Google’s AI Course for Beginners (in 10 minutes)!

Summary

This video script offers a concise introduction to artificial intelligence (AI), clarifying misconceptions and explaining the relationship between AI, machine learning, and deep learning. It distinguishes between supervised and unsupervised learning models, delves into deep learning's use of artificial neural networks, and differentiates between discriminative and generative models. The script also highlights the role of large language models (LLMs) in AI applications, such as ChatGPT and Google Bard, and their fine-tuning for specific industries. The content is designed to be accessible for beginners, providing a practical understanding of AI's foundational concepts.

Takeaways

• 📚 Artificial Intelligence (AI) is a broad field of study, with machine learning as a subfield, similar to how thermodynamics is a subfield of physics.
• 🤖 Machine Learning involves training a model with input data to make predictions on unseen data, with common types being supervised and unsupervised learning models.
• 🔍 Supervised learning uses labeled data to train models, allowing for predictions based on historical data points, while unsupervised learning identifies patterns in unlabeled data.
• 🧠 Deep Learning is a subset of machine learning that utilizes artificial neural networks, inspired by the human brain, to create more powerful models.
• 🔧 Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data for training deep learning models, such as fraud detection in banking.
• 📈 Discriminative models classify data points based on their labels, whereas generative models learn patterns from data and generate new outputs based on those patterns.
• 🖼️ Generative AI can output natural language text, speech, images, or audio, creating new samples similar to the data it was trained on.
• 📖 Large Language Models (LLMs) are a type of deep learning model pre-trained on vast datasets and fine-tuned for specific tasks, like improving diagnostic accuracy in healthcare.
• 🔗 LLMs and Generative AI are not identical; LLMs are generally fine-tuned for specific purposes after pre-training, unlike general generative AI models.
• 🎓 Google offers a free 4-hour AI course for beginners, which provides a comprehensive understanding of AI concepts and practical applications.
• 📝 Taking notes from the course? Use the video URL feature to easily navigate back to specific parts of the video for review.

Q & A

What is the main purpose of distilling Google's 4-Hour AI course into a 10-minute summary?

The main purpose is to provide a concise and practical overview of the basics of artificial intelligence, making it accessible to beginners who may not have a technical background.

How does the speaker describe their initial skepticism about the AI course?

The speaker was skeptical because they thought the course might be too conceptual and not focused on practical tips, which is the focus of their channel.

What misconception did the speaker have about AI before taking the course?

The speaker mistakenly believed that AI, machine learning, and large language models were all the same thing, not realizing that AI is a broad field with machine learning as a subfield, and deep learning as a subset of machine learning.

What are the two main types of machine learning models mentioned in the script?

The two main types of machine learning models mentioned are supervised and unsupervised learning models.

How does a supervised learning model make predictions?

A supervised learning model uses labeled historical data to train a model, which can then make predictions on new, unseen data based on the patterns it has learned from the training data.

What is the key difference between supervised and unsupervised learning models?

The key difference is that supervised models use labeled data, while unsupervised models use unlabeled data and try to find natural groupings or patterns within the data.

What is semi-supervised learning in the context of deep learning?

Semi-supervised learning is a type of deep learning where a model is trained on a small amount of labeled data and a large amount of unlabeled data, allowing it to learn basic concepts from the labeled data and apply those to the unlabeled data for making predictions.

How do discriminative and generative models differ in deep learning?

Discriminative models learn the relationship between data point labels and classify new data points based on those labels, while generative models learn patterns in the training data and generate new data samples based on those patterns.

What is the role of large language models (LLMs) in AI applications?

Large language models are a subset of deep learning that are pre-trained with a vast amount of data and then fine-tuned for specific purposes, such as text classification, question answering, and text generation, in various industries.

How can smaller institutions benefit from large language models developed by big tech companies?

Smaller institutions can purchase pre-trained LLMs from big tech companies and fine-tune them with their domain-specific data sets to solve specific problems, without having to develop their own large language models from scratch.

What is the significance of the course's structure for learners?

The course is structured into five modules, with a badge awarded after completing each module. This structure helps learners track their progress and provides a sense of accomplishment, while the theoretical content is balanced with practical applications.

Outlines

00:00

📚 Introduction to AI and Machine Learning

This paragraph introduces the basics of artificial intelligence (AI), clarifying that AI is a broad field of study with machine learning as a subfield, similar to thermodynamics in physics. It discusses the distinction between deep learning and machine learning, and further differentiates between discriminative and generative models. The paragraph also explains the concept of large language models (LLMs) and their role in AI applications like ChatGPT and Google Bard. The author shares their initial skepticism about the Google AI course for beginners but acknowledges the practical benefits of understanding these concepts.

05:02

🤖 Understanding Machine Learning Models

The paragraph delves into the specifics of machine learning, explaining that it involves training a model with input data to make predictions on unseen data. It differentiates between supervised and unsupervised learning models, using examples to illustrate how each type operates. Supervised learning uses labeled data to predict outcomes, while unsupervised learning identifies patterns in unlabeled data. The paragraph also touches on semi-supervised learning, which combines a small amount of labeled data with a large amount of unlabeled data for training deep learning models.

Keywords

💡Artificial Intelligence (AI)

AI refers to the field of study that aims to create systems capable of performing tasks that would typically require human intelligence, such as learning, problem-solving, and decision-making. In the video, AI is presented as a broad field with various subfields, including machine learning, which is a key component in developing practical AI applications like ChatGPT and Google Bard.

💡Machine Learning

Machine learning is a subfield of AI that focuses on developing algorithms that allow computers to learn from and make predictions based on data. The video explains that machine learning models are trained using input data to make predictions on new, unseen data. For instance, a model trained on Nike sales data could predict Adidas sales, illustrating the practical application of machine learning in business analytics.

💡Supervised Learning

Supervised learning is a type of machine learning where the model is trained on labeled data, meaning the input data has associated output labels. The video uses the example of predicting restaurant tips based on the total bill and order type (picked up or delivered), where the data points are labeled with the actual tip amounts, allowing the model to learn the relationship between inputs and outputs.

💡Unsupervised Learning

Unsupervised learning, as opposed to supervised learning, involves analyzing unlabeled data to discover hidden patterns or groupings. The video provides the example of plotting employee tenure against income to identify natural groupings, which could then be used to predict outcomes for new employees without prior labeled data.

💡Deep Learning

Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to model complex patterns in data. The video likens neural networks to the human brain, with layers of nodes and neurons that enable the model to become more powerful with increased layers. Deep learning is used in semi-supervised learning, where a model is trained on a small set of labeled data and a large set of unlabeled data, as in the case of a bank detecting fraudulent transactions.

💡Discriminative Models

Discriminative models in deep learning focus on learning the relationship between the labels of data points and classifying new data points accordingly. The video example is a model trained to recognize cats and dogs from labeled images, which then classifies new images as either cat or dog based on the learned patterns.

💡Generative Models

Generative models, unlike discriminative models, learn the underlying patterns in the training data and generate new data samples. The video explains that generative AI can create new content, such as images or text, based on the patterns it has learned. An example given is a generative model that generates a new image of a dog based on the patterns it identified in a set of unlabeled dog images.

💡Large Language Models (LLMs)

LLMs are a type of deep learning model specifically designed for natural language processing tasks. They are pre-trained on a vast amount of data and then fine-tuned for specific purposes. The video clarifies that while LLMs are a subset of deep learning and related to generative AI, they are distinct in that they are often fine-tuned with industry-specific data to solve particular problems, such as improving diagnostic accuracy in healthcare.

💡Fine-Tuning

Fine-tuning in the context of LLMs refers to the process of adapting a pre-trained model to perform better on specific tasks by training it with smaller, domain-specific datasets. The video uses the analogy of a dog being pre-trained with basic commands and then fine-tuned for specialized roles like a police dog or guide dog. Similarly, LLMs can be fine-tuned for applications in retail, finance, or healthcare to enhance their performance in those domains.

💡Prompting

Prompting in the context of AI applications like ChatGPT and Google Bard refers to the technique of providing input to the AI model in a way that elicits the desired response. The video suggests that mastering the art of prompting is crucial for effectively using AI tools, although it does not delve into the specifics within the provided transcript.

Highlights

Google's 4-Hour AI course for beginners has been condensed into a 10-minute overview.

AI is an entire field of study, with machine learning as a subfield, similar to thermodynamics being a subfield of physics.

Deep learning is a subset of machine learning, and further divides into discriminative and generative models.

Large language models (LLMs) like ChatGPT and Google Bard fall under the category of deep learning and are at the intersection of generative models.

Machine learning models use input data to train, and then make predictions on unseen data.

Supervised learning models use labeled data, while unsupervised learning models work with unlabeled data.

Supervised learning can predict outcomes based on historical data, like predicting sales of a new product based on past sales data.

Unsupervised learning identifies patterns or groups in data without labels, like classifying employees based on income and tenure.

Deep learning models use artificial neural networks inspired by the human brain, enabling semi-supervised learning with a mix of labeled and unlabeled data.

Discriminative models classify data points based on learned relationships, while generative models generate new content based on patterns in the data.

Generative AI can output natural language text, speech, images, or audio, unlike discriminative models which output classifications or probabilities.

Common types of generative AI models include text-to-text, text-to-image, text-to-video, text-to-3D, and text-to-task models.

Large language models are pre-trained on vast datasets and then fine-tuned for specific purposes, like a generalist dog being trained for specialized roles.

LLMs can be fine-tuned with industry-specific data to solve particular problems in fields like retail, finance, healthcare, and entertainment.

The full AI course offers a badge upon completion and includes modules on theoretical aspects as well as practical skills like mastering prompting.

The video provides a tip on how to easily navigate back to specific parts of the content by copying the video URL at the current time.

Transcripts

if you don't have a technical background

but you still want to learn the basics

of artificial intelligence stick around

because we are distilling Google's

4-Hour AI course for beginners into just

10 minutes I was initially very

skeptical because I thought the course

would be too conceptual we're all about

practical tips on this channel and

knowing Google the course might just

disappear after 1 hour but I found the

underlying concepts actually made me

better at using tools like ChatGPT and

Google bard and cleared up a bunch of

misconceptions I didn't know I had about

AI machine learning and large language

models so starting with the broadest

possible question what is artificial

intelligence it turns out and I'm so

embarrassed to admit I didn't know this

AI is an entire field of study like

physics and machine learning is a

subfield of AI much like how

thermodynamics is a subfield of physics

going down another level deep learning

is a subset of machine learning and deep

learning models can be further broken

down into something called

discriminative models and generative

models large language models LLMs also

fall under deep learning and right at

the intersection between generative and

LLMs is the technology that powers the

applications we're all familiar with

ChatGPT and Google bard let me know in

the comments if this was news to you as

well now that we have an understanding

of the overall landscape and you see how

the different disciplines sit in

relation to each other let's go over the

key takeaways you should know for each

level in a nutshell machine learning is

a program that uses input data to train

a model that trained model can then make

predictions Based on data it has never

seen before for example if you train a

model based on Nike sales data you can

then use that model to predict how well

a new shoe from Adidas would sell based

on Adidas sales data two of the most

common types of machine learning models

are supervised and unsupervised learning

models the key difference between the

two is supervised models use labeled

data and unsupervised models use

unlabeled data in this supervised

example we have historical data points

that plot the total bill amount at a

restaurant against the tip amount and

here the data is labeled Blue Dot equals

the order was picked up and yellow dot

equals the order was delivered using a

supervised learning model we can now

predict how much tip we can expect for

the next order given the bill amount and

whether it's picked up or delivered for

unsupervised learning models we look at

the raw data and see if a naturally

falls into groups in this example we

plotted the employee tenure at a company

against their income we see this group

of employees have a relatively High

income to years worked ratio versus this

group we can also see all these are

unlabeled data if they were labeled we

would see male female years worked

company function Etc we can now ask this

unsupervised learning model to solve a

problem like if a new employee joins are

they on the FasTrack or not if they

appear on the left then yes if they

appear on the right then no Pro tip

another big difference between the two

models is that after a supervised

learning model makes a prediction it

will compare that prediction to the

training data used to train that model

and if there's a difference it tries to

close that Gap unsupervised learning

models do not do this by the way this

video is not sponsored but it is

supported by those of you who subscribe

to my paid productivity newsletter on

Google tips Link in the description if

you want to learn more now we have a

basic grasp of machine learning it's a

good time to talk about deep learning

which is just a type of machine learning

that uses something called artificial

neural networks don't worry all you have

to know for now is that artificial

neural networks are inspired by the

human brain and looks something like

this layers of nodes and neurons and the

more layers there are the more powerful

the model and because we have these

neural networks we can now do something

called semisupervised learning whereby a

deep learning model is trained on a

small amount of labeled data and a large

amount of unlabeled data for example a

bank might use deep learning models to

detect fraud the bank spends a bit of

time to tag or label 5% of transactions

as either fraudulent or not fraudulent

and they leave the remaining 95% of

transactions unlabeled because they

don't have the time or resources to

label every transaction the magic

happens when the Deep learning model

uses the 5% of label data to learn the

basic concepts of the task okay these

transactions are good these are bad okay

apply those learnings to the remaining

95% of unlabeled data and using this new

aggregate data set the model makes

predictions for future transactions

that's pretty cool and we're not done

because deep learning can be divided

into two types discriminative and

generative models discriminative models

learn from the relationship between

labels of data points and only has the

ability to classify those data points

fraud not fraud for example you have a

bunch of of pictures or data points you

purposefully label some of them as cats

and some of them as dogs a

discriminative model will learn from the

label cat or dog and if you submit a

picture of a dog it will predict the

label for that new data point a dog we

finally get to generative AI unlike

discriminative models generative models

learn about the patterns in the training

data then after they receive some input

for example a text prompt from us they

generate something new based on the

patterns they just learned going back to

the animal example the pictures or data

points are not labeled as cat or dog so

a generative model will look for

patterns oh these data points all have

two ears four legs a tail likes dog food

and Barks when asked to generate something

called a dog the generative model

generates a completely new image based

on the patterns it just learned there's

a super simple way to determine if

something is generative AI or not if the

output is a number a classification spam

not spam or a probability it is not

generative AI it is GenAI when the

output is natural language text or a

speech an image or audio basically

generative AI generates new samples that

are similar to the data it was trained

on moving on to different generative AI

model types most of us are familiar with

text-to-text models like ChatGPT and

Google bard other common model types

include text-to-image models like Midjourney

DALL·E and stable diffusion these

can not only generate images but edit

images as well text-to-video models

surprise surprise can generate and edit

video footage examples include Google's

imagen video CogVideo and the Very

creatively named make a video text-to-3D

models are used to create game assets

and a little known example would be OpenAI's

shap-e model and finally text to

task models are trained to perform a

specific task for example if you type

@Gmail summarize my unread emails Google

bard will look through your inbox and

summarize your unread emails moving over

to large language models don't forget

that LLMs are also a subset of deep

learning and although there is some

overlap LLMs and GenAI are not the same

thing an important distinction is that

large language models are generally

pre-trained with a very large set of

data and then fine-tune for specific

purposes what does that mean imagine you

have a pet dog it can be pre-trained

with basic commands like sit come down

and stay it's a good boy and a

generalist but if that same good boy

goes on to become a police dog a guide

dog or hunting dog they need to receive

specific training so they're fine tuned

for that specialist role a similar idea

applies to large language models they're

first pre-trained to solve common

language problems like text

classification question answering

document summarization and text

generation then using smaller industry

specific data sets these LLMs are

fine-tuned to solve specific problems in

Retail Finance healthcare entertainment

and other fields in the real world this

might mean a hospital uses a pre-trained

large language model from one of the big

tech companies and fine-tunes that model

with its own first-party medical data to

improve diagnostic accuracy from X-rays

and other medical tests this is a

win-win scenario because large companies

can spend billions developing general

purpose large language models then sell

those LLMs to smaller institutions like

retail companies Banks hospitals who

don't have the resources to develop

their own large language models but they

have the domain specific data sets to

fine-tune those models Pro tip if you do

end up taking the full course I'll link

it down below it's completely free when

you're taking notes you can right click

on the video player and copy video URL

at the current time so can quickly

navigate back to that specific part of

the video there are five modules total

and you get a badge after completing

each module the content overall is a bit

more on the theoretical side so you

definitely want to check out this video

on how to master prompting next see you

on the next video in the

meantime have a great one