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by mlmonkey4 hours ago |
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upvoteby physicallyIllfr3 hours ago|
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Reminds me of how slot machine users swear the odds have changed on a machine.

also when someone says you just have to prompt it a certain way it reminds me of people who think they can get better results out of a slot machine by pressing buttons in a certain order

The providers of these models also design the UX similarly to slot machines (run it x amount of times for better results, multiplying your spend) this isnt a coincidence and they're playing into the gambler mentality, and probably hire UX designers that specialize in this.

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upvoteby treis2 hours ago|
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I am skeptical of this as well but slot machines are programmable and the house can change the odds.
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upvoteby cheevly3 hours ago|
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Wtf are you on my dude. Anthropic UI is designed like a slot machine? Hiring slot machine specialists? Sometimes I can’t believe im even on HN anymore with comments like this.
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upvoteby ddxv3 hours ago|
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I think some of it comes from that they do not publicly let you see the random seed. So each time you ask the answer is different (like a slot machine) and if they let users use the random seed it would let people much more accurately assess if an underlying model changed somehow (same seed and same input will always have the same output).

Of course the closed Anthropic would never share this, it would definitely take away the 'magic' feeling of the AI

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upvoteby pixl971 hours ago|
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With models there are a bunch of other dials that can be tuned even if the model itself remains exactly the same.

Are those dials set the same across all hardware configurations and clusters? Does model behavior average out the same across different hardware?

There are just too many different buttons that can be set to really trust a provider either not to directly commit fraud, or indirectly commit fraud with system complexity affecting the output.

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upvoteby mh-2 hours ago|
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To my understanding, with batched inference and other "optimizations" you wouldn't get the exact same token predictions even with temp=0.0.
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upvoteby porridgeraisin3 hours ago|
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Well, running an LLM X amount of times does give you better results provided you are willing to select the best one out of the X yourself.

But I agree with your general point. One of the reasons subscription plans are cheaper because they modulate usage in this way based on demand. They can also recover compute more coarsely via usage resets (which give positive PR).

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upvoteby zeroonetwothree3 hours ago|
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At that point I might as well do it myself
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upvoteby porridgeraisin1 hours ago|
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Well yeah. If for some task you find it easier to just do it yourself then you should. But you can improve the situation even if you can't entirely automate it by automating parts of the verification thereby making it easier to human-do larger verifications when X>1. But in many cases even that is not possible.

The progress however is such that the number of tasks that you can do with >p% automated and X=1 keeps increasing. So many times just waiting works. Of course, here also it changes from field to field. There are some tasks at which AI hasn't even gotten started, others where it has already peaked, others where it's increasing slowly, and others where it's increasing fast.

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upvoteby 4 hours ago|
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