A reminder: You will be sent pre-reading/watching a few days before each session (allow 1 hour), and homework at the end of each session (allow 1 hour). To get the most out of the course you should also set aside at least 2 hours per session to go back through the notes, read some of the links provided and make sure you understand everything we've covered. So that's a minimum of 6 hours per session for maximum benefit (2 hours live, 1 hour pre-reading/watching, 2 hours revision, 1 hour homework). If you are an absolute beginner it is particularly important to do all the above.

Recordings will be provided shortly after each live session, and every participant will be offered a short private consultation during the course (more on that in a future session).

Please try to watch the following before our first session:

Optional:

The course notes are accessible at https://tinyurl.com/intro-to-ai-jan-2024 (same link for all sessions). For course discussion, you are welcome to join the Telegram group via https://t.me/+oG8d0UVr-IdkZjBk. You can also leave comments in this doc.

I look forward to seeing you at 4pm UTC on Thurs 11th Jan at https://us06web.zoom.us/j/87879903601 (same Zoom link for all sessions).

Best,

Stephen

Session 1: Fundamentals of AI

Please note, you don't need to study this section beforehand (though you are welcome to) – I will be going over it in the live session. It is also subject to change up until the session.

You can use archive.is to read Medium articles if you don't have a Medium account.

AI (Artificial Intelligence), ML (Machine Learning) and DL (Deep Learning)

Jay's Visual Intro to AI

– Seema Singh, Towards Data Science

– Audrey Lorberfeld, Towards Data Science

-> Deep Unsupervised Learning, Deep Supervised Learning, Deep Reinforcement Learning

What is Deep Learning? - MachineLearningMastery.com

The state of DL-powered AI

AI already beats the average human at a number of key tasks…

…and is expected to surpass humans on a bunch more over the coming years

Recommended newsletters and podcasts

"With so many new AI newsletters popping up, it can be overwhelming to figure out which you'd actually enjoy reading. To choose the right AI newsletter, consider your level of expertise in the field. If you're a beginner, AI Breakfast. If you know a bit more about tech, you might want a newsletter that talks about daily software and tools you could implement today, if so – Ben's Bites. And lastly, if you want a newsletter that dives deep into technical discussions and emerging research you should go with The Batch. You really won't go wrong with any of them, they're all extremely interesting reads about technology that is actively changing the world."

AI tool directories

Top AI Tool Directories:

The best case

"If we can safely harness the power of AI for human betterment,

then we can paint a utopian future our ancestors could hardly fathom.

A future free of disease and hunger,

where biotechnology has stabilised the climate and biodiversity.

Where abundant clean energy is developed in concert with AI;

Where breakthroughs in rocketry and materials sciences

have propelled humans to distant planets and moons;

And where new tools for artistic and musical expression

open new frontiers of beauty, experience, and understanding."

– THE HUMAN FUTURE: A Case for Optimism

Reducing toil:

Catalysing creativity:

Promoting health:

Improving education:

History of deep learning

– A brief history of AI - Raconteur

1950s and 1960s: Birth and Initial Excitement

Photo of Frank Rosenblatt from Professor’s perceptron paved the way for AI – 60 years too soon

Perceptron Research from the 50's & 60's, clip

The concept of a perceptron, an early type of neural network, was introduced by Frank Rosenblatt in the late 1950s.
This period saw the first wave of excitement about the potential of these models to mimic brain-like computation and solve complex problems.

1970s: First Winter

Marvin Minsky and Seymour Papert published "Perceptrons" in 1969, which highlighted the limitations of perceptrons, especially their inability to solve non-linearly separable problems (like the XOR problem). This led to a decline in interest and funding for neural network research.

“What Rosenblatt wanted was to show the machine objects and have it recognize those objects. And 60 years later, that’s what we’re finally able to do,” Joachims said. “So he was heading on the right track, he just needed to do it a million times over. At the time, he didn’t know how to train networks with multiple layers. But in hindsight, his algorithm is still fundamental to how we’re training deep networks today… He lifted the veil and enabled us to examine all these possibilities, and see that ideas like this were within human grasp.”

1980s: Revival with Backpropagation

The introduction of the backpropagation algorithm in the 1980s by David Rumelhart, Geoffrey Hinton, and Ronald Williams allowed for training of multi-layer perceptrons, overcoming the limitations pointed out by Minsky and Papert.
This led to a resurgence in interest in neural networks. The availability of more powerful computers also helped fuel this interest.

1990s: Second Winter

– What Was Actually Wrong With Backpropagation in 1986?, slide by Geoffrey Hinton

Neural networks, especially deeper ones, suffered from issues like vanishing and exploding gradients, making them difficult to train.
Other machine learning techniques like Support Vector Machines (SVMs) and simpler methods became more popular due to their better performance in various tasks and clearer theoretical foundations.

2010s: Deep Learning Revolution

This is when neural networks, especially deep neural networks, began achieving state-of-the-art results in various tasks such as image recognition (e.g., ImageNet competition), speech recognition, and machine translation. Several factors contributed to this resurgence:

Data: With the advent of the internet, there's been an explosion in the amount of data available. Neural networks, especially deep ones, perform better with more data.
Hardware/'compute': The rise of Graphics Processing Units (GPUs) for parallel computation made it feasible to train very large neural networks.
Software/algorithms: Techniques like dropout, batch normalisation, and advanced activation functions were developed, making it easier to train deeper networks.

Late 2010s to Present: Generative AI

The late 2010s marked the rise of generative models within the deep learning community. Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models and other generative techniques began to demonstrate the ability to create realistic images, sounds, and videos.
OpenAI's GPT series and models like BERT showcased the power of generative models in the realm of natural language processing, producing human-like text and achieving state-of-the-art results in numerous NLP tasks.
The potential of generative AI has expanded the horizons of what's possible in fields like art, music, entertainment, and design. AI-driven content creation tools, style transfer techniques, and deepfakes are notable outcomes of this era.
The advancements in generative AI have also brought about new challenges and ethical considerations, particularly in areas like misinformation and the authenticity of digital content.

The Triple Exponential

The notion of the "triple exponential" in AI progress refers to the rapid growth in three key areas: data, hardware, and software. Each of these areas is experiencing exponential growth, and the combination of all three has catalysed the rapid advancement of AI in recent years. Let's break down each component:

Data: The digital age has led to an explosion in the amount of data available. Every online interaction, transaction, sensor reading, etc., generates data. The availability of big data is crucial for training sophisticated AI models, especially deep learning models that require vast amounts of labelled data. The exponential growth in data availability has been a key driver in the success of modern AI.
Hardware/'compute': This refers to the exponential growth in computational power. Moore's Law famously predicted that the number of transistors on a microchip would double approximately every two years, leading to an exponential increase in processing power. While the original formulation of Moore's Law has seen some challenges, other hardware innovations like specialised AI accelerators (e.g., TPUs or GPUs) and advancements in quantum computing continue to drive rapid growth in computational capabilities.
Software/algorithms: AI algorithms and models are becoming increasingly sophisticated. Deep learning, which was conceptualised decades ago, has seen a resurgence in the 2010s due to the availability of large datasets and powerful hardware. The exponential progress in software includes not only the development of new algorithms but also the refinement of existing ones to achieve better performance.

