Map of Artificial Intelligence
Summary
TLDRThis video script delves into the expansive field of artificial intelligence, outlining its foundational mathematics including linear algebra, vector calculus, and probability theory. It then explores methods such as optimization, machine learning, and deep learning, highlighting their applications in areas like computer vision, natural language processing, robotics, computational biology, and recommender systems. The script emphasizes AI's versatility and its reliance on fundamental mathematical principles to solve a myriad of real-world problems.
Takeaways
- 🧠 Artificial Intelligence (AI) is a broad field with various subfields, each with its own experts, problems, and methods.
- 📚 The foundations of AI are based on three major types of mathematics: linear algebra, vector calculus, and probability theory.
- 📈 Linear algebra deals with linear equations and systems, and is essential for modeling real-world phenomena and understanding geometric interpretations.
- 🔍 Vector calculus, an extension of calculus to multiple dimensions, is crucial for AI as it helps in understanding how variables controlling a model change relative to each other.
- 🎲 Probability theory is important for dealing with uncertainty in the real world and is fundamental in building AI models that can reason about uncertain outcomes.
- 🔧 Optimization is a key method in AI, focusing on finding the best solution within given constraints, which is vital for tasks like pathfinding and machine learning.
- 🤖 Machine learning is the science of learning from data, involving the adjustment of model parameters to minimize error, often categorized into supervised, unsupervised, self-supervised, and semi-supervised learning.
- 👾 Reinforcement learning is about learning from action, where an AI system learns to perform tasks by taking actions and receiving rewards or penalties.
- 🧠 Deep learning involves the use of neural networks, which are versatile models capable of learning complex relationships and behaviors from data.
- 👀 Computer vision is an application of AI focused on understanding and interpreting visual information from photos, videos, and other digital images.
- 💬 Natural language processing (NLP) is AI's application to understanding and generating human language, including speech recognition and chatbots.
- 🤖 Robotics is where AI interacts with the physical world, with AI playing a key role in perception and control, often integrating computer vision and reinforcement learning.
- 🧬 Computational biology applies AI to life sciences, including drug discovery, protein structure prediction, and genomics for disease prediction.
- 🔑 Recommender systems use AI to predict user preferences and interests, influencing what content is recommended on various social media platforms.
Q & A
What are the three fundamental types of mathematics that all AI is based on?
-The three fundamental types of mathematics that all AI is based on are linear algebra, vector calculus, and probability theory.
Why is linear algebra considered important in AI?
-Linear algebra is important in AI because it deals with systems of linear equations and can model a wide range of real-world phenomena. Its versatility, combined with the efficiency of computers in handling linear algebra, makes it a powerful tool in AI.
What does vector calculus extend in the context of AI?
-Vector calculus extends the concept of calculus to multiple dimensions, allowing for the study of changes in variables relative to each other in a multi-dimensional space, which is crucial for understanding the behavior of computer models.
How does probability theory contribute to AI?
-Probability theory contributes to AI by providing a mathematical framework for dealing with uncertainty, which is inherent in real-world scenarios and helps AI systems make predictions and decisions under uncertain conditions.
What is optimization in the context of AI?
-In the context of AI, optimization is the process of finding the best solution or setting within a set of constraints, often used to minimize error in machine learning models by adjusting parameters.
What is the main goal of machine learning?
-The main goal of machine learning is to enable computers to learn from data and make predictions or decisions without being explicitly programmed to perform a specific task.
What is the difference between supervised and unsupervised learning?
-Supervised learning involves learning from labeled data, where the correct answers are provided for each example. Unsupervised learning, on the other hand, involves learning from data without labels, discovering patterns and relationships within the data itself.
What is reinforcement learning and how does it differ from other types of learning?
-Reinforcement learning is about learning from action, where an agent learns to make decisions by performing actions in an environment to achieve a goal. It differs from other types of learning in that it involves sequential decision-making and the environment changes based on the agent's actions.
What is deep learning and how does it relate to neural networks?
-Deep learning is a subfield of machine learning that focuses on learning with neural networks, which are powerful models capable of learning complex patterns and relationships in data. Neural networks are the foundation of deep learning, allowing it to be versatile and applicable to various problems.
What are some of the major applications of AI mentioned in the script?
-Some of the major applications of AI mentioned in the script include computer vision, natural language processing, robotics, computational biology, and recommender systems.
How is AI used in computer vision?
-AI is used in computer vision for tasks such as object detection, facial recognition, image processing for self-driving cars, and automatic analysis of medical images, enabling machines to understand and interpret visual data.
What role does natural language processing (NLP) play in AI?
-Natural language processing (NLP) plays a crucial role in AI by enabling machines to understand, interpret, and generate human language, which is used in applications like speech recognition, chatbots, and language translation.
How does AI contribute to robotics?
