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 ๐ ) 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.
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It is the same for NFT collections. The more expensive ones have an interesting story, are created by a famous artist, and are exclusive.
It is not only about how the image of the NFT looks. It is not about the JPEG.
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People buy it to brag
Many people will buy this car to brag in front of their friends. They probably not put it on their PFP, but will drive it to the club. Nobody will go shopping at Walmart with it. It's a status symbol.
Same for expensive PFP collections and NFTs.
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People buy it as a store of value
A regular M850i will be worth much less in 20 years, but this car will probably be more expensive. Some people will buy it as a store of value and will speculate on its price.
Many NFT collections can be regarded as a store of values too.
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People will put it in a museum
Some people may buy it and put it in a private collection or a museum, making it more valuable.
People are creating awesome NFT galleries in @oncyber_io for example and they increase their collection value with blue-chip NFTS.
Many people will hate on this as a waste of money and time. It will probably not be much talk, though, because it will only be a small amount of very rich people buying it.
And this is fine because things have different values for different people.
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@oncyber_io NFTs, however, are accessible to almost everybody. What is only available to very rich people in the physical world is now available to everybody in the digital world.
And the people that hate on it because they don't see the value get much louder.
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@oncyber_io This car is the perfect example that the things many people are enraged about with NFTs are actually not specific to crypto.
These things are a "feature" of humanity.
The difference is that the upside can now be captured by anybody and not only by the rich.
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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 ๐
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.
It sucks if your ML model can't achieve good performance, but it is even worse if you don't know it!
Sometimes you follow all the best practices and your experiments show your model performing very well, but it fails when deployed.
A thread about Sampling Bias ๐
There is a lot of information about rules you need to follow when evaluating your machine learning model:
โช๏ธ Balance your dataset
โช๏ธ Use the right metric
โช๏ธ Use high-quality labels
โช๏ธ Split your training and test data
โช๏ธ Perform cross-validation
But this may not be enough ๐
A common problem when evaluating an ML model is the Sampling Bias.
This means that your dataset contains more samples of some part of the underlying distribution than others.
The Internet is already decentralized, why do we need web3? ๐ค
This is a common critique of web3. However, decentralization on its own is not always enough - sometimes we need to agree on a set of facts.
Blockchains give us a consensus mechanism for that!
Thread ๐งต
1/12
The Internet is built of servers that communicate using open protocols like HTTP, SMTP, WebRTC etc. Everybody can set up a server and participate. It is decentralized!
However, if two servers distribute contradicting information, how do you know which one is right?
2/12
This is what blockchains give us, a way for decentralized parties to agree on one set of facts. They offer a consensus mechanism!
Imagine the blockchain as a global public database that anybody can read and nobody can falsify - every transaction/change needs to be signed.
While there is a lot of hype around web3, NFTs, and decentralized apps (dApps), there is also a lot of criticism. Today, I'll focus on the critique that web3 is actually too centralized.
Let's try to have an honest discussion ๐
These are the main arguments I see regularly. Please add more in the comments.
1๏ธโฃ The Internet is already decentralized
2๏ธโฃ It is inefficient
3๏ธโฃ Everything can be implemented better using a centralized approach
4๏ธโฃ Important services are centralized
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I was inspired to write this in part after reading this great article by @moxie pointing some of the problems with the current state of web3. If you've been living under a rock in the last weeks, make sure you check it out: