4 ms·

I think we are, collectively, greatly underestimating the value of determinism and, conversely, the cost of nondeterminism. I've been trialing a different prod

I think we are, collectively, greatly underestimating the value of determinism and, conversely, the cost of nondeterminism. I've been trialing a different product with the same sales pitch. It tries to RCE my incidents by correlating graphs. It ends up looking like this page[1], which is a bit hard to explain in words, but both obvious and hilarious when you see it for yourself. [1]: https://tylervigen.com/spurious-correlations https://tylervigen.com/spurious-correlations

graemep 1y ago
Its fun, but the point should be well known (i know its not). Time series are very prone to spurious correlations - r² is not useful. Its even worse if you just eyeball a graph. If something changes over time, you need to use appropriate measures.
- feoren 1y ago
  > r² is not useful People want so badly to have an "objective" measure of truth that they can just pump data into and get a numeric result that "doesn't lie". r², p < 0.05, χ2, etc. It's too far to say these numbers aren't useful at all -- they are -- but we're just never going to be able to avoid the difficult and subjective task of interpreting experimental results in their broader context and reconciling them with our pre-existing knowledge. I think this is why people are so opposed to anything Bayesian: we don't want to have to do that work, to have those arguments with each other about what we believed before the experiment and how strongly. But the more we try to be objective, the more vulnerable we are to false positives and spurious correlations.
- SOLAR_FIELDS 1y ago
  I agree - the problem here is probably less that the AI makes the mistake and more that it’s just really easy to make this mistake whether you’re AI or human. It’s probably true that the AI selects more correlations that would be obviously irrelevant to the end user though. I, like others doing SRE work, spend a fair amount of time trawling through these types of graphs and it’s very common to see something that looks like a correlation, look closer, then dismiss it due to noise. Time series are also very much subject to MTUP[1]. Something that looks like a correlation or problem at a certain zoom level becomes totally normal behavior when you zoom out, for instance 1: https://en.m.wikipedia.org/wiki/Modifiable_temporal_unit_problem https://en.m.wikipedia.org/wiki/Modifiable_temporal_unit_pro...
worldsayshi 1y ago
Perhaps I'm missing you point a bit but you can absolutely have deterministic UX when it matters with LLM based applications if you design it right. Whenever you need determinism, make the LLM generate a deterministic specification for how to do something and/or record it's actions. And let the user save away re-playable specifications along with the dialogue. Then build ways for the AI to suggest fixes for failing specs when needed. It's basically the same flow as when you use AI for programming. Except you need to constrain the domain of the specifications more and reason more about how to allow the AI to recover from failing specifications if you don't want to force the user to learn your specification language.

graemep 1y ago

Its fun, but the point should be well known (i know its not). Time series are very prone to spurious correlations - r² is not useful. Its even worse if you just eyeball a graph. If something changes over time, you need to use appropriate measures.

feoren 1y ago
> r² is not useful People want so badly to have an "objective" measure of truth that they can just pump data into and get a numeric result that "doesn't lie". r², p < 0.05, χ2, etc. It's too far to say these numbers aren't useful at all -- they are -- but we're just never going to be able to avoid the difficult and subjective task of interpreting experimental results in their broader context and reconciling them with our pre-existing knowledge. I think this is why people are so opposed to anything Bayesian: we don't want to have to do that work, to have those arguments with each other about what we believed before the experiment and how strongly. But the more we try to be objective, the more vulnerable we are to false positives and spurious correlations.
SOLAR_FIELDS 1y ago
I agree - the problem here is probably less that the AI makes the mistake and more that it’s just really easy to make this mistake whether you’re AI or human. It’s probably true that the AI selects more correlations that would be obviously irrelevant to the end user though. I, like others doing SRE work, spend a fair amount of time trawling through these types of graphs and it’s very common to see something that looks like a correlation, look closer, then dismiss it due to noise. Time series are also very much subject to MTUP[1]. Something that looks like a correlation or problem at a certain zoom level becomes totally normal behavior when you zoom out, for instance 1: https://en.m.wikipedia.org/wiki/Modifiable_temporal_unit_problem https://en.m.wikipedia.org/wiki/Modifiable_temporal_unit_pro...

worldsayshi 1y ago

Perhaps I'm missing you point a bit but you can absolutely have deterministic UX when it matters with LLM based applications if you design it right. Whenever you need determinism, make the LLM generate a deterministic specification for how to do something and/or record it's actions. And let the user save away re-playable specifications along with the dialogue. Then build ways for the AI to suggest fixes for failing specs when needed. It's basically the same flow as when you use AI for programming. Except you need to constrain the domain of the specifications more and reason more about how to allow the AI to recover from failing specifications if you don't want to force the user to learn your specification language.