-AI contributes to robotics by providing perception, which helps robots understand the world through their sensors, and control, which involves making decisions based on the perceived information, often using techniques like computer vision and reinforcement learning.
What is computational biology and how does AI play a role in it?
-Computational biology is an interdisciplinary field that applies AI and computational methods to the life sciences. AI plays a role in areas such as automatic drug discovery, predicting protein structures from DNA sequences, and analyzing genomic data to predict diseases.
What are recommender systems in AI and how do they work?
-Recommender systems in AI are algorithms designed to predict user preferences and suggest items, such as products, movies, or content, that a user may like. They work by analyzing user behavior, preferences, and other data to provide personalized recommendations.
Outlines
🧠 Foundations of AI: The Mathematics Behind
This paragraph introduces the three fundamental mathematical disciplines that underpin all of artificial intelligence (AI). It emphasizes linear algebra, which is the study of linear equations and systems, allowing computers to solve complex systems efficiently. The paragraph explains how linear algebra can model real-world phenomena due to its versatility and the efficiency of computational methods. It also touches on vector spaces, which are essential for understanding dimensions beyond the three-dimensional world we are familiar with. The importance of linear algebra in AI is highlighted by its ability to frame problems in a way that unlocks powerful mathematical solutions.
📈 Vector Calculus and Probability: The Dynamics of AI
This section delves into vector calculus, the mathematics of change in multiple dimensions, which is crucial for understanding how variables interact within computer models. It explains how vector calculus helps in adjusting model parameters to minimize error, a fundamental process in AI known as learning. The paragraph also introduces probability theory as the mathematics of uncertainty, essential for building AI models that can reason about the unpredictable aspects of the real world. The importance of these mathematical fields is underscored by their role in creating AI systems that can adapt and learn from data.
🔧 Methods in AI: Optimization and Learning Paradigms
The paragraph discusses various methods used in AI, starting with optimization, which is about finding the best solution in a given setting, often subject to constraints. It uses the analogy of pathfinding to explain optimization and then transitions to machine learning, the science of learning from data. The paragraph distinguishes between supervised learning, where data includes labels, and unsupervised learning, where it does not. It also mentions reinforcement learning, which involves learning from actions in an environment that changes due to the system's decisions, and deep learning, which uses neural networks to learn complex relationships from data. The paragraph positions these methods as applications of the fundamental mathematics to solve specific problems.
🌐 Applications of AI: From Vision to Recommendation
The final paragraph outlines the various applications of AI, building upon the foundational mathematics and methods previously discussed. It covers computer vision, which involves AI's understanding of visual data, natural language processing, which enables AI to comprehend and generate language, and robotics, where AI is used for perception and control. The paragraph also touches on computational biology, which applies AI to life sciences, and recommender systems, which predict user preferences across various platforms. The summary highlights how these applications utilize the comprehensive framework of AI to solve real-world problems effectively.
Mindmap
Keywords
💡Artificial Intelligence (AI)
💡Linear Algebra
💡Vector Calculus
💡Probability Theory
💡Optimization
💡Machine Learning
💡Supervised Learning
💡Unsupervised Learning
💡Reinforcement Learning
💡Deep Learning
💡Computer Vision
💡Natural Language Processing (NLP)
💡Robotics
💡Computational Biology
💡Recommender Systems
Highlights
Artificial intelligence (AI) has seen a surge in popularity following the release of Chat GPT.
AI is a broad field with various subfields, each with its own experts, problems, and methods.
The video outlines the major subfields of AI and explains their significance.
AI is categorized into three main areas: fundamental maths, methods, and applications.
Linear algebra is the foundation of AI, dealing with linear equations and systems.
Linear algebra's versatility allows it to model a wide range of real-world phenomena.
Vector spaces, a concept from linear algebra, extend our understanding of dimensions beyond three.
Vector calculus, an extension of calculus, deals with change in multiple dimensions.
Parameters in AI models are analogous to knobs that control the model's behavior.
Learning in AI involves adjusting parameters to minimize error, guided by vector calculus.
Probability theory is essential for dealing with uncertainty in AI models.
Optimization is about finding the best solution within given constraints.
Machine learning is the science of learning from data, often involving supervised learning.
Unsupervised learning focuses on finding patterns in unlabeled data.
Reinforcement learning involves learning from actions and their consequences.
Deep learning uses neural networks to learn complex relationships from data.
Applications of AI include computer vision, natural language processing, robotics, computational biology, and recommender systems.
Computer vision enables AI to understand and process visual information.
Natural language processing allows AI to understand and generate human language.
Robotics combines AI with physical machines for tasks like perception and control.
Computational biology applies AI to life sciences, such as drug discovery and genomics.
Recommender systems use AI to predict user preferences and suggest content.
