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That type of work is not useful for securing a decentralized system. To secure a decentralized blockchain, you need the work to be provably wasteful. That said

by Taek 4y ago

That type of work is not useful for securing a decentralized system. To secure a decentralized blockchain, you need the work to be provably wasteful. That said, there's a new paradigm of Blockchain brewing called 'Proof of Physical Work', which accepts the centralized limitations and leverages the incentive scheme to produce useful outcomes for society. Perhaps the most interesting one right now is HiveMapper. It won't secure a decentralized blockchain, but it might still be a great value add for society.

antibasilisk 4y ago
by definition that makes it not a blockchain
alchemist1e9 4y ago
I don’t believe it’s accurate what you claim. The training of LLMs I believe has a well defined loss function with a measurable value on a specific input set. I actually already discussed this problem extensively with GPT-4 itself so I’ll leave you with parts of that discussion: > Me: Do LLMs have a well defined loss function that they optimize over? > GPT-4 Assistant: Yes, large language models (LLMs) have a well-defined loss function that they optimize over during the training process. The choice of loss function depends on the specific task the model is designed for and the architecture used. For LLMs like GPT-3, which are based on the transformer architecture and designed for natural language processing tasks, the most commonly used loss function is the cross-entropy loss. > Cross-entropy loss measures the difference between the model's predicted probability distribution over the possible output tokens (words, subwords, or characters, depending on the tokenization used) and the true probability distribution, which assigns a probability of 1 to the correct output token and 0 to all other tokens. The goal of the training process is to minimize the cross-entropy loss, which essentially means making the model's predicted probabilities as close as possible to the true probabilities. > During training, the LLM processes input sequences (e.g., sentences or paragraphs) and generates output token probabilities based on its current weights. The cross-entropy loss is computed using these probabilities and the true target tokens, and then the gradients of the loss with respect to the model's weights are calculated using backpropagation. The weights are updated using an optimization algorithm, such as stochastic gradient descent (SGD) or one of its variants like Adam or Adagrad, in order to minimize the loss. > The process of iteratively updating the model's weights to minimize the loss function is what allows LLMs to learn patterns and structure in the training data, ultimately enabling them to generate coherent text, answer questions, or perform other language-related tasks. > Me: Is it possible to restrict the training with a rules that says only these weights, say specific slices, are allowed to change, keep all other weights fixed? Is this how LLMs can be trained in a distributed fashion? > GPT-4 Assistant: Yes, it is possible to restrict the training of an LLM to specific slices or subsets of weights while keeping other weights fixed. This is a common approach used in distributed and parallel training scenarios, where multiple devices or nodes work on different parts of the model simultaneously. This approach is known as model parallelism. > In model parallelism, the model's weights are divided into smaller subsets or partitions, with each partition being assigned to a different device or node. During training, each device works only on updating the weights in its assigned partition while keeping the weights in the other partitions fixed. The gradients are computed for each partition separately, and the updates are performed locally on each device. > Once all devices complete their local updates, they communicate their updated weights with one another to synchronize the model's state. This communication can be done through various methods, such as parameter server architecture or all-reduce algorithms. The key to effective model parallelism is balancing the computational workload among the devices while minimizing the communication overhead. > Adapting this concept for the proposed proof of work and LLM training combination could involve assigning different subsets of weights to miners and having them work on updating these subsets. This would essentially turn the mining process into a form of distributed LLM training, with each miner contributing to a portion of the model's weights. However, coordinating and synchronizing the updates among miners in a decentralized environment, while maintaining the integrity and security of the blockchain, would be a significant challenge that needs to be addressed in the design.
- 6510 4y ago
  There is an AI generated spam joke inthere but I cant think of it right now. I'm much to overwhelmed [again] by the clarity of that explanation. I believe someone posted a paper talking about the riddle at the end: Apparently one can also merge weights if work is done on a partition repeatedly/accidentally. The rest of the merger seems a kind of bittorrent(?)
hu3 4y ago
What about Eth's proof of stake?
- pshc 4y ago
  Proof of stake involves lockup of capital, wasting the time value of money (despite it earning a small yield), and also taking on the risk of being slashed.
deleted 4y ago
[deleted]
sebzim4500 4y ago
So? If we start offering crypto to run sections of LLMs, then some portion of the crypto miners will move to doing something useful instead.
UncleEntity 4y ago
> To secure a decentralized blockchain, you need the work to be provably wasteful. But why does it have to be wasteful? As I understand it you just need a hard enough computation to prevent the 51% problem. Obviously if the goal was to compute something which had real value all the failed attempts would count as “provably wasteful” so would seem to fit the criteria.
- muyuu 4y ago
  > As I understand it you just need a hard enough computation to prevent the 51% problem. that's not the case, in fact hardness is barely a requirement provided you can scale up the number of sub-tasks as much as you need When the work is provably wasteful, the model simplifies a great deal. Usefulness of the work complicates the dynamics and makes many scenarios plausible that otherwise wouldn't. The game theory then doesn't follow that you'd choose a currency with extra elements of instability. I don't think there's enough evidence to make the hard requirement that it HAS to be provably wasteful. It's just that you introduce a bunch on extra, usually intractable, problems when the work is useful.

