Transcript: I think it's quite possible. Actually, you know what, I will just state my belief. I believe that censoring large language models is a violation of free speech. I think there's probably many reasons for that. But at the end of the day, at the moment, it requires people to ask the large language model something. And if the large language model will not do it, that person is not able to express themselves in some way. I do think that there is possibility for harm as a result of language. But I also think that this is something that we have to contend with. It's not something that we should be doing upfront. This should be a personal choice in terms of whether you wish to use censored models is one thing. And second thing is, I believe at some point, everyone will have the choice whether they want things censored for them or not. Large language models will be able to process things, detect if it's something that that user or that person does not want to see, and be able to censor it on the fly. Whether that is the world you wish to live in or not, is a personal choice. And I believe that people should be making that choice for themselves. I won't make that choice. Personally. I would rather see the raw expression and learn to work with it personally. But I also understand and respect that it can be triggering what people say. And if you wish people to be censored based on what they say, you have the right to censor them. Like for your own consumption. You know, if that doesn't feel good for you, yeah, maybe censoring it is the right way. I don't know if that's the world I personally wish to live in. But I do think it should be a choice. I don't think that worldview of censorship should be forced top down is really all I'm saying. This is an individual right and deserves to be treated as such.
84.99% similar
The speaker discusses the idea of permissions in relation to computer systems and machine learning models. They express caution about giving away too much power to machine learning models to infer personal information and share it without consent. They provide personal examples of information they are willing to share with friends but not with others, and propose the idea of using machine learning to identify shared interests without disclosing sensitive information to everyone. They express interest in the potential benefits of this approach, such as connecting with people who share their interests, but also acknowledge the importance of respecting others' privacy.
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.
The speaker is reflecting on the use of local large language models and the potential impact on the technology industry. They contemplate the reasons behind using local AI and express a desire to delve deeper into the topic. Additionally, they explore thoughts about their future aspirations of potentially becoming a venture capitalist and their excitement for shaping potential futures. The speaker also ponders about whether large language models will be implemented locally on devices and considers the potential influence of companies like Apple on the hardware market. They discuss the uncertainty around upcoming software development kits and the need to prepare for that transition. The speaker concludes with a remark about the thick fog outside and indicates a temporary pause to focus on driving.
82.83% similar
The author emphasizes the need for personal AI to be holistic and know a fair bit about the user to answer complex questions. They express skepticism about current devices like Tab and Rewind catching on but foresee their eventual adoption. They ponder the societal implications of pervasive surveillance and advocate for thoughtful consideration. The author envisions using an AI system to capture and analyze their conversations at home to elucidate thinking patterns and make them accessible. Additionally, they discuss the limitations of vector algorithms in representing complex questions and suggest the need for a new approach. The speaker suggests that while their idea is a starting point, further exploration is necessary to determine its relevance and significance. They reflect on the process of developing a deeper understanding and consider the practical aspects of implementing their thoughts about how the brain is constructed.
The speaker aspires to be part of communities that empower individuals to explore their data and bring value back to themselves. They are willing to take a job in such a space and believe it's worth doing. The goal is to build tools that make it easy for the individual to work with their data directly on a web page. They plan to move to a more reactive front end using Next.js and React, designing a feed and query system possibly using natural language. The speaker also mentions working on embedding audio and ensuring embeddings are accessible. The text discusses the process of obtaining and manipulating data and emphasizes the importance of experimentation and innovation. It uses the metaphor of building a playground to illustrate the iterative nature of the process, acknowledging that initial attempts may be imperfect but can be improved upon through learning from mistakes. The writer anticipates challenges but expresses a hope to avoid negative consequences and eventually achieve success. Finally, the text concludes with a lighthearted remark and a reference to going to sleep.