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!
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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.
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@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.
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@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.
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@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.
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@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.
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@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.
The Cross-Entropy Loss function is one of the most used losses for classification problems. It tells us how well a machine learning model classifies a dataset compared to the ground truth labels.
The Binary Cross-Entropy Loss is a special case when we have only 2 classes.
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The most important part to understand is this one - this is the core of the whole formula!
Here, Y denotes the ground-truth label, while Ŷ is the predicted probability of the classifier.
Let's look at a simple example before we talk about the logarithm... 👇
ROC curves measure the True Positive Rate (also known as Accuracy). So, if you have an imbalanced dataset, the ROC curve will not tell you if your classifier completely ignores the underrepresented class.
Math is not very important when you are using a machine learning method to solve your problem.
Everybody that disagrees, should study the 92-page appendix of the Self-normalizing networks (SNN) paper, before using
torch.nn.SELU.
And the core idea of SNN is actually simple 👇
SNNs use an activation function called Scaled Exponential Linear Unit (SELU) that is pretty simple to define.
It has the advantage that the activations converge to zero mean and unit variance, which allows training of deeper networks and employing strong regularization.
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There are implementations both in PyTorch (torch.nn.SELU) and TensorFlow (tf.keras.activations.selu).
You need to be careful to use the correct initialization function and dropout, but this is well documented.
This is a special edition BMW 8 series painted by the famous artist Jeff Koons. A limited-edition of 99 with a price of $350K - about $200K more than the regular M850i.
If you think about it, you'll see many similarities with NFTs
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Artificially scarce
BMW can surely produce (mint 😅) more than 99 cars with this paint. The collection size is limited artificially in order to make it more exclusive.
Same as most NFT collections - they create artificial scarcity.
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Its price comes from the story
The $200K premium for the "paint" is purely motivated by the story around this car - it is exclusive, it is created by a famous artist, it is a BMW Art Car.
It is not faster, more reliable, or more economic. You are paying for the story.