Decentralized Artificial Intelligence

> The academic literature is ridden with examples of state of the art (SOTA) models that weren’t reproducible

Probably too late, but Elsevier could fix its bad reputation if they required relevant academic papers to submit reproducible code and subjected them to automated testing.

Do you want Terminator? Because this is how you get Terminator. A blockchain-based distributed giant AI could go rogue and it would be very difficult to stop it.

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 other 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.

The use of the Lightning Network combined with a proposed 402 response code is an interesting concept for addressing some of these challenges: https://github.com/lightninglabs/aperture

It could provide a decentralized and efficient way to facilitate payments for dataset access, training, and inferencing to incentivize data sharing and model usage.

Yes, there is a lot of bunk AI safety discussions. But there are legitimate concerns as well. AI is close to human level. Logically they become dangerous, specially if given autonomy and bad goals. Many of the accredited researchers recognize this.

There is some level that you can discuss AI safety without AI expertise (specially as of a few years ago where everything as so uncertain), but I think currently you need a lot of awareness of physical and computational limits. Taking those limits into account, we're clearly very close to human level intelligences that can scale in unpredictable ways (probably not "grey goo" ways), but potentially dangerous ways under various scenarios, including manipulating our digital lives if there are humongous AI systems controlling everything as we are in danger of getting into as a society.

I think there's also a lot of elitism toward humanities implied that you should try to get past too. Humanities have a lot of insights about human nature, even if not all of it is reliable. See philosophers like Derek Parfit.

(in case you're wondering, I've implemented a few AIs mostly RL algorithms)

If the problem is version control, why not use git? It’s also easy to fork, and it’s much cheaper than a blockchain.