of these things come in.

In the early days, AI's adoption started off pretty slowly.

Most people didn't even know it existed, and if they did,

it was something that always seemed like it was about 5 to 10 years away.

But then machine learning, deep learning and things like that

came along and we started seeing some uptick.

Then foundation models, Gen AI and the like

came along and this stuff went straight to the moon.

These foundation models are what have changed the adoption curve.

And now you see AI being adopted everywhere.

And the thing for us to understand is where this is, where it fits in,

and make sure that we can reap the benefits from all of this technology.

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