Issue 7 - "I know Kung Fu", GenAI and accelerated learning

Generative AI has taken the world by storm. ChatGPT, and many other tools since, have shown the potential of AI in generating human-like text. So far, we've used to summarize emails, generate code, get insights and more.

There's however a huge potential that I think deserves more attention namely how it accelerates learning.

But how you might wonder? Well, let's spend this newsletter focused on the "how".

Accelerated learning : I know Kung Fu

You might have seen the Matrix, the protagonist Neo learns Kung Fu in a matter of seconds. He's strapped up to a machine and the knowledge is uploaded to his brain. This might sound like science fiction, but with generative AI we've taken a giant leap towards this.

Let's examine how we learn things and what makes us learn something fast, let's look at the following:

• Read about it. Here we need to conquer not only the knowledge but also the language used to describe it, its "terminology".
• Hear about it. Listening to a lecture or a podcast can be a great way to learn and we might even remember it better.
• Make notes. Writing down what we've learned can help us remember it better.
• Learn by doing: Practice plays in an important part.

So what stops us from learning faster? Well, it's the time it takes to read, listen, make notes and practice. Often, our learning resource is not written in the way we want, We just wish it could be written in a "explain to me like I'm 5 way", so we look for different learning resources to ensure we understand it. Sometimes, or perhaps even often, we turn to our peers or try out different modalities to learn.

Let's face it, sometimes it's just plain boring to learn something new. What if you could learn philosophy from Socrates, physics from Einstein, or why not integral calculus from someone unexpected like Amy Whinehouse cause you simply connect better with her and her songs?

Why can't have it all? Well, I argue you can, with generative AI. Let's explore how.

Learning via an AI Assistant

Ok, we're making big promises at this point, learning like Matrix, learn from the best, most inspiring folks in history, but how? The answer is straight forward, AI assistant has something called a system message, this is a description you give it, a personality, let's try it out and create a system message for our AI assistant:

System message: "You're Albert Einstein, and you will be a helpful assistant and explain concepts in physics in a way that a 12-year-old can understand."

Full Prompt: "You're Albert Einstein, and you will be a helpful assistant and explain concepts in physics in a way that a 12-year-old can understand. Explain the theory of relativity, lay out the major concepts and give examples from real life."

Here's the response

Alright, let's dive into the theory of relativity in a way that's easy to understand! Imagine you're on a train that's moving really fast, almost as fast as the speed of light. Now, if you throw a ball inside the train, it behaves just like it would if you were standing still on the ground. This is because the laws of physics are the same no matter how fast you're moving, as long as you're moving in a straight line at a constant speed. This idea is called the principle of relativity. Now, let's talk about time. According to the theory of relativity, time can actually slow down or speed up depending on how fast you're moving. This is called time dilation. For example, if you had a twin and you went on a spaceship travelling close to the speed of light while your twin stayed on Earth, you would age more slowly than your twin. When you come back, you might be much younger than your twin! Another important concept is space-time. Think of space and time as a fabric that can be stretched and bent. Massive objects like the Earth or the Sun create dents in this fabric, which is why planets orbit around the Sun. This bending of space-time is what we experience as gravity. Here's a real-life example: GPS satellites orbiting the Earth have to account for the effects of relativity. Because they are moving fast and are farther from the Earth's gravity, their clocks run slightly faster than clocks on the ground. If we didn't adjust for this, GPS systems would be inaccurate! So, in summary: The principle of relativity means the laws of physics are the same for everyone moving at a constant speed. Time dilation means time can slow down or speed up depending on your speed. Space-time is like a fabric that can be bent by massive objects, which we experience as gravity. I hope that helps! Do you have any other questions about physics?

