51–60 of 99 posts
Going forward be very very wary of inputting sensitive information in Anthropic, OpenAI products, especially if you work for a foreign government, corporation. Listen to Edward Snowden. This guy is not fucking around.
Perhaps (optimistically) this is just a credibility-grab from Anthropic, with no basis in fact.
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Okay, but you still need to debug the program. If your program must give correct results you still need to check the program output against every case. There's no free lunch there.
Speaking generally: The program doesn't always have to give correct results. The program just needs to reduce 30k documents down to 200 documents for human review. You're comparing LLMs to a hypothetical alternative where a human reviews all 30k documents in detail. But the real alternative is often just a worse quality sieve where more errors blunder their way through the existing flawed processes. LLMs can improve…
You're right, I am comparing it to that alternative. There are fields and applications where this is necessary. I do not know if drilling reports are one of them. If you can tolerate a large false negative rate then great. But if you need to be catching 99.99% of problems then IMO you should at least be able to show your work. Taking black box output and throwing it over the wall sounds so sketchy in engineering contexts.
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NSA should be training their own GPT-4 or better model as we speak and should have been doing it for a long while now. Anything else is borderline incompetence.
And given the volume of data they likely sift through, I'd also expect them to want very small, high-throughput models for identifying targets for larger models to examine. On the flip side, LLMs must give the NSA a new challenge: a flood of garbage text generated by no-one in particular. Perhaps there will be more effort to put surveillance directly on-device as tapping networks yields more noise.
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One thing I've struggled with while applying LLMs to business problems is how others have dealt with identifying and managing system failures. 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…
Same way you manage human failures?
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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?
How can you be sure with humans doing the work?
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?
NSA should be training their own GPT-4 or better model as we speak and should have been doing it for a long while now. Anything else is borderline incompetence.
Earlier quoted context omitted.
NSA should be training their own GPT-4 or better model as we speak and should have been doing it for a long while now. Anything else is borderline incompetence.
And given the volume of data they likely sift through, I'd also expect them to want very small, high-throughput models for identifying targets for larger models to examine. On the flip side, LLMs must give the NSA a new challenge: a flood of garbage text generated by no-one in particular. Perhaps there will be more effort to put surveillance directly on-device as tapping networks yields more noise.