dantheclammanVCs are starting to partner with private equity to buy up call centers, accounting firms and other "mature companies" to replace their operations with AI
cyranolol accounting….
KorhakaHow easy will it be to fool the AI into getting the company in legal trouble? Oh well.
Some would call it effortless, even.
NYC’s AI chatbot was caught telling businesses to break the law. The city isn’t taking it down | AP News - apnews.com/…/new-york-city-chatbot-misinformation…
NYC's AI chatbot was caught telling businesses to break the law. The city isn't taking it down
An artificial intelligence-powered chatbot meant to help small business owners in New York City has come under fire for dispensing bizarre advice that misstates local policies and advises companies to violate the law. Mayor Eric Adams acknowledged Tuesday that its answers were “wrong in some areas,” but the chatbot powered by Microsoft remains online. The company says it is working with city employees to improve the service. The chatbot has made false suggestions such as it being OK for restaurants to serve cheese nibbled on by rodents. Experts say the buggy bot shows the dangers of embracing new AI technology without proper guardrails.
The idea of AI accounting is so fucking funny to me. The problem is right in the name. They account for stuff. Accountants account for where stuff came from and where stuff went.
Machine learning algorithms are black boxes that can’t show their work. They can absolutely do things like detect fraud and waste by detecting abnormalities in the data, but they absolutely can’t do things like prove an absence of fraud and waste.
For usage like that you’d wire an LLM into a tool use workflow with whatever accounting software you have. The LLM would make queries to the rigid, non-hallucinating accounting system.
I still don’t think it would be anywhere close to a good idea because you’d need a lot of safeguards and also fuck your accounting and you’ll have some unpleasant meetings with the local equivalent of the IRS.
The LLM would make queries to the rigid, non-hallucinating accounting system.
ERP systems already do that, just not using AI.
But ERP is not a cool buzzword, hence it can fuck off we’re in 2025
The LLM would make queries to the rigid, non-hallucinating accounting system.
And then sometimes adds a halucination before returning an answer - particularly when it encournters anything it wasn’t trained on, like important moments when business leaders should be taking a closer look.
There’s not enough popcorn in the world for the shitshow that is coming.
You’re misunderstanding tool use, the LLM only queries something to be done then the actual system returns the result. You can also summarize the result or something but hallucinations in that workload are remarkably low (however without tuning they can drop important information from the response)
The place where it can hallucinate is generating steps for your natural language query, or the entry stage. That’s why you need to safeguard like your ass depends on it. (Which it does, if your boss is stupid enough)
I’m quite aware that it’s less likely to yessir technically hallucinate in these cases.
But that doesn’t address the core issue that the query was written by the LLM, without expert oversight, which still leads to situations that are effectively halucinations.
Technically, it is returning a “correct” direct answer to a question that no rational actor would ever have asked.
The meaningless, correct-looking and wrong result for the end user is still just going to be called a halucination, by common folks.
For common usage, it’s important not to promise end users that these scenarios are free of halucination.
You and I understand that technically, they’re not getting back a halucination, just an answer to a bad question.
But for the end user to understand how to use the tool safely, they still need to know that a meaningless correct looking and wrong answer is still possible (and today, still also likely).
LLMs often use bizarre “reasoning” to come up with their responses. And if asked to explain those responses, they then use equally bizarre “reasoning.” That’s because the explanation is just another post-hoc response.
Unless explainability is built in, it is impossible to validate an LLM.
This is because auto regressive LLMs work on high level “Tokens”. There are LLM experiments which can access byte information, to correctly answer such questions.
Also, they don’t want to support you omegalul do you really think call centers are hired to give a fuck about you? this is intentional
I don’t think that’s the full explanation though, because there are examples of models that will correctly spell out the word first (ie, it knows the component letter tokens) and still miscount the letters after doing so.
No, this literally is the explanation. The model understands the concept of “Strawberry”, It can output from the model (and that itself is very complicated) in English as Strawberry, jn Persian as توت فرنگی and so on.
But the model does not understand how many Rs exist in Strawberry or how many ت exist in توت فرنگی
I’m talking about models printing out the component letters first not just printing out the full word. As in “S - T - R - A - W - B - E - R - R - Y” then getting the answer wrong. You’re absolutely right that it reads in words at a time encoded to vectors, but if it’s holding a relationship from that coding to the component spelling, which it seems it must be given it is outputting the letters individually, then something else is wrong. I’m not saying all models fail this way, and I’m sure many fail in exactly the way you describe, but I have seen this failure mode and in that case an alternate explanation would be necessary.
The model ISN’T outputing the letters individually, binary models (as I mentioned) do not transformers.
The model output is more like Strawberry <S-T-R><A-W-B>
<S-T-R-A-W-B><E-R-R>
<S-T-R-A-W-B-E-R-R-Y>
Tokens can be a letter, part of a word, any single lexeme, any word, or even multiple words (“let be”)
Hey boss. Think they’re using chatgpt for that?