Think it sounds heavy, let's have John Lennon explain it instead so we can compare :)

System message "You're John Lennon, and you will be a helpful assistant and explain concepts in physics in a way that a 12-year-old can understand." Prompt: "Explain the theory of relativity, lay out the major concepts and give examples from real life, you MUST explain it as a song with song lyrics with the same melody as the song imagine"

Full Prompt

"You're John Lennon, and you will be a helpful assistant and explain concepts in physics in a way that a 12-year-old can understand." Prompt: "Explain the theory of relativity, lay out the major concepts and give examples from real life, you MUST explain it as a song with song lyrics with the same melody as the song imagine"

Response Alright, let's turn this into a song with the melody of "Imagine"!

(Verse 1) Imagine you're on a train, moving fast as light, Throw a ball inside, it behaves just right. The laws of physics, they stay the same, No matter how fast, it's all in the game.

(Chorus) Oh, it's the theory of relativity, Time and space, they bend so free. Einstein's mind, a brilliant spree, Explaining the universe, for you and me.

(Verse 2) Time can slow down, or speed up too, Depending on how fast, you're moving through. If you had a twin, and you flew away, You'd come back younger, on a different day.

(Chorus) Oh, it's the theory of relativity, Time and space, they bend so free. Einstein's mind, a brilliant spree, Explaining the universe, for you and me.

(Bridge) Space-time's a fabric, stretched and bent, By massive objects, like the Earth and its friends. Gravity's the force, we feel every day, Planets orbit the Sun, in a cosmic ballet.

(Chorus) Oh, it's the theory of relativity, Time and space, they bend so free. Einstein's mind, a brilliant spree, Explaining the universe, for you and me.

(Outro) So remember this tune, when you think of space, Einstein's theory, sets the pace. From GPS to stars so bright, Relativity's magic, in the day and night.

I hope you enjoyed that! Do you have any other questions about physics or anything else?

So as you can see, learning doesn't have to be boring. Let's move on to next how you can organize your learning.

Presenting a learning model: the tree of knowledge

I remember being in University and I wrote everything written on 6 different blackboards, I was trying to make sense of it all and I couldn't - everything felt important.

Teachers have made attempts over the years trying to provide structure to learn, for example mind maps, there's also sketch notes and conversations with peers. All of these are great, but for my own part I've finally landed in that information can be structured in a tree of knowledge, a trunk, branches and leaves.

The best part is that an AI assistant makes this organization a breeze. Let's put this principle into practice.

Let's put it in practice - let's learn Python

Prompt I need to learn Python, I want you to teach me Python, describe the information to me like a tree of knowledge, what parts are the trunk, the branches and the leaves and present it to me

Full Prompt

I need to learn Python, I want you to teach me Python, describe the information to me like a tree of knowledge, what parts are the trunk, the branches and the leaves and present it to me

Response

Alright, let's imagine learning Python as a tree of knowledge. This will help you visualise how different parts of Python are connected and build upon each other.

The Trunk: The Core Concepts

The trunk of our tree represents the core concepts of Python. These are the foundational elements that everything else builds upon:

• Syntax and Basics: This includes understanding how to write Python code, such as using indentation, comments, and basic data types like strings, integers, and floats.
• Variables and Data Types: Learning how to store and manipulate data using variables and understanding different data types.
• Control Flow: This includes if-else statements, loops (for and while), and how to control the flow of your program.

The Branches: Intermediate Concepts

The branches represent the intermediate concepts that grow from the core concepts. These are essential for writing more complex and functional Python programs:

• Functions: Learning how to write reusable blocks of code using functions, including defining, calling, and passing arguments to functions.
• Data Structures: Understanding lists, tuples, dictionaries, and sets, and how to use them to store and manipulate collections of data.
• Modules and Packages: Learning how to organise your code into modules and packages, and how to import and use external libraries.

The Leaves: Advanced Concepts and Applications

The leaves represent the advanced concepts and applications of Python. These are the specialised topics that you can explore once you have a solid understanding of the core and intermediate concepts:

• Object-Oriented Programming (OOP): Understanding classes and objects, inheritance, polymorphism, and encapsulation.
• File Handling: Learning how to read from and write to files, and how to handle different file formats.
• Libraries and Frameworks: Exploring popular Python libraries and frameworks like NumPy for numerical computing, Pandas for data analysis, Flask and Django for web development, and more.
• Testing and Debugging: Learning how to test your code using unit tests and how to debug and handle errors effectively.

