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 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!
@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.
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?
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!
👇
@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 😀).
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 👇
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.
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 🧵
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.