The PR folks at my current company are in full panic mode on Linkedin, judging from the passive-aggressive tone of their posts (sometimes very nearly begging customers not to use ChatGPT and friends).
They fully understand that LLMs are stealing lunch money from established information retrieval industry players selling overpriced search algorithms. For a long time, my company was deluded about being protected by insurmountable moats. I'm watching our PR folks going through the five stages of grief very loudly and very publicly on social media (particularly noticeable on Linkedin).
Here's a new trend happening these days. Upon releasing new non-fiction books to the general public, authors are simultaneously offering an LLM-based chatbot box where you can ask the book any question.
There is no good reason this should not work everywhere else, in exactly the same way. Take for example a large retailer who has a large internal knowledge base. Train an LLM on that corpus, ask the knowledge base any question. And retail is a key target market of my company.
Needless to say I'm looking for employment elsewhere.
> There is no good reason this should not work everywhere else, in exactly the same way. Take for example a large retailer who has a large internal knowledge base. Train an LLM on that corpus, ask the knowledge base any question.
Since LLM’s can’t scope themselves to be strictly true or accurate, there are indeed good reasons, like liability for false claims and added traditional support burden from incorrect guidance.
Everybody is getting so far ahead of the horse with this stuff, but we’re just not there yet and don’t know for sure how far we’re going to get.
Hmm. Hypothetically if a human on first line help desk gives advice that is so completely bad as to be a crime, are they liable or the company? Because I guess a chat-bot would definitely not be liable.
Correctness isn't one-dimensional. A wrong fast-food order might substitute or leave something out. There's essentially no chance the employee will swap in a random product from some other store.
But in this example the AI could hallucinate a statement attributed to you it actually formed by putting together reddit comments.
I'm interested to hear what these techniques are. Decreasing the generality will help, but I fail to see how that scopes the output. At best that mitigates the errors to an extent.
> Since LLM’s can’t scope themselves to be strictly true or accurate
Bing tries to solve this and succeeds somewhat. It will insert Wikipedia style citations against each of its claims. You can visit them and verify the statement if you want. And I do it often.
No reason why a future DocAI can't link to specific sections in internal documents whenever it answers a question.
> Here's a new trend happening these days. Upon releasing new non-fiction books to the general public, authors are simultaneously offering an LLM-based chatbot box where you can ask the book any question.
I couldn't get "designing data intensive applications" to explain to me how to design a graph database (from scratch, without using existing graph frameworks or technologies), but it only suggested reasons why graph databases are useful and the properties I have to keep in mind while designing it. I want to know how I can build one in practice.
Using a prompt like "Tell me how to build a graph database from scratch. Specifically, how to design the data model, implement the data storage layer, and design the query language." only gives a very vague answer. Sometimes it suggests using existing technologies.
One of my initial prompts mentioned graph databases as an example of a scalable system, so I wanted to ask it about the design properties that make it so. I figured that because it was a book about designing systems, it could give me an outline of how a graph database works in practice.
It's pretty annoying how the site erases your prompt once you receive your output. By the time it finishes loading I've half forgotten what my original question was.
Incredible results to my questions. Do these work by finding similar pieces of text from a vector DB, and then embedding those similar pieces of text in the prompt? The answers I'm getting seem to be comprehensive, as if it has considered large amounts of book text, curious how this works as there's an OpenAI token limit. I've heard this is what tools like langchain can help with, so maybe I should play around with that as this all seems like a mystery to me.
Genuinely unknown at this time. At some point this will be litigated in court, and if the parties don't end up settling, we'll then have some precedent that can answer your question.
I saw at least two examples of this here on HN. One of the books was about tech entrepreneurship 101, and I remember asking how to launch if you're a sole developer with no legal entity behind the product. I remember the answer being fairly coherent and useful. I don't have the URL handy, I suspect if you search HN for "entrepreneur book" you'll find it.
How did GPS tracking companies survive Google and Google Maps? I think there will probably be many niches to explore even as the big names work hard to compete and eventually commoditize LLMs
They fully understand that LLMs are stealing lunch money from established information retrieval industry players selling overpriced search algorithms. For a long time, my company was deluded about being protected by insurmountable moats. I'm watching our PR folks going through the five stages of grief very loudly and very publicly on social media (particularly noticeable on Linkedin).
Here's a new trend happening these days. Upon releasing new non-fiction books to the general public, authors are simultaneously offering an LLM-based chatbot box where you can ask the book any question.
There is no good reason this should not work everywhere else, in exactly the same way. Take for example a large retailer who has a large internal knowledge base. Train an LLM on that corpus, ask the knowledge base any question. And retail is a key target market of my company.
Needless to say I'm looking for employment elsewhere.