AI vs Machine Learning

Summary

The video explains the relationship between artificial intelligence (AI), machine learning (ML), and deep learning (DL). It defines AI as matching human intelligence and capabilities. ML uses data to make predictions and decisions by identifying patterns, learning over time. DL involves neural networks to model human thinking but outcomes aren't always explainable. The video states ML is a subset of AI, as are DL and other capabilities like vision and robotics. Together these comprise AI, which encapsulates human cognition. The goal is to match, not exceed humans.

Takeaways

• 😀 AI involves capabilities like discovering new information, making inferences, and logical reasoning
• 👍 Machine learning uses data to make predictions and decisions without explicit programming
• 🧠 Deep learning uses neural networks to model human brain functionality
• 🔬 Machine learning and deep learning are subsets of broader AI capabilities
• 🤖 AI includes diverse fields like natural language processing, computer vision, robotics
• 🗣 AI systems can interpret text, images, sounds to generate relevant outputs
• 😊 AI exceeds or matches capabilities of human intelligence and cognition
• ❓ AI systems may derive unintuitive solutions that are challenging to interpret
• 🔎 AI leverages large datasets to continuously learn and improve
• 📈 Practical AI integrates multiple techniques to achieve human-level performance

Q & A

How does the speaker define artificial intelligence?

The speaker defines artificial intelligence simply as exceeding or matching human capabilities and intelligence in areas like discovering new information, inferring meaning, and logical reasoning.

What are the main capabilities involved in machine learning?

The main capabilities involved in machine learning are making predictions and decisions based on data, learning from data rather than needing explicit programming, and improving with more data through supervised or unsupervised techniques.

What is the difference between supervised and unsupervised machine learning?

In supervised machine learning, humans label and organize the training data, while in unsupervised learning, the algorithms find patterns without explicit supervision or labeling of the data.

What makes deep learning a special subset of machine learning?

Deep learning uses neural networks modeled after the human brain with multiple layers to discover complex relationships in data, though the models can be difficult to interpret.

What capabilities beyond machine learning are part of AI?

Capabilities like natural language processing, computer vision, speech recognition and generation, robotics, and more are part of AI beyond just machine learning algorithms.

Why can deep learning models sometimes be unreliable?

Deep learning models may sometimes yield interesting but unreliable insights because the multiple neural network layers make it difficult to fully understand the reasoning behind the output.

What is the relationship between machine learning and AI?

Machine learning is a subset of AI, as it involves using data-based algorithms to mimic human-level intelligence in narrow applications.

Can robotics be considered a branch of AI?

Yes, robotics involves enabling machines to perform physical tasks like a human, thus it leverages AI capabilities and can be considered a branch of AI.

Why is machine learning considered a sophisticated form of statistical analysis?

Because machine learning algorithms detect complex patterns in data and make predictions based on probability and correlations, much like statistical analysis.

What real-world applications rely on the intersection of machine learning and AI?

Many complex real-world applications like self-driving cars, personalized recommendations, predictive analytics, and natural language processing rely on a combination of machine learning techniques as well as broader AI capabilities.

Outlines

00:00

😀 Defining AI and capabilities it involves

This paragraph provides a definition for AI as matching or exceeding human capabilities and intelligence. It states AI involves abilities like discovering new information, making inferences, reasoning to figure things out logically, etc. The goal is to match what the human mind can do.

05:00

😃 Explaining machine learning and its types

This paragraph defines machine learning as making predictions or decisions based on data, like a sophisticated statistical analysis. It explains the difference between supervised and unsupervised ML. Deep learning involving neural networks is presented as a subfield of ML that can provide interesting insights without fully showing its work.

😁 Relationship between ML, DL and AI

This paragraph concludes by showing the relationship between ML, DL and AI using a Venn diagram. ML and DL are subsets of AI, along with other capabilities like natural language processing and robotics. So ML is part of doing AI, but does not represent all of AI.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence (AI) refers to the capability of a machine to match or exceed human intelligence and abilities. In the video, AI is defined as exceeding or matching human capabilities related to discovering new information, making inferences, reasoning to solve problems, etc. AI encompasses various approaches like machine learning as well as application areas like computer vision and robotics.

💡Machine Learning (ML)

Machine Learning (ML) is presented as an approach to making predictions or decisions based on data, without explicit programming. It involves 'learning' from large amounts of data and improving over time. The video distinguishes between supervised ML where models are trained on human-labeled data versus unsupervised ML which finds patterns without labels.

💡Deep Learning

Deep Learning is introduced as a subfield of machine learning based on neural networks to model human brain functions. It uses multiple layers of neural networks to derive insights from data, even if the reasoning behind those insights is not fully traceable.

💡Natural Language Processing

The video mentions natural language processing as one of the domains within AI focused on enabling machines to understand and process human languages, similar to human capabilities.

💡Computer Vision

Computer vision is noted as a field within AI that deals with enabling machines to see and visually perceive the world around them through imaging and video data - a human capability that AI aims to match.

💡Robotics

Robotics involves developing machines capable of physical motion and actions similar to humans, like tying shoes, opening doors, lifting objects, and walking. The video positions robotics as a domain of AI requiring integration of perception, planning, and mechanical capabilities.

💡Text to Speech

Text to speech involves machine reading of written text and conversion into natural sounding speech, which the video mentions as one of the human capabilities AI aims to match.

💡Supervised Learning

Supervised machine learning is explained as an approach involving labeled training data and closer human oversight during model development, versus unsupervised learning where models find patterns without explicit supervision.