Data science notebooks

Classic 1-feature regression

Kaggle notebook for these 3 sections

The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)

Classic 1-feature binary classification

Note: don't do this 🙃 Use logistic regression instead:

Why are we so interested in classification?

Because it turns out both text and image generation can be understood as classification problems 🤯 🥳

Text generation

For text generation, given a vocabulary of size V, predicting the next word or character in a sequence can be viewed as a classification problem with V classes. This is, in fact, how many language models work. Given a sequence of words or characters, the model predicts the probability distribution over the vocabulary for the next word or character.

(Simpler version: When a computer tries to write text, it's like playing a guessing game. Imagine you have a set of words or letters. After reading some text, the computer tries to guess the next word or letter from this set. This guessing is similar to picking the right answer from multiple choices. Many computer programs that write text use this approach. They look at the words already written and then predict the next one.)

Image generation

For image generation, if you discretize the pixel values, you can treat the problem as predicting the value of each pixel given the values of previous pixels. For example, if you're generating a grayscale image and you discretize each pixel value into 256 values (0-255), generating an image becomes a series of classification problems where the task is to predict the value of the next pixel from 256 possible classes.

(Simpler version: Creating an image on a computer is like colouring a picture dot by dot. If the picture is black and white, each dot can be any shade from pure black to pure white. When a computer makes an image, it's trying to guess the right shade for each dot. It's like picking the correct colour from a palette of 256 shades. The computer looks at the shades it has already chosen and then tries to decide the best shade for the next dot.)

Classic 2-feature binary classification

Real v artificial neurons

"The idea behind perceptrons (the predecessors to artificial neurons) is that it is possible to mimic certain parts of neurons, such as dendrites, cell bodies and axons using simplified mathematical models of what limited knowledge we have on their inner workings: signals can be received from dendrites, and sent down the axon once enough signals were received. This outgoing signal can then be used as another input for other neurons, repeating the process. Some signals are more important than others and can trigger some neurons to fire easier. Connections can become stronger or weaker, new connections can appear while others can cease to exist. We can mimic most of this process by coming up with a function that receives a list of weighted input signals and outputs some kind of signal if the sum of these weighted inputs reach a certain bias. Note that this simplified model does not mimic neither the creation nor the destruction of connections (dendrites or axons) between neurons, and ignores signal timing. However, this restricted model alone is powerful enough to work with simple classification tasks."

“We are not interested in the fact that the brain has the consistency of cold porridge.”

– Alan Turing, 1952

2-feature classification using a single perceptron

Kaggle notebook for this section

"We take a weighted sum of the inputs, and set the output as one only when the sum is more than an arbitrary threshold (theta, θ). However, according to the convention, instead of hand coding the thresholding parameter thetha, we add it as one of the inputs, with the weight -theta as shown below, which makes it learnable."

w = np.random.rand(3) # initialize weights randomly
predictions = np.ones(X.shape[0])

epochs = 10
for epoch in range(epochs):
predictions = []

for i in range(X.shape[0]):
# Compute prediction
weighted_sum = sum(w[j] * X[i, j] for j in range(len(w)))
if weighted_sum >= 0 : # above activation threshold
prediction = 1
else: # weighted_sum < 0, below activation threshold
prediction = 0
predictions.append(prediction)

# Perceptron update rule ✨

for i in range(X.shape[0]):
if y[i] == 1 and predictions[i] == 0:

# activation was too weak, increase weights
w = w + X[i]
elif y[i] == 0 and predictions[i] == 1:

# activation was too strong, decrease weights
w = w - X[i]

Geometrical interpretation of the update rule

Perceptron Learning Algorithm: A Graphical Explanation Of Why It Works

Limitations of perceptrons

	binary classification	multiclass classification
linearly separable	single perceptron	parallel perceptrons
non-linearly separable	hidden layers, almost always with non-linear activation functions (though there's a clever multilayer linear solution to the famous XOR problem)	parallel perceptrons with hidden layers (again, almost always with non-linear activation)

The vast majority of real world problems are non-linearly separable, so we need hidden layers with non-linear activation functions.

Artificial neural networks

Neural Networks on MLU Explain

Hidden layers

One of the most effective ways to allow the classification of non-linearly separable data is to introduce one or more hidden layers between the input and output layers. This architecture can approximate any continuous function to an arbitrary degree of accuracy, given a sufficient number of hidden units (a result known as the universal approximation theorem). However, while adding hidden layers makes the model more expressive, it also makes it harder to train.

Non-linear activation functions

Getting to know Activation Functions in Neural Networks. | by Hasara Samson | Towards Data Science

The basic perceptron uses a step function, which not suitable for multi-layer training. To enable the model to capture non-linearities, we can use non-linear activation functions like the sigmoid, hyperbolic tangent (tanh), or Rectified Linear Unit (ReLU).

Backpropagation/Gradient descent

With the introduction of hidden layers, the weights can't be updated using the simple perceptron update rule. Instead, we use the backpropagation algorithm, which is a way to compute the gradient of the loss function with respect to each weight by applying the chain rule. This gradient information is then used to update the weights using gradient descent or its variants.

Universal Approximation Theorem

No matter how wiggly or complex a decision boundary might be in a dataset, the Universal Approximation Theorem assures us that a neural network even with a single layer can approximate it to any desired level of accuracy.

An example of an artificial neural network

Let's see the Universal Approximation Theorem in action!

Kaggle notebook for these 2 sections

'Deep learning'

Single layer of 1000 neurons: 4,001 trainable params

3 layers of 10 neurons: 261 trainable params, same accuracy!

Why use multiple layers?

