Future of Science and Technology Q&A (August 16, 2024)
Sep 23, 2024
auto_awesome
Stephen Wolfram, a pioneer in computational science, engages with viewers' questions on the future of science and technology. He discusses methods to minimize hallucination in large language models and the importance of prompt engineering. Wolfram envisions AI, notably the AI Scientist, transforming scientific research and peer review, blending innovative ideas with traditional processes. He also reflects on the ethical responsibilities of humans when AI makes mistakes, navigating the intricate relationship between language, computation, and decision-making.
Addressing hallucination in large language models (LLMs) necessitates utilizing them alongside systems like Wolfram Alpha for enhanced computational accuracy.
The integration of Retrieval-Augmented Generation Technology (RAG) can significantly boost LLM performance by providing relevant context for better output relevance.
The future of scientific research will increasingly rely on leveraging AI tools to augment human intuition and creativity in problem-solving.
Deep dives
Addressing Hallucination and Confabulation in LLMs
Reducing hallucination and confabulation in large language models (LLMs) is a significant challenge. Current LLMs operate in ways that simulate human responses but struggle with formal computational tasks that humans can do poorly. A strategy to tackle this issue involves using LLMs as linguistic interfaces while leveraging systems like Wolfram Alpha for computation and knowledge. This method has shown promise, especially as LLMs have become adept at generating code in the Wolfram Language, allowing for a better interplay between natural language and formal computation.
Retrieval-Augmented Generation Technology (RAG)
The introduction of Retrieval-Augmented Generation Technology (RAG) offers a sophisticated approach to enhancing LLM performance. RAG utilizes vector databases to match user queries with relevant text by associating sentences with numerical vectors representing their meanings. This technique primes LLMs to produce more relevant outputs by leveraging contextual retrieval from training data, thereby improving their ability to respond accurately to prompts. By integrating this technology with LLMs, more accurate and contextually aware outputs can be generated.
The Nature of Machine Learning and Reliability
Machine learning systems, particularly neural networks, often produce outputs based on a compilation of computational patterns that fit the desired results rather than employing formal procedural logic. This led to the view that machine learning effectively builds a 'stone wall' of computations, utilizing approximations rather than perfect calculations. Consequently, while machine learning may yield satisfactory results in many cases, it is less reliable for tasks requiring absolute precision. Thus, tasks necessitating high reliability should lean on formal computation rather than machine learning techniques.
Exploring Human-AI Collaboration in Scientific Research
The future of scientific research will likely depend on the collaboration between humans and AI systems, where AI tools enhance and expand human capabilities rather than replace them. Automation facilitates a more productive workflow that enables researchers to tackle complex problems efficiently, acting more as tools which help to explore the computational universe. Concepts like rheoliology illustrate how automated systems can generate and evaluate new scientific ideas. Nevertheless, genuine originality and relevant results depend on the quality of human intuition guiding the exploration process.
The Complexity of Fine-Tuning Large Language Models
Fine-tuning LLMs presents complexities that may not yield the expected performance improvements. Initial training rounds produce a foundational model based on vast datasets, but subsequent fine-tuning can inadvertently cause the model to forget critical prior knowledge. This presents a tricky scenario where ensuring the model retains its original learning while adapting to new tasks becomes paramount. Current methods like zero-shot learning and the integration of RAG suggest more effective paths forward than traditional fine-tuning, highlighting the necessity for further exploration in optimizing LLM performance.
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.
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode