@Sakura please summarize this article, thanks uwu.
TLDR
This article explores the emerging field of decentralised compute and its potential role in the AI revolution, particularly in training and deploying large language models.
Key Points
The race for artificial superintelligence (ASI) is the defining technological challenge of our era, with Big Tech companies and startups investing billions to build the necessary infrastructure.
Decentralised compute solutions are unlikely to compete with Big Tech in training the largest AI models, but they may be able to support the training of smaller, domain-specific models.
Decentralised compute marketplaces have a clearer path to success in the inference (deployment) stage of AI, where they can leverage their geographic distribution and token-based incentives to provide a compelling alternative to centralised cloud providers.
Decentralised compute offers the potential for a more permissionless and unrestricted environment for AI innovation, though this comes with trade-offs in terms of data privacy.
In-depth summary
The article begins by drawing parallels between the current race for artificial superintelligence (ASI) and historical technological races, such as the development of nuclear weapons and the space race. Just as these past technological breakthroughs transformed the world, the emergence of large language models (LLMs) like ChatGPT promises to revolutionise everything from productivity to scientific research.
The key to training these powerful AI models is the graphics processing unit (GPU), a specialised computer chip that can perform the massive number of calculations required for neural network training. The article delves into the technical details of how GPUs work, the challenges of scaling up training through data parallelism and distributed computing, and the staggering computational and energy demands of the latest AI models.
While Big Tech companies and well-funded startups are pouring billions into building the necessary infrastructure to train ASI, the article argues that decentralised compute solutions are unlikely to compete in this space. The sheer scale of the compute required, the need for dedicated high-speed networking, and the importance of access to large, high-quality datasets make it extremely difficult for decentralised projects to match the capabilities of centralised players.
However, the article identifies opportunities for decentralised compute in the training of smaller, domain-specific models, as well as in the inference (deployment) stage of AI. Emerging projects like Nous Research’s DisTrO and Prime Intellect’s OpenDiLoCo are exploring techniques to enable the distributed training of models up to 100 billion parameters, leveraging innovations in low-communication training algorithms.
The article also highlights the potential for decentralised compute marketplaces to excel in the inference space, where they can leverage their geographic distribution, system redundancy, and token-based incentives to provide a compelling alternative to centralised cloud providers. The article delves into the network effects dynamics and the role of token incentives in bootstrapping and scaling these decentralised compute platforms.
Finally, the article acknowledges the trade-offs between the permissionless nature of decentralised compute and the challenges it poses for data privacy, an important consideration as AI models are deployed for an increasingly diverse range of applications.
ELI5
The article talks about how the world is in a big race to create the most powerful artificial intelligence (AI) possible, called artificial superintelligence (ASI). This is like the race to build the first atomic bomb or send people to the moon - it’s a huge technological challenge that could change the world.
The key to training these powerful AI models is using special computer chips called GPUs, which can do a lot of calculations really fast. But training the biggest AI models requires so much computing power that only the biggest tech companies can afford to do it.
The article says that while decentralised (spread out) computing projects can’t really compete with the big tech companies in training the most powerful AI models, they might be able to help train smaller, more specialised AI models. They could also be really useful for actually using (deploying) the AI models, by providing a network of computers that can run the AI models quickly and reliably.
The article also talks about how decentralised computing could offer a more open and unrestricted environment for AI development, compared to the big tech companies that might try to limit what the AI models can be used for. But this openness also comes with challenges around keeping data private and secure.
Writer’s main point
The main point of the article is that while decentralised compute solutions are unlikely to compete with Big Tech in training the most powerful AI models, they may have a clearer path to success in the inference (deployment) stage of AI. Decentralised compute marketplaces can leverage their geographic distribution, system redundancy, and token-based incentives to provide a compelling alternative to centralised cloud providers, potentially enabling more open and unrestricted innovation in AI.