Expressiveness vs. Efficiency: While a single hidden layer can theoretically approximate any function, the number of neurons required might be impractically large. Deep networks, with multiple layers, can often represent complex functions more efficiently, with fewer total neurons. They can capture hierarchical features, which can be more compact and generalise better.
Hierarchical Feature Learning: Deep networks can learn hierarchies of features. For instance, in image processing, the first layers might capture edges, the next layers might capture shapes by combining edges, and even deeper layers might capture more complex structures. This hierarchical nature can be more suitable for many real-world tasks.
Training Dynamics: Training deep networks can have different dynamics compared to shallow ones. The way gradients flow and features are learned can vary. While training deep networks can introduce challenges (like the vanishing gradient problem), it also can lead to better generalisation in practice for many tasks.
Generalisation: Even if a single-layer network can represent a function, it doesn't mean it will generalise well to unseen data. Deep architectures, especially with regularisation techniques, might generalise better to new data by capturing the underlying data generation process more effectively.
Transfer Learning: Deep architectures, especially in domains like vision and language, have shown to be effective for transfer learning. The features learned in initial layers in one task can be useful for a variety of other tasks. This transferability might not be as effective with shallow architectures.
Modern Activation Functions: The Universal Approximation Theorem was originally proven for traditional activation functions like the sigmoid. However, modern deep networks often use other activations like ReLU (Rectified Linear Unit). ReLUs and their variants have properties that can make deep networks easier to train and more expressive with fewer neurons.

Hierarchical Feature Learning

Session 1 recordings

https://www.youtube.com/watch?v=cbqfHPa6X5Y

Previous course: https://www.youtube.com/watch?v=Vm-G263_wJU

Session 1 homework

Play with Interactive Linear Regression – GeoGebra. Randomise the data a few times and see if you can guess the best fit line.
If you didn't already, go through the Linear Regression and Neural Networks pages on MLU Explain. In particular, play with the 'Live Least-Squares Estimates' in the Linear Regression lesson, and the 'See For Yourself' section of the Neural Networks lesson.
Advanced, optional: Understand the Universal Approximation Theorem by playing with the excellent interactive A visual proof that neural nets can compute any function

Session 2 prep

Essential:

Advanced, optional:

Session 2: Large Language Models (LLMs)

What is a Large Language Model (LLM)?

What are Large Language Models (LLMs)?

Introduction to large language models

What are Large Language Models? | NVIDIA

Highly recommended:

Large Language Models from scratch

Large Language Models: Part 2

A Very Gentle Introduction to Large Language Models without the Hype | by Mark Riedl | Medium

In practice, an LLM is a language model based on the Transformer architecture, which itself is based on the Attention mechanism

Tokenization, sequencing and padding

Natural Language Processing - Tokenization (NLP Zero to Hero - Part 1)

Sequencing - Turning sentences into data (NLP Zero to Hero - Part 2)

ChatGPT has Never Seen a SINGLE Word (Despite Reading Most of The Internet). Meet LLM Tokenizers.

A simple neural network for next word prediction

Kaggle notebook: a neural network for next word prediction

😲: I almost wish I hadn't gone down that rabbit-hole--and yet--and yet--it's rather curious, you know, this sort of life!

🤖: [12, 542, 173, 12, 481, 387, 42, 16, 2876, 2877, 2878, 145, 2879, 13, 488, 37, 205, 5, 2880, 7]

Previously, we considered a neural network with 2 features and 2 classes

Here, we have ~100 features (number of words in the longest sentence) and ~5000 classes (number of distinct words), but the principle is the same – we're using a neural network as a classifier.

Conclusion from the notebook: our model kind of sucks.

Perhaps we just need more training data?

It turns out things don't get much better with more data when using simple (feedforward) neural networks (or even recurrent neural networks, RNNs, a type of neural network with some memory of previous inputs). Enter…

Transformers

Sep 2014: Bahdanau introduces the attention mechanism for recurrent neural networks

June 2017: "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence [...] entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train."

Transformer = Feed-forward neural network + Attention mechanism

The two key advantages of Transformers over recurrent neural networks (RNNs):

Long-range dependencies: RNNs struggle with long-range dependencies. Transformers, with their self-attention mechanism, can directly relate distant words or features without going through many intermediate steps.
Parallelization: One of the major advantages of Transformers over RNNs is their ability to handle sequences in parallel. RNNs process sequences step-by-step, which makes them inherently sequential and difficult to parallelize. On the other hand, Transformers can process all tokens in a sequence simultaneously, which is more suitable for modern hardware like GPUs.

Simpler:

Transformers, explained: Understand the model behind GPT, BERT, and T5

More complex:

Visual Guide to Transformer Neural Networks - (Episode 2) Multi-Head & Self-Attention

Let's build GPT: from scratch, in code, spelled out. (3.1m views)

Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI | Lex Fridman Podcast #333

The attention mechanism

The Neuroscience of “Attention”

"[Attention] allows you to look at the totality of a sentence, the Gesamtbedeutung [overall meaning] as the Germans might say, to make connections between any particular word and its relevant context… Attention allows you to travel through wormholes of syntax to identify relationships with other words that are far away — all the while ignoring other words that just don’t have much bearing on whatever word you’re trying to make a prediction about."

– A Beginner's Guide to Attention Mechanisms and Memory Networks | Pathmind

Why 'attention'?

"Take these two sentences: “Server, can I have the check?” & “Looks like I just crashed the server.” The word server here means two very different things, which we humans can easily disambiguate by looking at surrounding words. Self-attention allows a neural network to understand a word in the context of the words around it. So when a model processes the word “server” in the first sentence, it might be “attending” to the word “check,” which helps disambiguate a human server from a metal one. In the second sentence, the model might attend to the word “crashed” to determine this “server” refers to a machine."

– Transformers, Explained: Understand the Model Behind GPT-3, BERT, and T5

If you want to go deeper, check out these excellent videos from Serrano Academy:

The Attention Mechanism in Large Language Models

The math behind Attention: Keys, Queries, and Values matrices

Simpler:

More complex:

Attention Networks: A simple way to understand Self Attention | by Geetansh Kalra | Medium
Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch
Illustrated: Self-Attention
How Attention works in Deep Learning: understanding the attention mechanism in sequence models | AI Summer
The Attention Mechanism from Scratch - MachineLearningMastery.com
10.2. Attention Pooling: Nadaraya-Watson Kernel Regression (attention in the context of regression rather than neural networks)

LLMs

Best Large Language Models for 2024 and How to Choose the Right One

Open source LLMs

Ultimate Open-Source LLM Showdown (6 Models Tested) - Surprising Results!

Playgrounds

5 Amazing & Free LLMs Playgrounds You Need to Try

Running LLMs locally

Prompt engineering

Prompt Engineering 101 - Crash Course & Tips

Master the Perfect ChatGPT Prompt Formula (in just 8 minutes)!

Prompt Engineering Tutorial – Master ChatGPT and LLM Responses

The ULTIMATE Beginner's Guide to Prompt Engineering with GPT-4 | AI Core Skills

I Discovered The Perfect ChatGPT Prompt Formula

Prompt databases

'Hallucinations'/Fabrication/BS

Recommended:

Context windows

Claude's 100K Token Context Window is INSANE!

