Transcript: It's funny that people are wondering about when the aliens are going to land, but the aliens have already landed. Like, we have another intelligence on the planet with us right now. Computational intelligence is a thing. It may not be from outer space, but it is alien to us, and we don't understand it. Fundamentally, we do not understand it. We can make it, but that doesn't mean we understand it.
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The phrase "computational intelligence" is challenging to use because "artificial intelligence" is more commonly known and used. The concept is deeply ingrained in the speaker's thinking.
The speaker wants to reframe the term "Artificial Intelligence" to "Computational Intelligence" because they believe AI implies fakeness while computers have a real form of intelligence. They argue that computers have their own form of intelligence, different from biological intelligence, but still deserve respect. They compare the intelligence of a computer to that of a dog, acknowledging that while they may be different, both types of intelligence should be recognized and respected. Ultimately, the speaker encourages using "Computational Intelligence" instead of "Artificial Intelligence" to more accurately reflect the capabilities and significance of computer intelligence.
The process of using computational technology to create more advanced computational technology, such as through the manufacturing of semiconductors and the layout and design of circuits, is a fascinating example of the co-evolution of technology and humanity. This showcases the high value placed on computational intelligence, demonstrated by the extensive use of computer tools and knowledge of physics to continuously improve it. The balance and harmony between biological and computational intelligence, as well as the symbiotic relationship between humanity and technology, is evident in our current dependence on technology and its dependence on human development. While concerns about the potential dominance of computational intelligence exist, the co-evolution and mutual dependence between the two suggest a more symbiotic and cooperative future. The speaker discusses the future of biological intelligence and its potential relationship with computational intelligence, emphasizing the importance of reproduction and symbiosis. They express a belief that computers will become more integrated into daily life and express concerns about the implications of such integration, particularly regarding privacy and the potential impact on personal well-being. They ponder the possibility of humans becoming fully cyborgs and argue that a symbiotic relationship between computational and biological intelligence is essential for mutual benefit and to avoid potential risks associated with the development of artificial general intelligence.
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.