💡Unsupervised Learning

Unsupervised machine learning, as contrasted with supervised learning, does not rely on human-labeled data and oversight for developing models that find patterns and insights on their own.

💡Subset vs Superset

A key diagram depicts machine learning and deep learning as subsets of the broader domain of artificial intelligence. This visualizes the relationship of ML and DL being specific approaches under the umbrella of AI.

Highlights

AI is basically exceeding or matching the capabilities of a human

AI involves discovering new information, inferring unstated information, and reasoning to figure things out

Machine learning involves making predictions or decisions based on data, like a sophisticated statistical analysis

Machine learning 'learns' from data rather than having to be explicitly programmed

Supervised machine learning uses human-labeled data, unsupervised does not

Deep learning uses neural networks to model the human mind

Deep learning can provide insights without fully showing its work

AI includes machine learning, deep learning, natural language processing, computer vision, speech processing, and robotics

Machine learning is a subset of AI

When doing machine learning, you are doing AI

None of the AI subsets fully encompass all of AI

Machine learning provides important AI capabilities

AI aims to match a wide range of human capabilities involving perception, calculation, motion

AI is the superset, machine learning is a subset

The right framework is machine learning as a subset of AI

Transcripts

Artificial intelligence (AI) and machine learning (ML).

What's the difference?

Are they the same?

Well, some people kind of frame the question this way: it's "AI versus ML".

Is that the right way to think of this?

Or is it "AI = ML"?

Or is it "AI is somehow something different than ML"?

So here's three equations.

I wonder which one is going to be right.

Well, let's talk about this.

First of all, when we talk about AI, I think it's important to come with definitions

because a lot of people have different ideas of what this is.

So I'm going to assert

the simple definition that AI is basically exceeding or matching

the capabilities of a human.

So we're trying to match the intelligence, whatever that means, and capabilities of a human subject.

Now, what could that involve?

There's a number of different things.

For instance, one of them is the ability to discover, to find out new information.

Another is the ability to infer,

to read in information from other sources that maybe has not been explicitly stated.

And then also the ability to reason.

The ability to figure things out.

I put this in this together and I come up with something else.

So I'm going to suggest to you this is what AI is,

and that's the definition we'll use for this discussion.

Now, what kinds of things then would be involved if we were talking about doing machine learning?

Well, machine learning, I'm going to put that over here, is basically a capability.

We'll start with a Venn diagram.

Machine learning involves predictions or decisions based on data.

Think about this as a very sophisticated form of statistical analysis.

It's looking for predictions based upon information that we have.

So the more we feed into the system,

the more it's able to give us accurate predictions and decisions based upon that data.

It's something that learns, that's the "L" part,

rather than having to be programed.

When we program a system, I have to come up with all the code

and if I wanted to do something different,

I have to go change the code and then get a different outcome.

In the machine learning situation

what I'm doing could be adjusting some models, but it's different than programing,

and mostly it's learning the more data that I give to it.

So it's based on large amounts of information,

and there's a couple of different fields within, a couple of different types.

There is "supervised machine learning"

and, as you might guess, there's an "unsupervised machine learning".

And the main difference, as the name implies, is one has more human oversight,

looking at the training of the data,

using labels that are superimposed on the data.

Unsupervised is kind of able to run more

and find things that were not explicitly stated.

Okay, so that's machine learning.

It turns out that there is a subfield of machine learning that we call "deep learning".

And what is deep learning?

Well, this involves things like neural networks.

Neural networks involved nodes and statistical relationships between those nodes

to model the way that our minds work.

And it's called "deep" because we're doing multiple layers of those neural networks.

Now, the interesting thing about deep learning is we can end up with some very interesting insights,

but we might not always be able to tell how the system came up with that.

It doesn't always show its work fully.

So we can end up with some really interesting information,

not know in some cases how reliable that is, because we don't know exactly how it was derived.

But it's still a very important part of all of this realm that we're dealing with.

So those are two areas, and you can see that DL is a subset of ML.

But what about artificial intelligence?

Where does that fit in the Venn diagram?

And I'm going to suggest to you it is the superset of ML, DL, and a bunch of other things.

What could the other things be?

Well, we can involve things like natural language processing.

It could be vision.

So we want a system that's able to see.

We might even want a system that's able to hear,

and be able to distinguish what it's hearing and what it's seeing.

Because after all, humans are able to do that.

And that's part of what our brains do, is distinguish those kinds of things.

It can involve other things like the ability to do text-to-speech.

So if we take written words, concepts and be able to speak those out.

So this first one involved being able to see things.

This is now being able to speak those things as well.

And then other things that humans are able to do naturally that we often take for granted is motion.

This is the field of robotics,

which is a subset of AI,

the ability to just do simple things like tie our shoes, open and close the door, lift something, walk somewhere.

That's all something that would be part of human capabilities

and involves certain sorts of perceptions,

calculations that we do in our brains that we don't even think about.

So here's what it comes down to:

it's a Venn diagram and we've got machine learning, we've got deep learning, and we've got AI.

So I'm going to suggest to you, the right way to think about this is not these equations.

Those are not the way to look at it.

In fact, what we should think about this as machine learning is a subset of AI.

And that's how we need to think about this:.

when I'm doing machine learning, in fact, I am doing AI.

When I'm doing these other things, I'm doing AI,

but none of them are all of AI.

But they're a very important part.

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