Anthropic’s new 100K context window model is insane!

Beyond context windows: Retrieval Augmented Generation

What is Retrieval-Augmented Generation (RAG)?

Using ChatGPT

GPT4 modes

Upload files

Edit prompts

Stop generating

Regenerate

Plugins

Master These 26 ChatGPT Plugins to Stay Ahead of 97% of People

Top 10 ChatGPT Plugins You Can't Miss

I Tried All New ChatGPT Plugins... And here is the BEST!

I Tried All 757 ChatGPT Plugins, These are the 6 You Need To Know

GPTs

Data Analyst

Formerly Advanced Data Analytics and Code Intepreter

Become a Data Analyst using ChatGPT! (Full Guide)

This ChatGPT Change is HUGE for Data Analysts

ChatGPT Code Interpreter Tutorial - New Open AI GPT Model!

ChatGPT Code Interpreter - Complete Tutorial including Prompt List

ChatGPT Code Interpreter AMAZING Example Uses!

ChatGPT Code Interpreter - The Biggest Update EVER!

Top 10 ways to use ChatGPT Code Interpreter

ChatGPT just leveled up big time...

ChatGPT Advanced Data Analysis - A New Era of Data Science Begins

LLM applications

Writing

Coding

Language learning

Roleplay & companionship

Search & summarisation

Perplexity:

Google:

Bing/OpenAI:

The New Bing

Summarisation:

APIs

ChatGPT in Google Sheets: a beginner's guide (101)

GPT Recipes

GPT for Work

Session 2 homework

Chat with two anonymous models side-by-side and vote for which one is better in the Chatbot Arena
Advanced: Get an OpenAI API key and create your own version of the GPT Recipes sheet using GPT for Work

Optional:

Consider running an open-source LLM locally using GPT4All or LM Studio
Deepen your understanding of how LLMs work by watching Intro to Large Language Models - Andrej Karpathy [1hr Talk], Large Language Models: Part 2 or The Attention Mechanism in Large Language Models

Session 2 recordings

https://www.youtube.com/watch?v=V2A9XtB288M

Previous course: https://www.youtube.com/watch?v=tmJ-kC8vjV8

Session 3 prep

Watch Text to Image in 5 minutes: Parti, Dall-E 2, Imagen
Watch Introducing Pika 1.0, An Idea-to-Video Platform
Browse AI-generated songs and consider creating your own on Suno

Advanced, optional:

From DALL·E to Stable Diffusion: how do text-to-image generation models work? | Tryolabs

Session 3: Multimodal AI

Multimodal AI

OpenAI:

OpenAI’s ChatGPT Has Been Supercharged!

First Look At GPT-4 With Vision

Google:

Google Gemini Shocks The World and Might Be The ChatGPT-4 Killer

Google Gemini: AlphaGo-GPT?

Gemini: Google's Latest AI Challenging GPT-4

Meta:

AnyMal: Meta's New Multimodal Genius Surpassing GPT-4

AnyMAL: A Game-Changer in Multimodal Language Models | Medium

Open source:

LLAVA: The AI That Microsoft Didn't Want You to Know About!

“LLAMA2 supercharged with vision & hearing?!” | Multimodal 101 tutorial

Microsoft Copilot & Google Duet AI

Introducing Copilot Pro: Supercharge your Creativity and Productivity

Microsoft Copilot Pro - Everything You Need to Know

Microsoft Copilot - Excel has forever changed

Top things I've learned using Microsoft 365 Copilot | Demo

A new era for AI and Google Workspace

AI in Google Docs and Gmail IS HERE!

Duet AI The Future of Work is Already Here

Duet AI: Everything YOU NEED to Know

Duet AI for Google Workspace: Generative AI tools to transform work for the better

AI image generation

Stable Diffusion SDXL 1.0 Released! | Automatic1111 WebUI

OpenAI's DALL-E 3 - The King Is Back!

V6 is FINALLY HERE - Midjourney V6 FULL BREAKDOWN

Midjourney

Comparisons:

Midjourney V6 VS DALL•E 3: Prompt Battle & Full Review

Which is better? Midjourney v6 vs. DALL-E 3 vs. Stable Diffusion XL

Best AI Image? Midjourney V6 vs DALL E 3 vs Stable Diffusion

Coming soon:

Google’s Parti AI: Magical Results! 💫

Parti

Meta's CRAZY New AI Image Creator: "CM3leon"

Introducing CM3leon, a more efficient, state-of-the-art generative model for text and images

SDXL & Controlnet

ControlNet Revolutionized How We Use AI To Generate Images

NEW ControlNet for SDXL!

How ControlNet v1.1 Is Revolutionizing AI Art Even Further

Stable Diffusion ControlNet | getimg.ai

DALLE-3: no more prompt engineering?

Dall-E 3 + ChatGPT Smokes Stable Diffusion & Midjourney! No More Prompt Engineering Needed

Midjourney

Prompt engineering:

V6 Prompt Design - Midjourney Beginner Tutorial

Advanced Midjourney V6 Guide (Pushing Boundaries of Lifelike Cinematic AI Photography)

Think Like an AI & Prompt Better in Midjourney v6

Write Prompts like THIS for Success in Midjourney V6

Face swapping:

Using GPT to enhance Midjourney prompts:

Turn ChatGPT into a Powerful Midjourney Prompt Machine

Technical

Text to Image in 5 minutes: Parti, Dall-E 2, Imagen

Text to Image: Part 2 -- how image diffusion works in 5 minutes

How AI Image Generators Work (Stable Diffusion / Dall-E) - Computerphile

Text-to-image generation explained

AI art, explained

AI voice & video

I Tested 7 AI Video Generators.. Here's The BEST!

I Tried 5 Text-to-Video AI Generators (Here's the best one)

I Tried 5 AI Video Generators for Faceless Channels (Here’s the BEST!)

This Is THE BEST AI Video Generator To Create Faceless YouTube Videos (2024 Update)

I Tried 5 of The Best AI Text-To-Video Generators & Editing Tools of 2024... (Are They Any Good?)

3 Incredible Text-to-Video AI Generators You Have To Try!

10 Best AI Tools for Video Editing & Production | SproutVideo

ElevenLabs: Generative voice AI
D-ID: Create and interact with talking avatars
invideo AI: Turn ideas into videos | AI video creator

New AI Video Generator Does Prompt to YouTube Video

This AI Tool Creates Videos in Seconds! (No Editing)

Text-To-Film: 15-Minute Video From One Prompt!

HeyGen Translate: Translate your videos seamlessly with one click, using a natural voice clone and authentic speaking style

HeyGen AI Translation Can Translate Video into ANY Language!

Capsule: AI-powered video editor

Capsule introduces its AI-powered video editor for enterprise teams | TechCrunch

Cinema/animation

Star Wars by Wes Anderson Trailer | The Galactic Menagerie

Lord of the Rings by Wes Anderson Trailer | The Whimsical Fellowship

The forgotten punk pioneer (An A.I. mockumentary)

r/aivideo - The place for AI generated videos on reddit

The World's First AI Filmmaking Course — Curious Refuge

Runway AI Film Festival 2024

Going Viral: Behind the AI-Generated Wes Anderson Trailers for Star Wars and LOTR

All-In Summit: AI film and the generative art revolution with Caleb Ward

Creating Viral Videos With AI

Animating images:

Animate MidJourney Images - Full AI Animation Workflow.

How To Animate A MidJourney Image (For Free)

New A.I Mode: Create Animations From A Single Image!

Best AI Animation Tutorial - FREE Options | Step-by-Step (Ghibli Studio Inspired)

10 Free AI Animation Tools: Bring Images to Life

Top 7 Image To Video AI Tools: Create AI Animation For FREE

The Video AI Showdown: How Do Runway Gen-2, Pika and Moonvalley Stack Up? | by Vibeke Bertelsen (Udart) | Generative AI

Runway

Create Cinematic AI Videos with Runway Gen-2

Mode 01: Text to Video
Mode 02: Text + Image to Video
Mode 03: Image to Video
Mode 04: Stylization
Mode 05: Storyboard
Mode 06: Mask
Mode 07: Render
Mode 08: Customization

Pika Labs

Create Cinematic AI Videos with Pika Labs

Mind-Blowing New AI Video Generator: Text to Video AND Image to Video with Pika Labs

Moonvalley

This Free AI Video Generator Hits Different

The Most Realistic AI Video Tool Yet!

Open source: Zeroscope and Deforum

This Free AI Video Generator is Wild!

Zeroscope Text2Video is now BETTER than RunwayML Gen2 (FREE)

How To Make Cool AI Videos (Step-By-Step)

Deforum AI Full Tutorial: Text To Video Animation

Create Amazing Videos With AI (Deforum Deep-Dive)

Deforum + Controlnet

Simulon (coming soon)

AI music

A.I. Sampling and how the Music Industry will change forever

Make a HIT Song and Music Video with AI (for Free)

How AI might make a lot of musicians irrelevant

The AI Effect: A New Era in Music and Its Unintended Consequences

Music revolution: how AI could change the industry forever

Suno:

Suno AI: Generative Music Is HERE

How to Make a FULL Song with Suno AI

It's Over - The Machines are Here - Suno.ai

MusicLM:

Live from Latent Space (Album made with Google MusicLM)

Google's MusicLM: Text Generated Music & It's Absurdly Good

Production tools:

The Best A.I. Production Tools For Music Makers! (2023)

The Best A.I. Production Tools For Music Makers PT.2! (2023)

The 4 Best AI Music Production Tools Right Now

The Best A.I. Tools for Music Producers | Artificial Intelligence

These A.I Tools Will Change How Music Is Made FOREVER

AI-Generated Music Vocals Are Crazy (New Tech)

Session 3 homework

Generate an AI image, a video or a piece of music and post it in the Telegram group, explaining what tools you used to create it

Session 3 recording

https://www.youtube.com/watch?v=X8OGwy85YVE

Previous course: https://www.youtube.com/watch?v=ke0hdyC-8E0

Session 4 prep

Optional:

Session 4: AI Futures

Books

Videos

Recent expert voices:

"Godfather of AI" Geoffrey Hinton: The 60 Minutes Interview

“Godfather of AI” Geoffrey Hinton Warns of the “Existential Threat” of AI | Amanpour and Company

What was 60 Minutes thinking, in that interview with Geoff Hinton?

EMERGENCY EPISODE: Ex-Google Officer Finally Speaks Out On The Dangers Of AI! - Mo Gawdat | E252

Open AI Founder on Artificial Intelligence's Future | Exponentially

AI and the future of humanity | Yuval Noah Harari at the Frontiers Forum

Can we build AI without losing control over it? | Sam Harris

The danger of AI is weirder than you think | Janelle Shane

Will Superintelligent AI End the World? | Eliezer Yudkowsky | TED

The Urgent Risks of Runaway AI – and What to Do about Them | Gary Marcus | TED

What happens when our computers get smarter than we are? | Nick Bostrom

AGI in sight | Connor Leahy, CEO of Conjecture | AI & DeepTech Summit | CogX Festival 2023

The A.I. Dilemma - March 9, 2023

Douglas Rushkoff: I Will Not Be Autotuned - Crashing Technosolutionism

The Impact of chatGPT talks (2023) - Prof. Max Tegmark (MIT)

Recent commentary:

Artificial Intelligence: Last Week Tonight with John Oliver (HBO)

AI: Does artificial intelligence threaten our human identity? | DW Documentary

Artificial Escalation

Organisations

Life 3.0 (Max Tegmark)

Max Tegmark lecture on Life 3.0 – Being Human in the age of Artificial Intelligence

Life 3.0: Being Human in the Age of AI | Max Tegmark | Talks at Google

How to get empowered, not overpowered, by AI | Max Tegmark

Max Tegmark on Life 3.0: Being Human in the Age of Artificial Intelligence

On the Lex Fridman podcast:

Max Tegmark: Life 3.0 | Lex Fridman Podcast #1

Max Tegmark: AI and Physics | Lex Fridman Podcast #155

Max Tegmark: The Case for Halting AI Development | Lex Fridman Podcast #371

Glossary

Life 1.0 (biology): Biological life that evolves based on genetic information and cannot significantly redesign itself during its lifetime. Examples include bacteria.
Life 2.0 (culture): Life forms like humans that can learn and adapt, essentially having the ability to redesign their software (or neural patterns).
Life 3.0 (AI/biotech): A life form that can design not only its software (like Life 2.0) but also its hardware. In other words, these life forms would have the ability to drastically change or improve themselves both mentally and physically.
Intelligence: The ability to acquire and apply knowledge and skills, solve problems, and adapt to new situations. It encompasses reasoning, understanding, learning, and cognition.
Artificial Intelligence (AI): The simulation of intelligence in machines. It involves creating algorithms that allow computers to perform tasks that would typically require human intelligence. This includes tasks like visual perception, speech recognition, decision-making, and translation between languages.
Narrow Intelligence/Weak AI: An intelligent system designed and trained for a particular task. Examples include virtual personal assistants, such as Apple's Siri or image recognition software. They operate under a predefined set of rules and do not possess general problem-solving capabilities beyond their specific domain.
General Intelligence: Often associated with humans, this type of intelligence reflects the ability to understand, learn, and apply knowledge in multiple domains, reason through problems, and be adaptable to varied situations.
AGI (Artificial General Intelligence)/Strong AI: AI systems that possess intelligence comparable to human beings, allowing them to perform any intellectual task that a human being can do.
ASI (Artificial Superintelligence, Superintelligence): An intellect that is much smarter than the best human brains in practically every field, including creativity, reasoning, and social skills.

