cuckoo VS aperture

Asymmetric PoW algorithms, such as Cuckoo Cycle [1] or the poorly named Equihash [2] (which is not a hash function) do not lend themselves to password hashing, since a given instance can have 0 or 1 or many solutions.

[1] https://github.com/tromp/cuckoo

[2] https://en.wikipedia.org/wiki/Equihash

The full technical report describing the LOCKSS forerunner to bitcoin may be downloaded at [1]. Interestingly, LOCKSS used a memory bound Proof-of-Work, where both prover and verifier perform a random walk in a 1GB table. But the prover had to do this many times, to obtain some final hash with many leading zeroes. This was before the invention of asymmetric PoW systems like Cuckoo Cycle [2] where the PoW can be verified with no memory use.

[1] https://www.researchgate.net/publication/31869581_Preserving...

[2] https://github.com/tromp/cuckoo

A non-hashcash-style PoW scheme is Cuck(at)ooCycle.

The use of scrypt as underlying hash function is a rather poor choice though, as scrypt's memory hardness makes PoW verification unnecessarily expensive.

It's perfectly possible to make a memory hard PoW that's instantly verifiable, by using something other than hashcash. Examples include Cuckoo Cycle [1], and Equihash [2].

[1] https://github.com/tromp/cuckoo

[2] https://en.wikipedia.org/wiki/Equihash

Non-Solution #8: Cuckoo Cycle https://github.com/tromp/cuckoo Why: At least a few people have looked at it, and any attacker is far more likely to directly attack the blockchain itself, than my server (which doesn't get involved with the blockchain) Why not: The "mathematical specification" https://github.com/tromp/cuckoo/blob/master/doc/mathspec is woefully inadequate, their "C spec" focuses more on ASCII art than actual readability https://github.com/tromp/cuckoo/blob/master/doc/spec and as https://handshake.org/files/handshake.txt points out, cannot be easily adjusted in difficulty. Also, I would need to implement it from scratch, but I guess I'll have to do that anyway.

The hashcash proof-of-work scheme that bitcoin uses is vulnerable to Grover's quantum search algorithm, that can find a solution in the 2^76 search space for the current target difficulty in roughly sqrt(2^76) = 2^38 quantum hashing steps, for a 2^38 factor speedup.

Other proof-of-work schemes (e.g. finding cycles in graphs [1]) are not vulnerable to quantum speedup.

[1] https://github.com/tromp/cuckoo

[1] https://github.com/tromp/cuckoo

It's valuable to examine the challenges in machine learning without assuming decentralization as a solution:

> High Cost and Resource Requirements

For training and local inferencing use, quantization may help. Problem becomes local via quantization vs. remote full tensor use. Solution may involve distributed inferencing. Techniques like model distillation can help create smaller, more efficient models for inferencing.

> Data Privacy

For training, some private datasets may be needed. For local inferencing use, determining what needs to be inferenced locally vs. what needs to be run remotely may be useful. Problem becomes privacy scope mapped onto a marketplace to mitigate high cost and resource requirements. Techniques like model explainability (versioning) and robustness testing can help build trust in AI systems.

Complying with data privacy regulations and ensuring that AI systems adhere to legal and ethical standards can be a challenge, especially in international contexts.

> Incentives

Instead of assuming the solution when considering the problem, we assume there is an incentive to either simply train a model or use one. Problem becomes financial rewards, data access agreements, or even altruistic motivations.

> Stale Data and Reproducibility

Both the code and datasets for training the model need to be updated. Inferencing needs RAG, so the augmented reference data needs to be updated as well. Anything updated might need some type of revision control, especially if that data (or code) results in poor output. Labeling data and knowledge transfer are another problems that needs revision control.

> Interoperability

We can assume a marketplace for a ML train/inference platform is needed. We have HuggingFace, for example. The problem here is likely based on the tendency for datasets to be private, such as in the case of Llama 2. Models contain the "essence" of the dataset, but we still need RAG to ground the responses.

There does exist one technology that may assist in solving most of these issues without assuming full decentralization, and that is the Lightning Network combined with the yet to be implemented 402 response code: https://github.com/lightninglabs/aperture

You are right, it's not a strawman argument. It's an ad hominem argument. I get them confused.

While this guy may be shady as they come, and I'm not a big fan personally, the idea of combining Lightning and 402 responses is very interesting, and one I've been thinking about since Joseph and Tadge wrote the Lightning paper.

There is a means to do this today: https://github.com/lightninglabs/aperture

Consider that these entities may become self-managing at some point in the near future. If they need to pay for calls to other models, crypto-payments can be useful because they include identity and payment in one function.

As far as the vendors go, I would agree that some of them don't care about the ethical considerations and they remain focused on grabbing marketshare. That said, it's likely there we be a multitude of models running and trying to get them all talking to each other and paying for the compute required for inferencing is going to be a pain.

While it is true that there have been scams and fraudulent activities in the cryptocurrency space, it is unfair to label all blockchains as scams. Many blockchain projects have legitimate use cases and are backed by reputable organizations.

The Lightning Network is not a cryptocurrency itself but rather a layer-two scaling solution built on top of existing blockchain networks, most notably Bitcoin. It is designed to facilitate faster and cheaper transactions by creating off-chain payment channels between participants, which has risk limited to the initial payment and subsequent updates to the contract. These payment channels allow for quicker and more cost-effective transactions, while the final settlement is recorded on the underlying blockchain.

In the case of the Lightning Network, Bitcoin is the underlying cryptocurrency used for value transfer and security, while the Lightning Network enables faster and more scalable transactions by leveraging the trustless nature of the blockchain.

https://github.com/lightninglabs/aperture

I’m not sure if lsat.tech is having issues, looks like the protocol was recently renamed L402:

Crypto is useful. A very long time ago there was a conversation about killer use cases. People on the ethereum side of the room thought programmable chains were it. People on the Bitcoin side said it was micropayments and script based post dating.

I said that it was likely paying for compute resources that would be a killer use case. Implementing 402s would be that manifest: https://github.com/lightninglabs/aperture

Now we’re moving deep into the AI markets, this will be a thing, combining both.

That said, con artists will still try to hustle others.

> what's stopping anyone from bridging a non-CN/RU Intranet to CN/RU-Intranet.

If someone were considering this, here's a means to do it with 402s: https://github.com/lightninglabs/aperture

Check out LNURL-auth or perhaps LSATs

https://lsat.tech/ with this authentication specification you can gate your content with lightning payments