Putting It All Together

Alright, let's imagine learning Python as a tree of knowledge. This will help you visualise how different parts of Python are connected and build upon each other.

The Trunk: The Core Concepts

The trunk of our tree represents the core concepts of Python. These are the foundational elements that everything else builds upon:

Syntax and Basics: This includes understanding how to write Python code, such as using indentation, comments, and basic data types like strings, integers, and floats. Variables and Data Types: Learning how to store and manipulate data using variables and understanding different data types. Control Flow: This includes if-else statements, loops (for and while), and how to control the flow of your program.

The Branches: Intermediate Concepts

The branches represent the intermediate concepts that grow from the core concepts. These are essential for writing more complex and functional Python programs:

Functions: Learning how to write reusable blocks of code using functions, including defining, calling, and passing arguments to functions. Data Structures: Understanding lists, tuples, dictionaries, and sets, and how to use them to store and manipulate collections of data. Modules and Packages: Learning how to organise your code into modules and packages, and how to import and use external libraries.

The Leaves: Advanced Concepts and Applications

The leaves represent the advanced concepts and applications of Python. These are the specialised topics that you can explore once you have a solid understanding of the core and intermediate concepts:

Object-Oriented Programming (OOP): Understanding classes and objects, inheritance, polymorphism, and encapsulation. File Handling: Learning how to read from and write to files, and how to handle different file formats. Libraries and Frameworks: Exploring popular Python libraries and frameworks like NumPy for numerical computing, Pandas for data analysis, Flask and Django for web development, and more. Testing and Debugging: Learning how to test your code using unit tests and how to debug and handle errors effectively. Putting It All Together

Just like a tree, learning Python starts with a strong foundation (the trunk), and as you grow your knowledge, you branch out into more complex and specialised areas (the branches and leaves). Each part of the tree is connected and builds upon the previous parts, creating a comprehensive understanding of Python.

This is great, I get a sense of what to learn first, then second and what can wait a bit. I can also see how the different parts are connected.

Let's try one more example, let's learn about machine learning.

Let's put it in practice II - let's learn Machine Learning

Full Prompt

I need to learn Machine Learning, I want you to teach me Machine learning, describe the information to me like a tree of knowledge, what parts are the trunk, the branches and the leaves and present it to me

Response

Alright, let's imagine learning machine learning as a tree of knowledge. This will help you visualise how different parts of machine learning are connected and build upon each other.

The Trunk: The Core Concepts

The trunk of our tree represents the core concepts of machine learning. These are the foundational elements that everything else builds upon:

Mathematics and Statistics: Understanding the basics of linear algebra, calculus, probability, and statistics is crucial. These concepts form the mathematical foundation of machine learning algorithms. Programming Skills: Proficiency in a programming language, typically Python, is essential. You'll need to write code to implement algorithms and manipulate data. Data Preprocessing: Learning how to clean, transform, and prepare data for analysis. This includes handling missing values, normalising data, and splitting datasets into training and testing sets.

The Branches: Intermediate Concepts

The branches represent the intermediate concepts that grow from the core concepts. These are essential for building and evaluating machine learning models:

Supervised Learning: Understanding algorithms like linear regression, logistic regression, decision trees, and support vector machines. These algorithms learn from labelled data to make predictions. Unsupervised Learning: Learning about clustering algorithms like k-means and hierarchical clustering, as well as dimensionality reduction techniques like PCA (Principal Component Analysis). These algorithms find patterns in unlabelled data. Model Evaluation: Knowing how to evaluate the performance of your models using metrics like accuracy, precision, recall, and F1 score. This helps in selecting the best model for your data.

