Future of Science and Technology Q&A (August 16, 2024)

The Stephen Wolfram Podcast - A podcast by Wolfram Research

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Stephen Wolfram answers questions from his viewers about the future of science and technology as part of an unscripted livestream series, also available on YouTube here: https://wolfr.am/youtube-sw-qa Questions include: ​​What do you view as the best strategies for reducing or eliminating hallucination/confabulation right now? Is there any chance that we'll be able to get something like confidence levels along with the responses we get from large language models? - ​​I love this topic (fine tuning of LLMs); it's something I'm currently studying. - The AI Scientist is an LLM-based system that can conduct scientific research independently, from generating ideas to writing papers and even peer-reviewing its own work. How do you see this technology impacting the development of Wolfram|Alpha and other knowledge-based systems in the future? - ​​It's fascinating the difference in response from LLMs/as to how you pose your questions. - ​​I have found that giving key terms and then asking the LLM to take the "concepts" and relate them a particular way seems to work pretty well. - How we are going to formalize the language structures arising from this microinformatization, which was capable of creating such a semantic syntax that we had not observed through structuralism? - Why is being rude and "loud" to the model always the most efficient way to get what you want if the one-shot fails? I notice this applies to nearly all of them. I think it's also in the top prompt engineering "rules." I always feel bad even though the model has no feelings, but I need the proper reply in the least amounts of questions. - AI Scientist does what you're describing. The subtle difference is that it is generating plausible ideas, creating code experiments and then scoring them–question is whether this approach can/should be extended with Alpha? - How soon do you think we'll have LLMs that can retrain in real time? - What's your take on integrating memory into LLMs to enable retention across sessions? How could this impact their performance and capabilities? - Do you think computational analytics tools are keeping up with the recent AI trends? - Would it be interesting to let the LLM invent new tokens in order to compress its memories even further? - Philosophical question: if one posts a Wolfram-generated plot of a linear function to social media, for media is math, should it be tagged "made with AI"? It's a social media's opinion probably–just curious. A math plot is objective, so different than doing an AI face swap, for example. - For future archeologists–this stream was mostly human generated. - Professor_Neurobot: Despite my name, I promise I am not a bot.

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