Transcript: One thing I would like to be able to do is be able to edit transcripts, and then after the transcript has been edited, basically rerun the steps of the pipeline that depend on the transcript.
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.
The user is looking to implement a caching mechanism to quickly summarize new content added to a pipeline. They are considering a simple approach, such as selecting the most recent items and creating a summary, as well as exploring the possibility of summarizing content on a weekly basis. The user also expresses a desire for the summarization process to involve natural language queries rather than programming, and seeks to explore methods to refine natural language programming capabilities.
The speaker is considering the research question of how to achieve distributed compute, particularly the need for parallelism in executing pipelines and AI agents. They question the potential for building a Directed Acyclic Graph (DAG) that allows for agents to dynamically contribute to it and execute in parallel, emphasizing the need for pipeline development to accommodate this level of complexity. The discussion also touches on the scalability and parallel execution potential of the mixture of experts model, such as GPT-4, and the potential for hierarchical or vector space implementation. The speaker is keen on exploring the level of parallelism achievable through mixture of experts but acknowledges the limited understanding of its full capabilities at this point. They also express curiosity about fine-tuning experts for personal data. The speaker is discussing the data they are generating and the value of the training data for their system, particularly emphasizing the importance of transforming the data to suit their context and actions. They mention meditating and recording their thoughts, which they intend to transform into a bullet point list using an AI model after running it through a pipeline. The individual also discusses making their data publicly accessible and considering using GPT (possibly GPT-3) to post summaries of their thoughts on Twitter. They also ponder the potential of using machine learning models to create a personal Google-like system for individual data. The text discusses using data chunking as a method for generating backlinks and implementing PageRank in an agent system. It mentions steep space models and the continuous updating of internal state during training. It also compares the level of context in transformer models and discusses the idea of transformer as a compression of knowledge in a language. The speaker expresses interest in understanding the concept of decay in relation to memory and its impact on the storage and retrieval of information. They draw parallels between the processing of information in their mind and the functioning of a transformer model, with the long-term memory being likened to a transformer and short-term memory to online processing. They speculate on the potential of augmenting the transformer model with synthetic training data to improve long-term context retention and recall. Additionally, they mention a desire to leverage a state space model to compile a list of movies recommended by friends and contemplate the symbiotic relationship between technology and human sensory inputs in the future. In this passage, the speaker reflects on the relationship between humans and computers, suggesting that a form of symbiosis already exists between the two. They acknowledge the reliance on technology and the interconnectedness of biological and computational intelligence, viewing them as mutually beneficial and likening the relationship to symbiosis in nature. They express a preference for living at the juxtaposition of humans and computers, while acknowledging the potential challenges and the need to address potential risks. Additionally, they mention that their thoughts on this topic have been influenced by their experiences with psychedelics. The speaker discusses the potential increase in computing power over the next five years, mentioning the impact of Moore's Law and advancements in lithography and semiconductors. They refer to the semiconductor roadmap up to 2034, highlighting the shift towards smaller measurements, such as angstroms, for increased transistor density. They emphasize that the nanometer measurements are based on nomenclature rather than actual transistor size, and the challenges in increasing density due to size limitations and cost constraints. The conversation touches on different companies' approaches to transistor density and the role of ASML in pushing lithography boundaries, before concluding with a reference to the high cost and potential decline in revenue for semiconductor production. The speaker discusses the importance of semiconductor manufacturing in the U.S. and China's significant focus in this area. They mention watching videos and reading sub stacks related to semiconductor technology, specifically referencing industry analysts and experts in the field. The speaker expresses enthusiasm for staying updated on developments and offers to share information with the listener. The conversation concludes with a friendly farewell and the possibility of future discussions.
The author discusses the need to group individual steps in composing pipelines and seeks advice on existing products from Jamie. They express the goal of improving the infrastructure for Glyph but acknowledges the current lack of resources. They emphasize focusing on the problem and making the execution of LLMs faster, and the ability to experiment with them quickly. Their ultimate aim is to understand human context and establish protocols between AI agents, while also streamlining the architecture and recording context.
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