Transcript: In some kind of ideal world, I guess the best thing to do would be when you add a new entry to the log, is that that entry is put into the larger context of the whole. Think, I think that's like what you would want to happen. And that also kind of assumes that you can figure out or infer what the larger context of the whole is. Or maybe that is an iterative process that lets you figure that out. I'm a bit unsure. But that's like kind of the sense that I'm getting from this and thinking about it. And right, if I want to support, you know, say my conversations, text messages with friends. Yeah, like having that as a separate data source is interesting, but again, if I can just throw it all inside of a LLM context window, can it begin to understand like the greater overall theme of that message in some ways? Like given this thing, you know, look at everything and now place this inside of the greater context of everything. Yeah, it's curious to say the least. And maybe it's worth like literally just shoving in like as much context as we can and generating this every time. And what I mean by that is like when we get a new thing in, is tokenize it, figure out how many tokens there are, and then go through the latest N entries and basically build it up to be like 64,000 minus N tokens and shove all of that into GPT-4 and see if it can put whatever I just wrote in the context of everything else that has been written already. That would be like one way of kind of just like brute forcing your way into some contextual understanding. So that would be like maybe an interesting last step or a step that can be executed completely asynchronously. And I mean, in fact, like probably we need the pipeline to be executing in a more asynchronous fashion anyhow, but this is kind of directional, like where to go.
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The individual has discovered that working backward from a desired result with a large language model is surprisingly effective, especially when detailing the problem forward seems challenging. This backward approach has simplified the problem and resulted in the use of GPT-4 for data transformation within the context window, improving the process. An automatic metadata generation pipeline is emerging, where data transformations are added as needed, potentially storing transformations for future use based on query relevance. This system will generate an extensive amount of synthetic data, allowing for the extraction of relevant information through queries fed into the model at later stages, rather than having to pre-determine all questions.
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.
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The speaker describes their experience of partially understanding a podcast, particularly a term "Socratic search space," while on a walk and expresses a desire to delve deeper into its meaning. They prefer an interactive approach where they can ask a device to provide references and contextual explanations, as opposed to receiving a summary generated by an AI model like GPT, which might lack the most recent uses of the term. They are skeptical about the capability of language models to provide a comprehensive understanding, given that they might not recognize terms with minimal occurrences in training data. The speaker envisions a system that could compile and present relevant information in a coherent way, enhancing their grasp of the podcast's content and making the learning process more meaningful.
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The speaker is contemplating how to ensure a substrate recognizes the relationship between two related but unlinked entries. They consider whether to trust the system's ability to connect them or address the issue using the Cray layer. The role of metadata is questioned; whether it could enhance the process or complicate it. Ultimately, the speaker is weighing the benefits of a simpler approach against a more complex but precise one.
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A shared 'brain' is being discussed as a platform for asynchronous voice note conversations where metadata could enhance understanding and visualization of conversational threads. The speaker suggests a focus on DEMO rather than DEC as a fork in the road, believing it better suits the work they've been doing with building prototypes. A group experiment is proposed with four members to delve into how these voice notes can overlap and interconnect, with the idea of marking chapters within responses to clarify dialogue. The concept also touches on the nuances of information retrieval, preferring vector databases over direct text searches, hinting at a similarity to the speaker's initial voice note exchanges with Savannah after meeting on a dating app. Voice communication offers significant advantages as a medium, and there's an idea presented here that its power should extend beyond just live conversations. Current messaging apps are filled with voice notes that are often difficult to search, filter, or respond to, though iMessage now has transcripts, which are generally reliable and useful once you've listened to the original voice note. The ability to refer back to transcribed voice notes can aid in crafting thoughtful responses and engaging in more meaningful discussions. The sender of the message suggests that by embracing this approach to communication, we could enhance our conversations and is curious to see how it will develop.
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The author is reflecting on the challenges of effectively showcasing their work on the internet, particularly in relation to portfolios and resumes. They express frustration with the limitations of resumes in capturing the depth of their experience and contributions. Additionally, they discuss the ongoing financial and practical challenges of maintaining online projects and the importance of preserving past work for the benefit of future creators. The author considers using archive.org as a potential solution but expresses reservations about outsourcing this responsibility to a non-profit organization. They ultimately prioritize the use of such resources for preserving knowledge that benefits the broader community rather than their own personal or professional work. The speaker is exploring the idea of preserving their work and experiences in a meaningful and sustainable way. They express concerns about relying on external platforms like archive.org and consider alternatives such as hosting their own content and encoding it into a lower fidelity medium. They also discuss the concept of creating their own encapsulation and representation of their work, which they hope will be more long-term sustainable. The text discusses the idea of creating a collaborative storytelling and writing platform that acts as a memory time capsule by archiving and snapshotting links. It addresses the challenge of link rot and suggests that decentralized hosting and a network of machines could potentially help in the future. The text discusses the concept of a scoped IPFS that functions similar to RAID, where each file is known only once but stored multiple times based on its significance. It also touches on the importance of data permanence on the internet, addressing concerns about archiving family photos and trusting companies like iCloud to maintain data indefinitely. The author questions if they should trust these companies and expresses uncertainty about the longevity of their data stored on such platforms.
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The speaker is considering how to categorize inputs for a burrito-like system, focusing on what constitutes a minimum ingredient for a filling, using metadata like voice notes, images, and GPS tags. They ponder the need to explicitly connect related inputs, such as a photo and a voice note about the same subject, or whether temporal and spatial proximity should implicitly link them. The speaker also reflects on the holistic context influencing inputs, including mood and environment, questioning how far explicit bundling should go. Ultimately, they imply that inputs with similar timing and location could be considered related without the need for explicit connection, likening this to lab notes.