Superintelligence: Science or Fiction? | Elon Musk & Other Great Minds (Elon Musk, Stuart Russell, Ray Kurzweil, Demis Hassabis, Sam Harris, Nick Bostrom, David Chalmers, Bart Selman, Jaan Tallinn)

Friendly AI: An AI that is designed to be beneficial to humanity and safe.
Utility function: A mathematical representation of what an agent (like an AI) wants or values. It is used to describe the agent's goal or objective.
Consciousness: The subjective experience of being. Tegmark discusses various perspectives on consciousness and its potential relevance to AI.
Technological unemployment: The phenomenon where automation and AI displace jobs faster than new jobs are created.
Existential risk: A risk that threatens the entire future of humanity.
Recursive self-improvement: The process by which an AI system could potentially improve its own architecture or algorithms, leading to rapid increases in intelligence.
Intelligence explosion: The idea that once we create an AI smarter than us, it might assist in building an even smarter AI, leading to a rapid chain reaction of increasing intelligence levels.

The intelligence explosion: Nick Bostrom on the future of AI

Singularity: A hypothetical point in the future when technological growth becomes uncontrollable and irreversible, leading to unforeseeable changes to human civilization. Often, the singularity is associated with the moment when an AI undergoes recursive self-improvement, rapidly advancing its own intelligence beyond human comprehension.
The Alignment Problem: The challenge of ensuring that the goals and behaviours of AI systems align with human values and intentions. As AI systems become more autonomous and potentially more powerful, there is a concern that they might act in ways that are not beneficial or even harmful to humanity. The alignment problem emphasises the need to develop AI in such a way that it understands and respects our values and does not inadvertently cause harm or operate in unintended ways.

Summary

Definition of Life 3.0: Tegmark categorises life into three stages based on how it processes information. Life 1.0 (like bacteria) is life that evolves but doesn't learn during its lifetime. Life 2.0 (like humans) can learn and adapt but is limited by biological evolution. Life 3.0 can redesign not only its software (learning) but also its hardware (body), breaking free from evolutionary shackles.
The Promise and Peril of AI: Tegmark discusses the immense potential benefits of AI, such as curing diseases, solving complex problems, and ushering in an era of abundance. However, he also warns of the risks, including the possibility of AI surpassing human intelligence (superintelligent AI) and becoming uncontrollable.
Cosmic Perspective: He speculates on the broader cosmic implications of AI, suggesting that the transition from Life 2.0 to Life 3.0 may be a recurrent cosmic event, and that our universe might be filled with Life 3.0 civilizations.
AI Development Scenarios: Various scenarios are explored regarding how superintelligent AI might come about, including a slow development where society has time to prepare and a rapid development that could catch humanity off guard.
Values and Goals: One central concern is ensuring that superintelligent AI aligns with human values. Small misalignments could lead to unintended negative consequences. Tegmark discusses the challenge of defining these values and ensuring they are instilled in AI.
The Future of Jobs and Economy: The impact of AI on the job market is discussed, considering both the potential for job displacement and the emergence of new professions. The book also touches upon the implications for wealth distribution and possible solutions like universal basic income.
Consciousness and Identity: Tegmark delves into the philosophical questions of consciousness and identity in the age of AI, contemplating if machines could ever be conscious and what that means for our understanding of self.
The Role of Physics: Being a physicist, Tegmark relates the development of AI to the laws of physics, suggesting that understanding the cosmos can inform our understanding of intelligence and cognition.
Collective Decision Making: Tegmark argues that the future of AI should be decided collectively, emphasising the importance of global cooperation to navigate the challenges and capitalise on the opportunities of the AI revolution.
Call to Action: The book concludes with a call to action for researchers, policymakers, and the general public to engage in the conversation about the future of AI, ensuring that its development benefits all of humanity.

Aftermath scenarios

Superintelligence in SF. Part I: Pathways to Superintelligence | Thore Husfeldt
Superintelligence in SF. Part II: Failures | Thore Husfeldt: "Given the highly disruptive and potentially catastrophic outcome of rampant AI, how and why was the Superintelligence released, provided it had been confined in the first place? It can either escape against the will of its human designers, or by deliberate human action."

Summary of AI risks (beyond alignment)

Lethal Autonomous Weapons (LAWs): LAWs can independently identify and attack targets without human direction. Their introduction into warfare and policing could lead to rapid escalation of conflicts and unintended civilian casualties. The ethical concerns surrounding the removal of human judgement from life-or-death situations make these weapons highly controversial.

Slaughterbots - if human: kill()

Slaughterbots

The ideas behind 'Slaughterbots - if human: kill()' | A deep dive interview

Surveillance Systems: AI-driven surveillance technology, such as facial recognition, enhances the ability of entities, especially governments, to monitor populations. This technology can be misused by authoritarian regimes to suppress opposition, monitor dissent, and violate privacy rights, leading to a dystopian loss of privacy and freedom.

Artificial intelligence study decodes brain activity into diaglogueAI Mind Reading Experiment!

Loss of Jobs: As AI and automation technologies advance, they threaten to displace human jobs in several sectors, from manufacturing to services. While new job roles might emerge, the transition could lead to economic hardships, societal unrest, and a need for new skills training and social safety nets.

Artificial Intelligence responsible for 5% of jobs lost in May

How AI Is Already Reshaping White-Collar Work | WSJ

Is AI coming for your job? | DW Business

Wealth Concentration: AI could empower a handful of mega-corporations with significant competitive advantages, leading to monopolistic behaviours, stifling innovation, and concentrating wealth and power, further widening societal inequalities.

Yanis Varoufakis: Welcome to the age of technofeudalism
How AI will make inequality worse

Algorithmic Bias: AI systems often learn from historical data. If this data contains societal biases, the AI will replicate or even amplify these biases, leading to unfair or discriminatory decisions. Such biases can manifest in various areas like hiring, lending, or law enforcement, and can perpetuate racial, gender, or socio-economic disparities.

How AI Image Generators Make Bias Worse
ChatGPT Has A Serious Problem

Malfunction or Unexpected Behaviours: Advanced AI models can sometimes act unpredictably, especially when confronted with situations they weren't trained on. Such erratic behaviours in critical areas like healthcare, transportation, or finance could have dire consequences.

