31–40 of 186 posts
Off topic > 3 months later, reading a paper while on board a boring flight home, I have my answer. I noticed people from hacker news routinely read scientific papers. This is a habit I envy but don't share. Any tips or sites for someone interested in picking up more science papers to read.
Step 1. Find papers you're interested in Step 2. Open them Step 3. read them
Off topic > 3 months later, reading a paper while on board a boring flight home, I have my answer. I noticed people from hacker news routinely read scientific papers. This is a habit I envy but don't share. Any tips or sites for someone interested in picking up more science papers to read.
When google doesn't return a good result to a specific question, switch to scholar.google.com and start reading abstracts. It'll seem like an opaque maze at first, but just keep reading and it'll start clearing up pretty quickly and become useful.
Off topic > 3 months later, reading a paper while on board a boring flight home, I have my answer. I noticed people from hacker news routinely read scientific papers. This is a habit I envy but don't share. Any tips or sites for someone interested in picking up more science papers to read.
When google doesn't return a good result to a specific question, switch to scholar.google.com and start reading abstracts. Everything may seem like an opaque maze at first, but just keep reading and patterns start emerging quickly and become useful.
Is it saying that part of its more-efficient inferencing relies on mixing tokens from completely-separate inputs – eg, from other users? And then, depending on what other inputs chance into the same grouping, the relative assignment-to-'experts' varies, and thus the eventual completions?
If so, I'd see that as not just introducing non-determinism, but also potentially making the quality of your responses dependent on how-many-concurrent-requests are fighting for the same expert-allocations.
(For example, maybe the parts of the system best at translating/interpreting Hindi give worse results during peak usage hours-of-the-day in India, when the most concurrent inputs are competing for that same competence.)
Perhaps also, this is another possible explanation for perceived quality-degradation over time. When certain tests were reliably succeeding earlier, there was less congestion for the relevant 'experts'. Now, with more concurrent use, those same tests aren't as reliably winning as much of relevant 'experts' effort.
This may also suggest a bit of a quagmire: on whatever domains some sub-experts seem impressively good, initially, even more proportionate use will be attracted. But such new congestion means all the copycat use no longer gets the same expert allocations – and thus the initially-impressive performance degrades.
(And if the effect is strong, & known-but-undisclosed-by-OpenAI, does it amount to a bait-and-switch? Attract users with unrepresentative excellence on an initially-uncongested Mixture-of-Experts system, but then offer them the lower-quality results from a more-congested system.)
Off topic > 3 months later, reading a paper while on board a boring flight home, I have my answer. I noticed people from hacker news routinely read scientific papers. This is a habit I envy but don't share. Any tips or sites for someone interested in picking up more science papers to read.
Build the habit. When google doesn't return a good result to a specific question, switch to scholar.google.com and start reading abstracts. Everything may seem like an opaque maze at first, but just keep reading and patterns start emerging quickly and become useful.
I feel like this introduces the potential for weird and hard-to-implement side channel attacks, if the sequences in a batch can affect the routing of others.
I think you’re right. Would be very hard to exploit I imagine though.
the language models in our heads have not caught up to the ones in our browsers.
as the similarities and associations crystallize a bit better, it won’t look so hard.
bookmark this if you think it bullshit. eight months.
Off topic > 3 months later, reading a paper while on board a boring flight home, I have my answer. I noticed people from hacker news routinely read scientific papers. This is a habit I envy but don't share. Any tips or sites for someone interested in picking up more science papers to read.
Step 1. Find papers you're interested in Step 2. Open them Step 3. read them
Earlier quoted context omitted.
I don't think this commitment had any plausibility. Token "probabilities" only have a straightforward probabilistic interpretation for base models. In fine-tuned models, they do no longer represent the probability of the next token given the prompt, but rather how well the next token fulfills the ... tendencies induced by SL and RL tuning. Which is presumably pretty useless information. OpenAI has no intention to pro…
Topic laundering, the probabilities are the probabilities, you don't suddenly get wrong probabilities with more training on more data
Off topic > 3 months later, reading a paper while on board a boring flight home, I have my answer. I noticed people from hacker news routinely read scientific papers. This is a habit I envy but don't share. Any tips or sites for someone interested in picking up more science papers to read.