Google’s AI Course for Beginners (in 10 minutes)!
Summary
TLDRThis 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
📚 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.
🤖 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.
Mindmap
Keywords
💡Artificial Intelligence (AI)
💡Machine Learning
💡Supervised Learning
💡Unsupervised Learning
💡Deep Learning
💡Discriminative Models
💡Generative Models
💡Large Language Models (LLMs)
💡Fine-Tuning
💡Prompting
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
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