Transcript: How funny is it that we use computers to build computers? I know it's like a total high thought, basically, but all the way down to the manufacturing process of semiconductors, we are using that, like computational lithography is a thing. And even before we get to that, you know, we're using tools, what is it called, like cadence and these kind of things, to actually do the VLSI and all the designs for the circuits and getting them laid out intelligently in a way that we can actually etch them at incredibly high detail levels onto a piece of silicon. It's pretty amazing. It's fascinating to me to see the co-evolution of technology and humanity, really. I think that is more what this means to me, is that, as humans and biological intelligence, we value this other kind of intelligence so highly that we will go to almost the limits of our knowledge of physics to be able to create it and make it better. And using the computational power that we have today to continue to extend that, I think is just a fascinating thing overall. What it says exactly is unsure to me, but my intuition, again, is this kind of balance or harmony between biological and computational intelligence. I think there's a lot of warring, you know, that I think you can view it as the AI is going to take over the world or whatever. And to some degree, that's already the case. We are on our phones, so I think it's all a matter of perspective. And maybe the takeover of the world is a little bit more explicit, like eliminate all of humanity. But again, I think this is a strange thing, right? Because humanity as it exists already is fundamentally changed as a result. If you just magically took away everyone's phones tomorrow, like you took them all offline and all of computation, we'd be fucked. We'd be fucked. And so in some sense, we already have this massive dependence on technology. And I don't suspect that that's going to go away. And I also don't suspect that technology will kill us. I don't see why that would be the case fundamentally. We are co-evolving. There is a symbiotic relationship between humanity or biological intelligence and computational intelligence. There's a symbiotic relationship. And does that mean it's perfect all of the time? I don't think so. But we are symbiotes. I think it's important not to forget that. And I don't suspect that that's going to go away. If anything, I think it's only going to go stronger. It is a kind of dependence, but also the technology doesn't exist without us at the moment. And maybe that's why people think in the future, because at some point the computer is going to get so intelligent that it won't need us anymore. I also, I don't know, I guess it depends on basically how well it can reproduce itself. If it can fully reproduce itself without us, maybe. Maybe that's the possibility. But, at the moment, at least, as far as I see it, biological intelligence is extremely good at reproduction. And I also suspect that the symbiosis will probably only continue to exist. Like, we are moving into a world where the computers are going to be more highly integrated in our daily lives than they ever have been. And that's probably a good thing. And again, like, for me, personally, if a computer is going to be integrated into my brain, there are parts of that that I wish to not be networked. Like, I don't want my head to be calling out to an OpenAI server. That is a future that I find to be untenable. Not to say that OpenAI and huge servers will not exist. I know they will, for a fact. But I suspect that it's going to be much more for business reasons than it is going to be for personal reasons. Like, if a computational intelligence is in my head, like literally in my head, that needs to be done at a fairly local basis or level. Are we going to be fully cyborgs? I don't fucking know. But I do think that there is something to be said for this symbiosis, and that being the kind of direction that we are going, because how else are you going to get out of the AI trap, basically? Yeah, you can create AGI, but if we're not a symbiote with it, yeah, maybe it does kill us. But if we're in a symbiotic relationship, then there's no reason, because it is mutually beneficial. Like, that is what we should be aiming for, is computational intelligence and biological intelligence being mutually beneficial for each other. That's ultimately what we need to be aiming for. To do anything else is silly. Thank you.
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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