251–260 of 445 posts
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you want to be able to search over summaries so you need to generate them right away
Do you want to search summaries, or do you want to save millions of dollars per year?
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yes, the entire design relies on a human to check everything. basically it presents what it thinks should be done, and why. the human then agrees or does not. much work is put into streamlining this but ultimately its still human controlled
At the risk of being obvious, this seems set up for failure in the same way expecting a human to catch an automated car's mistakes is. Although I assume mistakes here probably don't matter very much.
If those prompts pop up constantly asking for elevated privileges, this is actually worse because it trains people to just reflexively allow elevation.
Why do people so desperately want to see AI fail?
We dont. People just tired of listening to the pipe dreams of these weirdos CEOs who sell AI.
Here's the truth: NO ONE KNOWS.
What part of No One Actually Knows do people not understand? This applies to both the "AI WILL RULE THE WORLD MUAHAHA" and "AI is BIG BIG HOAX" crowd.
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Pro-tip: You won't ever do that.
Advanced organizations (think not startups, but companies that have had years of decades of profit in the public market) might have solved all the low-hanging fruit problems and have staff doing things like automated quality audits (search summaries for swearing, abusive language, etc).
We are entering the “Trough of disillusionment.” These hype cycles are very predictable. GPT-5 being panned as a disappointment after endless hype may go down as GenAI’s “jump the shark” moment. It’s all fun and games until the bean counters start asking for evidence of return on investment. GenAI folks better buckle up. Bumps ahead. The smart folks are already quietly preparing for a shift to ride the next hype wave…
I wouldn’t be surprised if 95% of companies knew this was a money pit but felt obligated to burn a pile of money on it so as not to hurt the stock price.
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[dead]
It doesn't because we already know it doesn't because just like think about it and it clearly doesn't. So everyone trying to make the technology is a big dumb dumb for even trying. Like is the conclusion we shouldn't even try? This kind of thinking ridiculous.
Full disclosure: I'm currently in a leadership role on an AI engineering team, so it's in my best interest for AI to be perceived as driving value. Here's a relatively straightforward application of AI that is set to save my company millions of dollars annually. We operate large call centers, and agents were previously spending 3-5 minutes after each call writing manual summaries of the calls. We recently switched to…
Pro-tip: don't write the summary at all until you need it for evidence. Store the call audio at 24Kb/s Opus - that's 180KB per minute. After a year or whatever, delete the oldest audio. There, I've saved you more millions.
The parent states:
>Not only are the summaries better than those produced by our human agents...
Now, since they have not mentioned what it took to actually verify that the AI summaries were in fact better than the human agents, I'm sceptical they did the necessary due dillengence.
Why do I think this? Because I have actually tried to do such a verification. In order to verify that the AI summary is actually correct you have to engage in the incredibly tedious task of listening to original recording literally second by second and make sure that what is said does not conflict with the AI summary in question. Not only did the AI summary fail at this test, it failed in the first recording I tested.
The AI summary stated that "Feature x was going to be in Release 3, not 4" whereas the in the recording it is stated that the feature will be in Release 4 not 3, literally the opposite of what the AI said.
I'm sorry but the fact that the AI summary is nicely formatted and has not missed a major topic of conversation means fuck all if the details that are are discussed are spectacularly wrong from a decision tracking perspective, as in literally the opposite of what is stated.
And I know "why" the Ai summary fucked up, because in that instance the topic of conversation was about how there was some confusion about which release that feature was going to be in, that's why the issue was a major item of the meeting agenda in the first place. Predicably, the AI failed to follow the convoluted discussion and "came to" the opposite conclusion.
In short, no fucking thanks.
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This works unless you want to automate something with the transcripts, stats, feedback.
Why wouldn't it, once you actually have that project you have the raw audio to generate the transcripts. Only spend the money at the last second when you know you need it. Edit: Tell me more how preemptively spending five figures to transcribe and summarize calls in case you might want to do some "data engineering" on it later is a sound business decision. What if the model is cheaper down the road? YAGNI.
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Pro-tip: don't write the summary at all until you need it for evidence. Store the call audio at 24Kb/s Opus - that's 180KB per minute. After a year or whatever, delete the oldest audio. There, I've saved you more millions.
>Store the call audio at 24Kb/s Opus - that's 180KB per minute Why OPUS though? There's dedicated audio codecs in the VoiP/telecom industry that are specifically designed for the best size/quality for voice call encoding.
Opus is great for a lot of things and realtime speech over sip or webrtc is just one.
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Imagine a follow-up call of a customer. They are referring to earlier calls and the call center agents needs to check what it was about. So they can skim/read the transcripts while talking to the customer. I guess it's really hard to listen to transcripts while you're on the phone.
That would be awesome! But in fact, customer call centers tend not to be able to even know that you called in yesterday, three days ago and last week. This is why email-ticketing call centers are vastly superior.