by Taek 4y ago

antibasilisk 4y ago

by definition that makes it not a blockchain

alchemist1e9 4y ago

I don’t believe it’s accurate what you claim. The training of LLMs I believe has a well defined loss function with a measurable value on a specific input set. I actually already discussed this problem extensively with GPT-4 itself so I’ll leave you with parts of that discussion: > Me: Do LLMs have a well defined loss function that they optimize over? > GPT-4 Assistant: Yes, large language models (LLMs) have a well-defined loss function that they optimize over during the training process. The choice of loss function depends on the specific task the model is designed for and the architecture used. For LLMs like GPT-3, which are based on the transformer architecture and designed for natural language processing tasks, the most commonly used loss function is the cross-entropy loss. > Cross-entropy loss measures the difference between the model's predicted probability distribution over the possible output tokens (words, subwords, or characters, depending on the tokenization used) and the true probability distribution, which assigns a probability of 1 to the correct output token and 0 to all other tokens. The goal of the training process is to minimize the cross-entropy loss, which essentially means making the model's predicted probabilities as close as possible to the true probabilities. > During training, the LLM processes input sequences (e.g., sentences or paragraphs) and generates output token probabilities based on its current weights. The cross-entropy loss is computed using these probabilities and the true target tokens, and then the gradients of the loss with respect to the model's weights are calculated using backpropagation. The weights are updated using an optimization algorithm, such as stochastic gradient descent (SGD) or one of its variants like Adam or Adagrad, in order to minimize the loss. > The process of iteratively updating the model's weights to minimize the loss function is what allows LLMs to learn patterns and structure in the training data, ultimately enabling them to generate coherent text, answer questions, or perform other language-related tasks. > Me: Is it possible to restrict the training with a rules that says only these weights, say specific slices, are allowed to change, keep all other weights fixed? Is this how LLMs can be trained in a distributed fashion? > GPT-4 Assistant: Yes, it is possible to restrict the training of an LLM to specific slices or subsets of weights while keeping other weights fixed. This is a common approach used in distributed and parallel training scenarios, where multiple devices or nodes work on different parts of the model simultaneously. This approach is known as model parallelism. > In model parallelism, the model's weights are divided into smaller subsets or partitions, with each partition being assigned to a different device or node. During training, each device works only on updating the weights in its assigned partition while keeping the weights in the other partitions fixed. The gradients are computed for each partition separately, and the updates are performed locally on each device. > Once all devices complete their local updates, they communicate their updated weights with one another to synchronize the model's state. This communication can be done through various methods, such as parameter server architecture or all-reduce algorithms. The key to effective model parallelism is balancing the computational workload among the devices while minimizing the communication overhead. > Adapting this concept for the proposed proof of work and LLM training combination could involve assigning different subsets of weights to miners and having them work on updating these subsets. This would essentially turn the mining process into a form of distributed LLM training, with each miner contributing to a portion of the model's weights. However, coordinating and synchronizing the updates among miners in a decentralized environment, while maintaining the integrity and security of the blockchain, would be a significant challenge that needs to be addressed in the design.

6510 4y ago
There is an AI generated spam joke inthere but I cant think of it right now. I'm much to overwhelmed [again] by the clarity of that explanation. I believe someone posted a paper talking about the riddle at the end: Apparently one can also merge weights if work is done on a partition repeatedly/accidentally. The rest of the merger seems a kind of bittorrent(?)

hu3 4y ago

What about Eth's proof of stake?

pshc 4y ago
Proof of stake involves lockup of capital, wasting the time value of money (despite it earning a small yield), and also taking on the risk of being slashed.

deleted 4y ago

[deleted]

sebzim4500 4y ago

So? If we start offering crypto to run sections of LLMs, then some portion of the crypto miners will move to doing something useful instead.

UncleEntity 4y ago

> To secure a decentralized blockchain, you need the work to be provably wasteful. But why does it have to be wasteful? As I understand it you just need a hard enough computation to prevent the 51% problem. Obviously if the goal was to compute something which had real value all the failed attempts would count as “provably wasteful” so would seem to fit the criteria.

muyuu 4y ago
> As I understand it you just need a hard enough computation to prevent the 51% problem. that's not the case, in fact hardness is barely a requirement provided you can scale up the number of sub-tasks as much as you need When the work is provably wasteful, the model simplifies a great deal. Usefulness of the work complicates the dynamics and makes many scenarios plausible that otherwise wouldn't. The game theory then doesn't follow that you'd choose a currency with extra elements of instability. I don't think there's enough evidence to make the hard requirement that it HAS to be provably wasteful. It's just that you introduce a bunch on extra, usually intractable, problems when the work is useful.