Vlad Profile picture
Mar 3, 2022 11 tweets 7 min read Read on X
When machine learning met crypto art... they fell in love ❤️

The Decentralized Autonomous Artist (DAA) is a concept that is uniquely enabled by these technologies.

Meet my favorite DAA - Botto.

Let me tell you how it works 👇
Botto uses a popular technique to create images - VQGAN+CLIP

In simple terms, it uses a neural network model generating images (VQCAN) guided by the powerful CLIP model which can relate images to text.

This method can create stunning visuals from a simple text prompt!

👇
Creating amazing images, though, requires finding the right text prompt

Botto is programmed by its creator - artist Mario Klingemann (@quasimondo), but it creates all art itself. There is no human intervention in the creation of the images!

Botto is trained by the community 👇
@quasimondo The Botto Community

Botto asks its community for feedback and learns from it. Every week, Botto creates 350 new images and the members of the community vote for the pieces they like.

The feedback is then incorporated into the creations for the next weeks.

👇
@quasimondo At the end of every week, the best piece is sold at auction.

And Botto has been quite successful - it has generated $1.9M from 19 auctions. The most expensive piece, Scene Precede, sold for $430,000!

superrare.com/bottoproject

👇
@quasimondo Now, this is a lot of money, so how are they used?

The money goes back to the community!

The mechanism is quite interesting, but first, I need to tell you about the $BOTTO token.

👇
@quasimondo $BOTTO is a crypto coin that serves as a membership token for the community.

Only people holding $BOTTO are allowed to vote on the art. Every member gets voting points proportional to how much $BOTTO they have staked.

$BOTTO can be bought on decentralized exchanges.

👇
@quasimondo The $BOTTO supply is limited to max 100,000,000 tokens. In fact, the supply is decreasing - after every auction, Botto uses the proceeds to buy $BOTTO and burn it.

This makes the remaining supply more valuable and drives the price up. That's how the Botto community profits.

👇
@quasimondo Additionally, $BOTTO holders can earn more tokens by providing liquidity on UniSwap or by staking.

The tokenomics of the project is quite interesting because it creates incentives for people to stake their tokens and train Botto to become a better artist.

👇
@quasimondo And finally, Botto's presence is not limited to the Metaverse!

It travels the world accompanied by its creator @quasimondo to present its work at various events and in physical galleries.

👇
@quasimondo If you want to learn more about Botto, follow it @bottoproject and visit the website botto.com.

I also highly recommend reading this thread about how Botto was created:

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More from @haltakov

Jul 5, 2022
Zero-Knowledge Proofs 0️⃣📘

How can I prove to you that I know a secret, without revealing any information about the secret itself?

This is called a zero-knowledge proof and it is a super interesting area of cryptography! But how does it work?

Thread 🧵
Let's start with an example

Peggie and Victor travel between cities A and B. There are two paths - a long path and a short path. The problem is that there is a gate on the short path for which you need a password.

Peggie knows the password, but Victor doesn't.

👇
Victor wants to buy the password from Peggie so he can use the short path.

But what if Victor pays Peggie, but she lied and she didn't know the password? How can Peggie prove to Victor she knows the password, without actually revealing it?

They use a zero-knowledge proof 👇
Read 20 tweets
Mar 30, 2022
Launching a charity project for Ukraine 🇺🇦

Me and @ianbydesign teamed up to build @RescueToadz - an NFT collection raising funds for humanitarian aid via @Unchainfund. Many thanks to @cryptoadzNFT for the support!

rescuetoadz.xyz

It's unlike any other NFT, though👇
@ianbydesign @RescueToadz @Unchainfund @cryptoadzNFT Trustless

Rescue Toadz looks like a regular NFT collection at first - you can mint a toad and you get an NFT in your wallet.

100% of the mint fee is directly sent to @Unchainfund - an organization that provides humanitarian aid to Ukraine and that has already raised $9M!

👇 Image
@ianbydesign @RescueToadz @Unchainfund @cryptoadzNFT The process is completely trustless and automatic! All the logic is coded in the smart contract which cannot be changed and which everybody can inspect.

You trust the code, not us! We have no way to steal the funds even if we wanted (we don't 😀).

etherscan.io/address/0x5760…

👇 Image
Read 6 tweets
Mar 25, 2022
Dealing with imbalanced datasets 🐁 ⚖️ 🐘

Real world datasets are often imbalanced - some of the classes appear much more often than others.

The problem? You ML model will likely learn to only predict the dominant classes.

What can you do about it? 🤔

Thread 🧵 #RepostFriday
Example 🚦

We will be dealing with an ML model to detect traffic lights for a self-driving car 🤖🚗

Traffic lights are small so you will have much more parts of the image that are not traffic lights.

Furthermore, yellow lights 🟡 are much rarer than green 🟢 or red 🔴.
The problem ⚡

Imagine we train a model to classify the color of the traffic light. A typical distribution will be:
🔴 - 56%
🟡 - 3%
🟢 - 41%

So, your model can get to 97% accuracy just by learning to distinguish red from green.

How can we deal with this?
Read 14 tweets
Mar 22, 2022
Machine Learning Explained 👨‍🏫

PCA

Principal Component Analysis is a commonly used method for dimensionality reduction.

It's a good example of how fairly complex math can have an intuitive explanation and be easy to use in practice.

Let's start from the application of PCA 👇 Image
Dimensionality Reduction

This is one of the common uses of PCA in machine learning.

Imagine you want to predict house prices. You get a large table of many houses and different features for them like size, number of rooms, location, age, etc.

Some features seem correlated 👇
Correlated features

For example, the size of the house is correlated with the number of rooms. Bigger houses tend to have more rooms.

Another example could be the age and the year the house was built - they give us pretty much the same information.

We don't want that 👇
Read 16 tweets
Mar 18, 2022
s this formula difficult? 🤔

This is the formula for Gradient Descent with Momentum as presented in Wikipedia.

It may look intimidating at first, but I promise you that by the end of this thread it will be easy to understand!

Thread 👇

#RepostFriday
The Basis ◻️

Let's break it down! The basis is this simple formula describing an iterative optimization method.

We have some weights (parameters) and we iteratively update them in some way to reach a goal

Iterative methods are used when we cannot compute the solution directly
Gradient Decent Update 📉

We define a loss function describing how good our model is. We want to find the weights that minimize the loss (make the model better).

We compute the gradient of the loss and update the weights by a small amount (learning rate) against the gradient.
Read 8 tweets
Mar 16, 2022
Machine Learning Formulas Explained 👨‍🏫

For regression problems you can use one of several loss functions:
▪️ MSE
▪️ MAE
▪️ Huber loss

But which one is best? When should you prefer one instead of the other?

Thread 🧵 Image
Let's first quickly recap what each of the loss functions does. After that, we can compare them and see the differences based on some examples.

👇
Mean Square Error (MSE)

For every sample, MSE takes the difference between the ground truth and the model's prediction and computes its square. Then, the average over all samples is computed.

For details, check out this thread:


👇
Read 20 tweets

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