Transcript: One interesting thing is that the cost of compute, or rather the amount of compute that will be available is only going to go up. So being able to take advantage of that compute and do lots and lots of processing or post-processing does seem extremely relevant to me, especially as architectures continue to evolve on the hardware side is, assuming we can support something like this and do massively parallel inference, being able to do a lot of parallel post-processing via large language models seems very reasonably done and probably fairly important as well.
The author is considering the dilemma between renting and buying AI hardware, particularly GPUs, for a company that requires significant compute resources to take off. Renting encourages minimal use of funds, which conflicts with the need for extensive GPU utilization to create something noteworthy. The author suggests that constantly running GPUs at full capacity for inference is a unique strategy that could provide a competitive edge by allowing real-time, high-performance applications. This approach implies a constant inference process on data, making it more accessible and valuable for sorting and classifying, a concept the author is pondering on.
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The article "The Bitter Lesson" shared by Raphael emphasizes the idea of relying on computation to achieve greater capabilities in artificial intelligence, rather than complex feature extraction methods. It underlines the notion that the accelerating pace of computation enables more significant advancements in AI. The possibility of tackling problems by increasing computational resources is highlighted, particularly in the context of contextual AI. The article suggests that models like Mamba, which employ state space techniques, may offer potential avenues for this approach.
The writer expresses enthusiasm for the potential of recent technological advancements, specifically with regard to enhancing individual engagement and benefit rather than corporate application. They believe in the potential of mobile devices to run large language models, ultimately changing how individuals interact with computers and information. They draw parallels between early computing and the current focus on corporate-oriented technology, expressing a preference for the democratization of such capabilities. The writer feels optimistic about the direction of technology and its potential for widespread value, despite current perceptions.
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The distributed execution pipeline is a top priority, particularly as the focus shifts towards retrieval. It's crucial to be able to distribute queries across multiple computers or GPUs.
In the first bucket, the focus is on achieving AI-level parallelism, creating a better pipeline, enabling the execution of different LLM tasks in parallel, and allowing future agents to add information to an execution graph. This parallelization is crucial for distributed systems processing and likely to advance the distribution and parallel running of models. The second bucket involves implementing transformations, such as converting unstructured transcripts into organized bullet point lists, and making this adaptable and viable through JSON. The goal is to seamlessly convert text into a GitHub issue, providing instructions for transformation and capturing context to refine models.