21–30 of 99 posts
There's no doubt that LLMs massively expand the ability of agencies like the NSA to perform large-scale surveillance at a higher quality. I wonder if Anthropic (or other LLM providers) ever push back or restrict these kinds of use cases? Or is that too risky for them?
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Will it really though? So far I've seen most of the "revolutionise" claims to be mainly hot air and marketing. It's possible that LLMs will suddenly make a leap in reliability and usability (e.g. much higher context window without corresponding massive increases in memory usage). But I have yet to see it. So far it's great at some specific usecases. Interacting with humans, rewriting or making up text. Summarising. A…
I use LLMs extensively in my field to automate all sorts of tasks. Need to classify a million PDF documents for cheap? Write a prompt and submit a batch job. Need to read 30,000 drilling reports to automatically scan for hazards? Done in 60 minutes. These are tasks that would have taken months of development or millions of dollars in manual effort before. It's not just hype.
Once again they are selling it like something that's for everyone right now. This is the problem. THe same with the metaverse. It has some really great usecases, but they made it out like next year we would all ditch our phones and work exclusively in a VR headset. Obviously that didn't happen, as the tech was nowhere near that and probably people don't want it either.
Also, if you really need to be sure that those 30.000 drilling reports really didn't contain any hazards, you still have to go through it all yourself. Don't forget LLMs aren't reproducible.
But no, my point was exactly that it's not just hype. There are genuine useful usecases, I totally agree.
As there were for metaverse, and probably even for blockchain (NFT not so sure tho :) I always thought they were really a solution looking for a problem). The key thing about a hype is that they overblow the potential benefits way too much though. I see this happening here once again.
I find all of the virtue signalling from AI companies exhausting.
That does sound exhausting.
Earlier quoted context omitted.
I use LLMs extensively in my field to automate all sorts of tasks. Need to classify a million PDF documents for cheap? Write a prompt and submit a batch job. Need to read 30,000 drilling reports to automatically scan for hazards? Done in 60 minutes. These are tasks that would have taken months of development or millions of dollars in manual effort before. It's not just hype.
A genuine question and not meant as a snipe: as hallucinations are an inherent “feature” of LLMs, how can you be sure of the accuracy of the model’s interpretation of those 30,000 drilling report hazards? Or what is the acceptable level of risk?
Earlier quoted context omitted.
Will it really though? So far I've seen most of the "revolutionise" claims to be mainly hot air and marketing. It's possible that LLMs will suddenly make a leap in reliability and usability (e.g. much higher context window without corresponding massive increases in memory usage). But I have yet to see it. So far it's great at some specific usecases. Interacting with humans, rewriting or making up text. Summarising. A…
I use LLMs extensively in my field to automate all sorts of tasks. Need to classify a million PDF documents for cheap? Write a prompt and submit a batch job. Need to read 30,000 drilling reports to automatically scan for hazards? Done in 60 minutes. These are tasks that would have taken months of development or millions of dollars in manual effort before. It's not just hype.
Let's say some of your drilling reports contain a pattern that indicates balrog activity, which the LLM misses. The legal or insurance context requires you to monitor and address potential balrog activity. How do you plan for these failures?
In almost every case I've seen, the plan is to not have a plan, which is another way of saying that the data doesn't matter so long as no one complains about the results.
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GPT-4 is “holy shit, this actually works, could be better but it’s so good I almost can’t believe it” while GPT-3.5 is “when it works it’s pretty great, just a pity it almost never does”. So I would assume that three letter agencies would love to take something like GPT-4 and fine tune it based on all the data they have about existing terrorists.
I'm still dealing with hallucinations nearly every time I use it.
Earlier quoted context omitted.
I use LLMs extensively in my field to automate all sorts of tasks. Need to classify a million PDF documents for cheap? Write a prompt and submit a batch job. Need to read 30,000 drilling reports to automatically scan for hazards? Done in 60 minutes. These are tasks that would have taken months of development or millions of dollars in manual effort before. It's not just hype.
Boy, I can’t wait for the foundation of my house to disappear because the LLM mis-classified a drilling report as non-hazardous. What’s the deal here with liability and accountability? That’s a serious problem when considering using these for anything other than toy problems.
Earlier quoted context omitted.
Will it really though? So far I've seen most of the "revolutionise" claims to be mainly hot air and marketing. It's possible that LLMs will suddenly make a leap in reliability and usability (e.g. much higher context window without corresponding massive increases in memory usage). But I have yet to see it. So far it's great at some specific usecases. Interacting with humans, rewriting or making up text. Summarising. A…
I use LLMs extensively in my field to automate all sorts of tasks. Need to classify a million PDF documents for cheap? Write a prompt and submit a batch job. Need to read 30,000 drilling reports to automatically scan for hazards? Done in 60 minutes. These are tasks that would have taken months of development or millions of dollars in manual effort before. It's not just hype.
Earlier quoted context omitted.
Will it really though? So far I've seen most of the "revolutionise" claims to be mainly hot air and marketing. It's possible that LLMs will suddenly make a leap in reliability and usability (e.g. much higher context window without corresponding massive increases in memory usage). But I have yet to see it. So far it's great at some specific usecases. Interacting with humans, rewriting or making up text. Summarising. A…
I use LLMs extensively in my field to automate all sorts of tasks. Need to classify a million PDF documents for cheap? Write a prompt and submit a batch job. Need to read 30,000 drilling reports to automatically scan for hazards? Done in 60 minutes. These are tasks that would have taken months of development or millions of dollars in manual effort before. It's not just hype.
Earlier quoted context omitted.
A genuine question and not meant as a snipe: as hallucinations are an inherent “feature” of LLMs, how can you be sure of the accuracy of the model’s interpretation of those 30,000 drilling report hazards? Or what is the acceptable level of risk?
You have it write a program to analyze it. I think a lot of people fail to understand that you don't always need the LLM to do the thing, have it write a program to do the thing for you.