The Most Unsettling Records From the AI Incident Database
Epic AI fails

Progress on interpretability:

Security Vulnerabilities: AI systems can become targets for cyberattacks, or even worse, be used by malicious actors to find and exploit vulnerabilities in other systems. With AI-driven cyber warfare, the scale, speed, and potential damage of attacks could be unprecedented.

Hacking with ChatGPT: Five A.I. Based Attacks for Offensive Security

Dependency: A societal over-reliance on AI, especially in sectors like energy, transportation, or healthcare, could be perilous. Any malfunction, deliberate shutdown, or cyberattack could lead to systemic collapses.

The Real Danger Of ChatGPT

Misinformation & Propaganda: AI can be used to craft persuasive narratives or generate misleading content at scale, undermining truth and facilitating propaganda campaigns.

'Artificial Intelligence could pollute the world with misinformation'
What will the future of AI-powered disinformation look like? | The Stream
Fake image of explosion near Pentagon stirs concerns over artificial intelligence
AI's Disinformation Problem | AI IRL

Deepfakes: Deepfakes are the result of advanced AI models that manipulate media, often replacing one person's likeness with another. These AI-generated creations can be almost indistinguishable from genuine recordings. Their primary danger lies in spreading misinformation, creating fake evidence, or even blackmailing individuals. In the realm of politics, journalism, or legal proceedings, the presence of deepfakes can erode public trust and threaten democratic processes.

The Incredible Creativity of Deepfakes — and the Worrying Future of AI | Tom Graham | TED

Deep Fakes are About to Change Everything

Deepfake audio of Sir Keir Starmer released on first day of Labour conference

MrBeast and BBC stars used in deepfake scam videos - BBC News

AI Content Flood: As AI becomes more adept at generating content – from articles and videos to art and music – we are witnessing a surge in the volume of AI-created content. This influx has the potential to overwhelm traditional content, making it challenging for individuals to discern between human-created, genuine content and AI-generated, potentially inauthentic content.

AI Just Killed YouTube

Filter Bubbles & Echo Chambers: AI-driven platforms can trap users in information silos, exposing them only to similar viewpoints and reinforcing pre-existing beliefs. This can polarize societies and weaken the shared understanding of reality.

Beware online "filter bubbles" | Eli Pariser
How news feed algorithms supercharge confirmation bias | Eli Pariser | Big Think

Emotional & Psychological Impact: Relying on AI for companionship or replacing traditionally human roles can affect our emotional health, potentially diminishing genuine human interaction and altering our social fabric.

The Depressing Rise of AI Girlfriends
The Rise of A.I. Companions [Documentary]

AI, Copyright, and Intellectual Property: The rise of AI-generated content presents complex challenges to traditional notions of copyright and intellectual property (IP). When an AI creates a piece of music, a work of art, or a novel, who owns the rights to that work? Furthermore, AI can be used to replicate styles or mimic human creations, potentially infringing upon original works without directly copying them.

ChatGPT and Generative AI Are Hits! Can Copyright Law Stop Them?

Can artists protect their work from AI? – BBC News

AI-created artwork sparks copyright debate

A.I. Versus The Law

Environmental Impact: Training AI models, especially the larger ones, requires significant computational resources. This can have a sizable carbon footprint and further strain our planet's resources.

AI's hidden climate costs | About That

Peter Henderson: Environmental Impact of AI (and What Developers Can Do)

Statement on AI Risk

Statement on AI Risk | CAIS

Elon Musk calls for artificial intelligence pause

AI pioneer calls to stop before it’s too late | Stuart Russell

AI 'godfather' quits Google over dangers of Artificial Intelligence - BBC News

Limitations of AI

Papers:

AI and regeneration

AI and climate

Can AI Help Solve the Climate Crisis? | Sims Witherspoon | TED

AI Is Poised to Slow Climate Change. Here Are the 6 Industries Where It Can Pack the Biggest Punch (Inc, Jan 2024)

Greenhouse Gas Emissions Monitoring
Power Sector
Manufacturing
Materials Innovation
Food Systems
Road Transport

4 ways AI can help with climate change, from detecting methane to preventing fires (NPR, Jan 2024)
8 ways AI is helping tackle climate change (WEF, Jan 2024)
2024: Is this the year AI helps us fight climate change? (Sifted, Jan 2024)
The AI Revolution in Climate Science (Project Syndicate, Jan 2024)
Crowdsourcing AI Solutions for Climate Change: AI-Startup Winners at COP28 (AI for Good, Dec 2023)
Accelerating climate action with AI (Google, Nov 2023)
AI for Climate Action: Technology Mechanism supports transformational climate solutions (UNFCC, Nov 2023)
Explainer: How AI helps combat climate change (UN, Nov 2023)
Tackling climate change with machine learning (MIT Sloan, Oct 2023)
How To Fight Climate Change Using AI (Forbes, Jul 2022)

Key papers

by Climate Change AI

Tackling Climate Change with Machine Learning - A Summary

by Innovation for Cool Earth Forum

Artificial Intelligence for Climate Change Mitigation

AI & drones for reforestation

Drones and AI team up to reforest Rio de Janeiro | Technology

These seed-firing drones plant thousands of trees each day | Pioneers for Our PlanetUsing Drones to Plant 20,000,000 Trees

Tree-Planting Drones 🌳🌱 | WWF-Australia

Startup's seed-dropping drones can plant 40,000 trees a day

In Brazil, drones take flight in Rio in high-tech reforestation push (Reuters, Jan 2024)
New Tree Tech: AI, drones, satellites and sensors give reforestation a boost (Mongabay, Jul 2023)
Drones and AI: an alternative in strategic reforestation (TecScience, Apr 2023)
Seed-Planting Drones Are Reforesting Canada With Lightning Speed (Reasons to be Cheerful, Feb 2023)
How can AI help protect and sustain our global forest ecosystems - AI for Good (AI for Good, Oct 2022)

AI for talking to animals and plants

How artificial intelligence is helping scientists talk to animals - BBC News

Using AI to Decode Animal Communication with Aza Raskin

How Scientists Are Using AI Tech To Communicate With Animals

How AI is decoding the animal kingdom (FT, Jan 2024)
How AI could help us talk to animals | The Week UK (The Week, Jan 2024)
New wave of startups uses AI to decode the secret language of plants (Fast Company, Nov 2023)
Artificial Intelligence Could Finally Let Us Talk with Animals (Scientific American, Oct 2023)
How to Use AI to Talk to Whales—and Save Life on Earth | WIRED (Wired, Aug 2023)

Podcasts:

Podcasts of the Year: Talking to Animals using Artificial Intelligence | Scientific American (Science, Quickly; Dec 2023)
Talking With Animals... Using AI (Centre for Humane Technology, May 2023)
Tech Tonic presents: Can AI help us speak to animals? Part one & Part two (FT, Sep 2023)

AI to accelerate innovation

Could artificial intelligence power the future of fusion? (U. Rochester, Sep 2023)
Fast-tracking fusion energy’s arrival with AI (MIT News, Feb 2023)
DeepMind Has Trained an AI to Control Nuclear Fusion (WIRED, Feb 2022)

How Google Solved Nuclear Fusion's Big Problem

AI Cracked the Code of Nuclear Fusion to Destroy Oil and Gas

Can AI solve nuclear fusion? | Demis Hassabis and Lex Fridman

Could AI discover new mathematics/physics?

Multi-Agent Hide and Seek

Will artificial intelligence ever discover new laws of physics? (New Scientist, Nov 2022)
A.I. Is Coming for Mathematics, Too (The New York Times, Jul 2023)

Scientists begin building AI for scientific discovery (University of Cambridge, Oct 2023)
Roll over Oppenheimer: a new AI trained on the laws of physics could unlock the universe (Sifted, Aug 2023)

What is xAI, Elon Musk’s new AI company, and will it succeed? (New Scientist, Jul 2023)
What Elon Musk is really building inside his ChatGPT competitor xAI (Fortune, Nov 2023)

This AI just figured out geometry — is this a step towards artificial reasoning? (Nature, Jan 2024)
Google DeepMind’s new AI system can solve complex geometry problems (MIT Technology Review, Jan 2024)
DeepMind approaches gold standard in complex maths in latest AI breakthrough (FT, Jan 2024)
AI Matches the Abilities of the Best Math Olympians (Scientific American, Jan 2024)
DeepMind's AI finds new solution to decades-old math puzzle — outsmarting humans (The Next Web, Dec 2023)
AI scientists make ‘exciting’ discovery using chatbots to solve maths problems (The Guardian, Dec 2023)

AI to redesign economies

Design goals for a new economic system from Daniel Schmachtenberger's New Economics Series:

Lasting global peace – Conflicts prevented and solved non-violently when needed.
Thriving physical and psychological well-being for everyone. Robust health optimized, disease prevented, and where health issues do arise, they should be cured as completely as possible, as quickly as possible, addressing all causal dynamics, utilizing all the tools available, with minimum side effects.
A transparent, open, information sharing world. Where all the information that could empower people is readily available; all interests are aligned with what is true and systemically positive; disinformation is identified and discarded, etc. Choice making (governance) can only be as good as the relevant information fed into the process (sense-making). [Partial and/or corrupted information make good choicemaking impossible.]
Abundance of all meaningful goods and values for everyone in the system. Where scarcity is intentionally, progressively engineered out of the system as an essential design goal. Where economic valuation is rigorously connected to real value.
A thriving diverse ecology and biosphere. Where new products are made from old products, obsoleting waste and environmental damage from resource acquisition, in a closed loop, upcycling materials economy. With nutrient and microbiome rich soils. No industrial pollutants in the environment. Healthy coral, large fish populations, old growth forests, protected natural areas and nature integrated with the human built world, etc.
A system that supports the maximum freedom of individuals and encourages their unique self-actualization… while encouraging the greatest depth and breadth of interpersonal intimacy and synergy. All people having access to the best resources of health care, education, and creativity that are technologically possible. People incented to create and to support others to create…and to connect meaningfully with other humans…to appreciate the beauty of the world and to add beauty to it.
Good systems of choice making, not damaged by vested interest. Choices that require the participation of many people, and/or that will affect many people, that need maximum integrity and minimum bias. Processes for resolving conflicts that are structurally oriented to prefer optimal conflict resolution.
Anti-fragility and full richness of all complex systems: ecology, physiology, psychology, culture. Resilience, antifragility, health, and aliveness are proportional to self-organizing complexity. Both the safety and real value of a civilization depends on its alignment with these fundamental complex systems.
Antifragility in the presence of exponential technology. Developing the power of gods requires developing the wisdom and care of gods.

How the CIA Destroyed the Socialist Internet: Cybersyn, Part 1 | Kernel Panic | MashableThe British Guru Who Wired Chile’s Cybernetic Socialism: Cybersyn, Part 2 | Kernel Panic| MashableHow an Insurrection Strangled Chile’s Digital Utopia: Cybersyn, Part 3 | Kernel Panic | MashableCybersocialism: Project Cybersyn & The CIA Coup in Chile (Full Documentary by Plastic Pills)

There were almost two internets. Then, the CIA destroyed one (Mashable, Feb 2022)
Enter third-wave economics (FT, Oct 2021)

Hayek promoted markets over central planning on the basis that successful economies need to be run via some form of decentralised collective intelligence. At the time, the best (or least-worst) form of this decentralised collective intelligence was the market. In 2024, however, is it possible that AI and blockchain technology together constitute a new and improved form of economic decentralised collective intelligence, superior to the market mechanism - what some are calling third-wave economics, or Cybersyn 2.0?

So You Want to Be a Sorcerer in the Age of Mythic Powers?

What is the Philosophers Stone? Introduction to Alchemy - History of Alchemical Theory & Practice

"We believe Artificial Intelligence is our alchemy, our Philosopher’s Stone – we are literally making sand think."

– The Techno-Optimist Manifesto

From Sand To Silicon: The Making of a Chip | Intel

How To: Turn Sand Into Silicon Chips

"The philosopher's stone is a mythic alchemical substance capable of turning base metals such as mercury into gold or silver. It is also called the elixir of life, useful for rejuvenation and for achieving immortality; for many centuries, it was the most sought-after goal in alchemy."

So You Want to Be a Sorcerer in the Age of Mythic Powers... (The AI Episode) - The Emerald

Josh Schrei "Mythic Powers in the Age of AI"

"The rise of Artificial Intelligence has generated a rush of conversation about benefits and risks, about sentience and intelligence, and about the need for ethics and regulatory measures. Yet it may be that the only way to truly understand the implications of AI — the powers, the potential consequences, and the protocols for dealing with world-altering technologies — is to speak mythically…"

Wise AI:

"It’s up to liberal democracies to demonstrate institutional co-evolution as a third-way between degenerate anarchy and an AI Leviathan."

…similar to Daniel Schmachtenberger's Third Attractor framework:

In Search of the Third Attractor, Daniel Schmachtenberger (part 1)

In Search of the Third Attractor, Daniel Schmachtenberger (part 2)

AI & Moloch: We already have a misaligned superintelligence, it's called humanity

Daniel Schmachtenberger | Misalignment, AI & Moloch | Win-Win with Liv Boeree

Daniel Schmachtenberger: "Artificial Intelligence and The Superorganism" | The Great Simplification

Who is Moloch and What is the MetaCrisis?

AI, Moloch and the Genie's Lamp