This is like an NFT in the physical world

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

๐Ÿ‘‡ Image
Artificially scarce

BMW can surely produce (mint ๐Ÿ˜…) 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.

๐Ÿ‘‡
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.

๐Ÿ‘‡
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.

๐Ÿ‘‡
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.

๐Ÿ‘‡
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.

๐Ÿ‘‡
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.

๐Ÿ‘‡
@oncyber_io People will hate it

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.

๐Ÿ‘‡
@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.

๐Ÿ‘‡
@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. Image

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More from @haltakov

Feb 22
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 ๐Ÿ‘‡ ImageImageImageImage
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.

๐Ÿ‘‡ ImageImage
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.

The code is open-source as well: github.com/bioinf-jku/SNNs

๐Ÿ‘‡
Read 9 tweets
Feb 18
Did you ever want to learn how to read ROC curves? ๐Ÿ“ˆ๐Ÿค”

This is something you will encounter a lot when analyzing the performance of machine learning models.

Let me help you understand them ๐Ÿ‘‡

#RepostFriday
What does ROC mean?

ROC stands for Receiver Operating Characteristic but just forget about it. This is a military term from the 1940s and doesn't make much sense today.

Think about these curves as True Positive Rate vs. False Positive Rate plots.

Now, let's dive in ๐Ÿ‘‡
The ROC curve visualizes the trade-offs that a binary classifier makes between True Positives and False Positives.

This may sound too abstract for you so let's look at an example. After that, I encourage you to come back and read the previous sentence again!

Now the example ๐Ÿ‘‡
Read 19 tweets
Feb 17
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 ๐Ÿ‘‡
There is a lot of information about rules you need to follow when evaluating your machine learning model:

โ–ช๏ธ Balance your dataset
โ–ช๏ธ Use the right metric
โ–ช๏ธ Use high-quality labels
โ–ช๏ธ Split your training and test data
โ–ช๏ธ Perform cross-validation

But this may not be enough ๐Ÿ‘‡
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.

Some examples ๐Ÿ‘‡
Read 12 tweets
Jan 18
The Internet is already decentralized, why do we need web3? ๐Ÿค”

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 ๐Ÿงต

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.

3/12
Read 15 tweets
Jan 18
How decentralized is web3 really?

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 ๐Ÿ‘‡
These are the main arguments I see regularly. Please add more in the comments.

1๏ธโƒฃ The Internet is already decentralized
2๏ธโƒฃ It is inefficient
3๏ธโƒฃ Everything can be implemented better using a centralized approach
4๏ธโƒฃ Important services are centralized

๐Ÿ‘‡
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:

moxie.org/2022/01/07/webโ€ฆ

๐Ÿ‘‡
Read 9 tweets
Jan 17
How many parameters do you need in your neural network to solve any problem? ๐Ÿค”

GPT-3 has 175 billion, MT-NLG has 530 billion and Wu Dao has 1.75 trillion.

But the truth is you only need 1 parameter. No, not 1 billion. Just a single parameter!

Let me explain ๐Ÿ‘‡
Yes, of course, I'm trolling you, but only a little bit ๐Ÿ˜

I want to show you this very cool work by @ranlot75 about how to fit an arbitrary dataset with a single parameter and the following function

github.com/Ranlot/single-โ€ฆ

๐Ÿ‘‡
@ranlot75 Here are examples of some 2D image datasets. You see the parameter alpha and the reconstructed image.

Now, let me give you some high-level intuition how this works ๐Ÿ‘‡
Read 7 tweets

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