Transcripts
artificial intelligence has exploded in
popularity since the release of chat GPT
and for a lot of people chat GPT and
mid-journey are pretty much the only AI
systems they know but AI as a field is
so much broader than that and like any
scientific field it's broken into a
bunch of different subfields with their
own experts problems and methods in this
video I'll lay out the major subfields
of AI and explain what each of them
means
I'm going to split it into three
different categories fundamental maths
methods and applications let's start
with the foundations the math there are
three major types of math on which all
of AI is based all of AI boils down in
one way or another to these three kinds
of math these are like the Deep lore or
the dark magic of AI first among these
is linear algebra which is the study of
linear equations equations in any number
of variables where the highest power of
any variable is one you can add subtract
multiply and divide and Shuffle around
these terms however you want but you
can't include powers of variables so y
equals MX plus b which you might
recognize from middle school that's
linear 4X plus 3y equals 10 that's
linear 4X plus 3y plus 5z equals one
that's linear but x squared plus 4X plus
3y plus four Z equals one that's not
linear because of the x squared y cubed
plus x equals one not linear y plus x
equals one linear when you stack a bunch
of these equations together you get a
system of equations and you can use
computers to really quickly solve these
systems of equations to figure out what
X Y and Z make all of these equations
true at the same time now why do linear
equations matter well Gilbert Strang one
of the Godfathers of linear algebra put
it really well when he said that linear
algebra is the study of flat things and
flat things surprisingly can be used to
model all sorts of real world phenomenon
even things that aren't flat get flat if
you zoom in enough so you pair that
level of Versatility with how
efficiently computers can deal with
linear algebra and you get a very
powerful combination linear algebra also
studies all of the geometric
interpretations of the math where we get
ideas like vector spaces which lie at
the heart of all of AI without getting
too into the weeds Vector spaces are
basically the mathematic radical
extension of what lies Beyond three
dimensions we can all visualize 1D and
2D and 3D pretty well but when we get
beyond that things start to get tricky
Vector spaces allow us to extend these
things we do understand from the math of
three dimensions or less to any number
of Dimensions thousands hundreds of
thousands or millions in a nutshell if
you can find a way to frame a problem in
terms of linear algebra you unlock an
entire library of super powerful math
that probably already contains the
solution somewhere inside of it thank
you
[Music]
next we have Vector calculus calculus is
the mathematics of change Vector
calculus is the extension of calculus to
multiple Dimensions to the kind of
vector spaces that we get from linear
algebra so if regular calculus is the
study of how Y is changing relative to X
Vector calculus extends that to 3D 4D
and Way Beyond if we stick to three
dimensions the X the Y and the Z then
Vector calculus answers the questions
how are z and y changing relative to X
but also how is y changing relative to Z
and Z relative to Y and X relative to Y
and Z how are all these variables
changing relative to each other and how
does changing X Y and Z change some
arbitrary function of x y and z
foreign
[Music]
now the reason why Vector calculus is so
important to AI is because generally x y
and z are not just random numbers
they're numbers that control the
behavior of a computer model you can
think of these like knobs that were
turning to tune a radio we call numbers
that control the behavior of a computer
model parameters we also generally have
a function that measures how good each
setting of The Knobs is and we call that
the loss if we're trying to get as close
as we possibly can to matching some data
points that we collected out in the
field the loss might be the difference
between our estimate and the actual data
points in other words we have this
function that measures our error Vector
calculus lets us calculate how changing
each knob will change our error if we
know how changing each knob will change
the error we can keep turning the knob
in the direction that reduces the error
and if we do that over and over and over
again we can get to the best possible
setting of these knobs and we call that
process learning
[Music]
welcome
all right here we are the last of the
fundamental three maths is probability
Theory probability is the math of
uncertainty and the real world is full
of uncertainty if we're predicting the
weather for example we can only really
tell you what the probability of rain is
we can't ever be sure so whenever we're
building a model of the real world
whenever we're building AI probability
helps us reason about uncertainty
[Applause]
so those are the three fundamental kinds
of math behind AI all of AI boils down
to these three kinds of math so much so
that you'll sometimes hear people in the
field refer to AI as applied linear
algebra and they're only half joking
let's move on to methods these are ways
that we use the three fundamental maths
to build problem-solving approaches that
can be applied to a wide array of
specific problems the first one here
could also be considered a fundamental
math for AI but here I'm thinking of it
as the group of methods which we call
optimization optimization is the
mathematics of finding the best thing
and if that sounds incredibly vague and
Broad that's because it is optimization
is a super versatile field studying any
setting where we're trying to find the
best thing out of all things like it a
good example of this is pathfinding like
how Google Maps routes you to your
destination there the problem is to find
the best set of direct out of all
possible sets of directions most of the
time optimization is constrained meaning
there are some rules or limitations that
it has to obey in this case for example
you have to obey the rules of the road
you can't go the wrong way up a one-way
road we also have to Define what we mean
by best we have to Define some Criterion
for choosing between the things that
we're choosing between in this case that
would be the length of the path one
subfield of optimization is machine
learning which is the science of
learning from data in machine learning
our Criterion and our constraints come
from the data let's imagine that we're
trying to build a computer model that
tells us whether an image contains a dog
or a cat in this case the data would be
a collection of images each one with an
Associated label that says dog or cat
our computer model like before has these
knobs or parameters that control Its
Behavior and our optimization problem is
to find the best setting of these knobs
that minimizes our error on the data
data rather than painstakingly writing
out a solution that says you know for
example if this pixel is white and this
pixel is black then this is a cat and if
this pixel is green but this pixel is
gray then this is a dog we want instead
for the computer to optimize this
problem on its own minimizing its error
on the data and that's machine learning
that also happens to be supervised
learning a subfield of machine learning
that studies learning from data when the
data is labeled meaning when the data
includes the right answer for each
example within supervised learning you
have all sorts of methods like support
Vector machines and decision trees and
random forests
improvised learning is a subfield of
machine learning that studies learning
from data when the data doesn't include
labels for an idea of how this might
work you can look at clustering which
uses just similarities between the data
points to discover what should be
together and what should be a part and
where the separations lie there is also
self-supervised and semi-supervised
learning but those are out of scope for
this video
and next we have reinforcement learning
which is the science of learning from
action let's say you want a robot to
open a door this might sound simple but
it's actually hard for many reasons
first of all for our dog cat classifier
there is always a single right answer
cat if the image contains the cat and
dog if the image contains a dog but
there are many possible motor control
actions that the robot can take to open
a door and success is only determined
after many consecutive decisions this
means that we can't provide labels or
write answers that the system can learn
from like we can with the dog cat
example second of all every decision
that our system makes changes the
environment the dog cat classifier
doesn't have to worry that answering cat
to one image might change the next image
from a dog to a cat but if a robot moves
its arm forward it might bump the door
and close it which will change the
correct decision to make at the next
step in other words every decision it
makes has a ripple effect that changes
the correct decisions it should make in
the future and the science of solving
problems like this of learning from
action is called reinforcement learning
finally we have deep learning the
science of learning with neural networks
neural networks are just a kind of
computer model but they're really
powerful because their Universal
function approximators which means that
given the right data and the right
algorithm these models can learn to
mimic anything and to replicate any
Behavior they can learn all sorts of
different relationships no matter how
complicated and because of that they're
super versatile so you'll see them used
in supervised unsupervised
semi-supervised self-supervised and
reinforcement learning you'll often find
neural networks and deep learning listed
as subfields of their own and I think
that's wrong there's a science and a
theory underlying neural networks and a
kind of expertise in knowing how to use
them right but at their core they're
just application versions of the three
fundamental maths that can be applied to
all sorts of different problems so I'll
call them methods
and now finally we get to the
applications which you'll find on many
online lists ranked right alongside the
methods and the fundamental maths we
just described with no differentiation
you might be surprised at how short the
rest of this video is that's because I
hope I've spent the time and organized
this video so that you can see the
hierarchy here how each layer builds
upon the last so that by the time I get
to these applications I actually don't
have to spend that much time getting
into the weeds you can just know that
they use everything from the methods
which uses everything from the maths to
solve these specific classes of problems
so first is computer vision computer
vision is AI for understanding the
visual World basically AI that sees and
concretely this means any AI that has to
do with photos videos or any other kind
of digital image object detection facial
recognition image processing for
self-driving cars and automatic analysis
of medical images all of these are
computer vision
[Music]
the next one is natural language
processing which is AI for understanding
language this includes things like
speech to text like Siri or Alexa this
also includes chat Bots like Chachi BT
and other large language models
[Music]
thank you
then there's robotics which is AI for
interacting with the physical world
everyone knows what robots are you can
probably recognize Boston Dynamics spot
or the Roomba you might have in your
house but AI is important to robotics
mainly for perception which is basically
how does the robot use its sensors to
understand the world and is often highly
intertwined with computer vision and
control which is how does the robot use
that information to make decisions in
its frequently intertwined with
reinforcement learning
[Music]
there's also computational biology which
is AI for the life sciences and includes
things like automatic drug Discovery or
predicting protein structures from DNA
sequences like deepminds Alpha fold
which made headlines a couple years ago
this also includes things like a
molecular simulations and predictions of
diseases from genomics
[Music]
finally we have recommender systems
which is AI for predicting what you like
this is what people on YouTube Tick Tock
Twitter and all other sorts of social
media call the algorithm that's ai2
trying to figure out what will make you
click there are of course more subfields
and many more subfields of subfields
there's expert systems planning
reasoning symbolic Ai and many more but
we just don't have the time for that
today so let me know what you think in
the comments Please Subscribe if you
learned something today and as always
I'll see you next week
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