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The thing is, the killer app of the generative AI space—at least for large language models (LLMs)—might already be ChatGPT. While people are still searching for

by fsndz 2y ago

The thing is, the killer app of the generative AI space—at least for large language models (LLMs)—might already be ChatGPT. While people are still searching for the next big application in this field (https://www.lycee.ai/blog/build-killer-app-of-generative-ai https://www.lycee.ai/blog/build-killer-app-of-generative-ai), it's possible that it has already been built. ChatGPT includes a human-in-the-loop design, avoiding the complexities of agentic workflows. You ask questions, get answers, iterate, and use the code interpreter if necessary. It’s like having a thought partner. Given the issue of hallucinations in LLMs, this might be the only feasible user experience. Users must be aware of the potential for hallucinations and have a way to iterate until the desired output is achieved. How else could this be done effectively except through a chat interface? We need to cut through the noise quickly and start leveraging the true value of LLMs (https://www.lycee.ai/blog/llm-noise-value-openai https://www.lycee.ai/blog/llm-noise-value-openai).

ben_w 2y ago
The counterpoint is that "free" ChatGPT looks really good, and that's hard to monetise because of all the other free chat interfaces. My guess is that ChatGPT is the free advert for the real product, which is their API and in particular the fine-tuning. Using the language comprehension of an LLM as part of a bigger system, RAGging them, forcing the output to comply with continuous tests, etc. does still provide other business opportunities not available to a fully general-purpose chat system with no limits to the kind of content it can produce. If you're trying to make a system that always produces valid SQL, you want it to not just pass a syntax checker but also be valid for the specific schema it's being asked about; you definitely don't want it running fully automated if there's a chance it will append "Let me know if there's anything else I can help you with!" to the end of the query. But this isn't a mere statement of the problem, people are doing that kind of thing with these tools: https://twimlai.com/podcast/twimlai/building-real-world-llm-products-with-fine-tuning-and-more/ https://twimlai.com/podcast/twimlai/building-real-world-llm-...
- fsndz 2y ago
  We all know the saying: "If it's free, you're the product." I believe the free version of ChatGPT is primarily a data acquisition tool that OpenAI uses to fine-tune its models and conduct reinforcement learning from human feedback (RLHF) to enhance their usefulness. In machine learning, data is key, and OpenAI is outpacing its competitors largely due to the vast amount of data it can gather from free ChatGPT users. It's already possible to perform Retrieval-Augmented Generation (RAG) on ChatGPT, from simple to complex cases. However, I'm somewhat skeptical about use cases that involve integrating large language models (LLMs) as part of a larger system. While these applications can help build new features, they may not lead to the creation of a 'killer app' in the generative AI space. Take text-to-SQL, for example: it's a useful tool, but you still need a human to audit the generated SQL. You can't blindly trust an LLM to create flawless SQL queries. Therefore, text-to-SQL remains more suitable for technical users rather than those with limited data analytics skills. Otherwise, it could lead to confusion. Imagine a scenario where the commercial director and the marketing director have differing views on last year's sales because they each received different answers from a chatbot. That would be a nightmare. We haven't even managed to make simple dashboards universally acceptable, so diving into hallucination-driven data analytics seems premature. Even with RAG implementations in companies and tools like text-to-SQL, the user interface often remains a chat application. I haven't seen much variation in this regard. While there's a lot of talk about the need to reimagine user interfaces, there's little to show for it so far. Chat interfaces seem to be the most suitable format for LLMs, and ChatGPT is likely the 'killer app' in this domain. OpenAI will likely explore further monetization strategies in the future, perhaps by incorporating ads in some manner. https://www.lycee.ai/blog/ai-reliability-challenge https://www.lycee.ai/blog/ai-reliability-challenge
  - AmericanChopper 2y ago
    How is it possible for OpenAI to use their users to train their model? I will sometimes as ChatGPT a couple of questions, and sometimes it gives me useful responses and I’m done, and other times it gives me useless responses and I’m done. Presumably OpenAI would need a way of discerning the useful output from the useless output in order to do this, but I’m not providing any feedback about which is which when I use the product.

by fsndz 2y ago

ben_w 2y ago

The counterpoint is that "free" ChatGPT looks really good, and that's hard to monetise because of all the other free chat interfaces. My guess is that ChatGPT is the free advert for the real product, which is their API and in particular the fine-tuning. Using the language comprehension of an LLM as part of a bigger system, RAGging them, forcing the output to comply with continuous tests, etc. does still provide other business opportunities not available to a fully general-purpose chat system with no limits to the kind of content it can produce. If you're trying to make a system that always produces valid SQL, you want it to not just pass a syntax checker but also be valid for the specific schema it's being asked about; you definitely don't want it running fully automated if there's a chance it will append "Let me know if there's anything else I can help you with!" to the end of the query. But this isn't a mere statement of the problem, people are doing that kind of thing with these tools: https://twimlai.com/podcast/twimlai/building-real-world-llm-products-with-fine-tuning-and-more/ https://twimlai.com/podcast/twimlai/building-real-world-llm-...

fsndz 2y ago
We all know the saying: "If it's free, you're the product." I believe the free version of ChatGPT is primarily a data acquisition tool that OpenAI uses to fine-tune its models and conduct reinforcement learning from human feedback (RLHF) to enhance their usefulness. In machine learning, data is key, and OpenAI is outpacing its competitors largely due to the vast amount of data it can gather from free ChatGPT users. It's already possible to perform Retrieval-Augmented Generation (RAG) on ChatGPT, from simple to complex cases. However, I'm somewhat skeptical about use cases that involve integrating large language models (LLMs) as part of a larger system. While these applications can help build new features, they may not lead to the creation of a 'killer app' in the generative AI space. Take text-to-SQL, for example: it's a useful tool, but you still need a human to audit the generated SQL. You can't blindly trust an LLM to create flawless SQL queries. Therefore, text-to-SQL remains more suitable for technical users rather than those with limited data analytics skills. Otherwise, it could lead to confusion. Imagine a scenario where the commercial director and the marketing director have differing views on last year's sales because they each received different answers from a chatbot. That would be a nightmare. We haven't even managed to make simple dashboards universally acceptable, so diving into hallucination-driven data analytics seems premature. Even with RAG implementations in companies and tools like text-to-SQL, the user interface often remains a chat application. I haven't seen much variation in this regard. While there's a lot of talk about the need to reimagine user interfaces, there's little to show for it so far. Chat interfaces seem to be the most suitable format for LLMs, and ChatGPT is likely the 'killer app' in this domain. OpenAI will likely explore further monetization strategies in the future, perhaps by incorporating ads in some manner. https://www.lycee.ai/blog/ai-reliability-challenge https://www.lycee.ai/blog/ai-reliability-challenge
- AmericanChopper 2y ago
  How is it possible for OpenAI to use their users to train their model? I will sometimes as ChatGPT a couple of questions, and sometimes it gives me useful responses and I’m done, and other times it gives me useless responses and I’m done. Presumably OpenAI would need a way of discerning the useful output from the useless output in order to do this, but I’m not providing any feedback about which is which when I use the product.