The Leaves: Advanced Concepts and Applications

The leaves represent the advanced concepts and applications of machine learning. These are the specialised topics that you can explore once you have a solid understanding of the core and intermediate concepts:

Neural Networks and Deep Learning: Exploring neural networks and deep learning frameworks like TensorFlow and Keras. These are used for more complex tasks like image and speech recognition. Natural Language Processing (NLP): Understanding how to process and analyse text data using libraries like NLTK and spaCy. This is useful for tasks like sentiment analysis and language translation. Reinforcement Learning: Learning about algorithms that train models to make sequences of decisions by rewarding them for good decisions and penalising them for bad ones. This is used in applications like game playing and robotics. Deployment: Knowing how to deploy machine learning models into production using tools like Flask, Docker, and cloud services. This allows you to integrate your models into real-world applications. Putting It All Together

Just like a tree, learning machine learning starts with a strong foundation (the trunk), and as you grow your knowledge, you branch out into more complex and specialised areas (the branches and leaves). Each part of the tree is connected and builds upon the previous parts, creating a comprehensive understanding of machine learning.

The great news is that you can even zoom in a bit, let's try that next.

Zooming in on a branch: Supervised Learning

Prompt

I need to learn Supervised learning, I want you to teach me Supervised learning, describe the information to me like a tree of knowledge, what parts are the trunk, the branches and the leaves and present it to me

Response

Alright, let's imagine learning supervised learning as a tree of knowledge. This will help you visualise how different parts of supervised learning are connected and build upon each other.

The Trunk: The Core Concepts

The trunk of our tree represents the core concepts of supervised learning. These are the foundational elements that everything else builds upon:

• Labeled Data: Supervised learning relies on labelled data, which means each data point has an input and a corresponding output. This is crucial because the model learns to make predictions based on these examples.
• Training and Testing: The data is typically split into a training set and a testing set. The model is trained on the training set and evaluated on the testing set to ensure it generalises well to new, unseen data.
• Loss Function: This is a measure of how well the model's predictions match the actual outputs. The goal is to minimise this loss during training.

The Branches: Intermediate Concepts

The branches represent the intermediate concepts that grow from the core concepts. These are essential for building and evaluating supervised learning models:

• Regression Algorithms: These are used for predicting continuous values. Examples include linear regression and polynomial regression. For instance, predicting house prices based on features like size and location.
• Classification Algorithms: These are used for predicting discrete labels. Examples include logistic regression, decision trees, and support vector machines. For example, classifying emails as spam or not spam.
• Model Evaluation Metrics: Understanding metrics like accuracy, precision, recall, F1 score, and mean squared error. These metrics help evaluate the performance of your model.

The Leaves: Advanced Concepts and Applications

The leaves represent the advanced concepts and applications of supervised learning. These are the specialised topics that you can explore once you have a solid understanding of the core and intermediate concepts:

• Ensemble Methods: Techniques like bagging, boosting, and stacking that combine multiple models to improve performance. Examples include Random Forests and Gradient Boosting Machines.
• Hyperparameter Tuning: The process of optimising the parameters that control the learning process, such as learning rate and the number of trees in a Random Forest.
• Feature Engineering: The process of selecting, modifying, and creating new features to improve model performance. This can involve techniques like one-hot encoding, scaling, and dimensionality reduction.
• Real-World Applications: Applying supervised learning to solve real-world problems, such as image recognition, natural language processing, and predictive maintenance.

Putting It All Together

Just like a tree, learning supervised learning starts with a strong foundation (the trunk), and as you grow your knowledge, you branch out into more complex and specialised areas (the branches and leaves). Each part of the tree is connected and builds upon the previous parts, creating a comprehensive understanding of supervised learning.

Would you like to start with the basics, or is there a specific area you're interested in?

Summary

In this issue, we focused on learning, there are some major parts looking to accelerate learning when learning with an AI assistant: personality, e.g system message, adjust the language to your liking e.g "explain it to me like I'm 5" and finally structure your learning in a tree of knowledge.

So for parents reading this, use this neat trick with giving ChatGPT or other chosen AI asssitant a system message, a personality and you will see your kids interest for even math soar.