You’re right, my original wording was too strong. I edited it to say that it agrees with so many diets instead of explains why they work.
Joel Burget
Karma: 483
One thing I like about the PUFA breakdown theory is that it agrees with aspects of so many different diets.
Keto avoids fried food because usually the food being fried is carbs
Carnivore avoids vegetable oils because they’re not meat
Paleo avoids vegetable oils because they weren’t available in the ancestral environment
Vegans tend to emphasize raw food and fried foods often have meat or cheese in them
Low-fat diets avoid fat of all kinds
Ray Peat was perhaps the closest to the mark in emphasizing that saturated fats are more stable (he probably talked about PUFA breakdown specifically, I’m not sure).
Edit: I originally wrote “neatly explains why so many different diets are reported to work”
If this was true, how could we tell? In other words, is this a testable hypothesis?
What reason do we have to believe this might be true? Because we’re in a world where it looks like we’re going to develop superintelligence, so it would be a useful world to simulate?
From the latest Conversations with Tyler interview of Peter Thiel
I feel like Thiel misrepresents Bostrom here. He doesn’t really want a centralized world government or think that’s “a set of things that make sense and that are good”. He’s forced into world surveillance not because it’s good but because it’s the only alternative he sees to dangerous ASI being deployed.
I wouldn’t say he’s optimistic about human nature. In fact it’s almost the very opposite. He thinks that we’re doomed by our nature to create that which will destroy us.
Three questions:
What format do you upload SAEs in?
What data do you run the SAEs over to generate the activations / samples?
How long of a delay is there between uploading an SAE and it being available to view?
This is fantastic. Thank you.
Thanks! I added a note about LeCun’s 100,000 claim and just dropped the Chollet reference since it was misleading.
Thanks for the correction! I’ve updated the post.
I assume the 44k PPM CO2 exhaled air is the product of respiration (I.e. the lungs have processed it), whereas the air used in mouth-to-mouth is quickly inhaled and exhaled.
What’s your best guess for what percentage of cells (in the brain) receive edits?
Are edits somehow targeted at brain cells in particular or do they run throughout the body?
I don’t have a well-reasoned opinion here but I’m interested in hearing from those who disagree.
How would you distinguish between weak and strong methods?
Re Na:K : Potassium Chloride is used as a salt substitute (which tastes surprisingly like regular salt). This makes it really easy to tweak the Na:K ratio (if it turns out to be important). OTOH, it’s some evidence that it’s not important, otherwise I’d expect someone to notice that people lose weight when they substitute it for table salt.
We don’t hear much about Apple in AI—curious why you rank them so important.
Though the statement doesn’t say much the list of signatories is impressively comprehensive. The only conspicuously missing names that immediately come to mind are Dean and LeCun (I don’t know if they were asked to sign).
I have a couple of basic questions:
Shouldn’t diagonal elements in the perplexity table all be equal to the baseline (since the addition should be 0)?
I’m a bit confused about the use of perplexity here. The added vector introduces bias (away from one digit and towards another). It shouldn’t be surprising that perplexity increases? Eyeballing the visualizations they do all seem to shift mass away from
band towardsa.
Link to Rob Bensinger’s comments on this market:
I worry that this is conflating two possible meanings of FLOPS:
Floating Point Operations (FLOPs)
Floating Point Operations per Second (Maybe FLOPs/s is clear?)
The AI and Memory Wall data is using (1) while the Sandberg / Bostrom paper is using (2) (see the definition in Appendix F).
(I noticed a type error when thinking about comparing real-time brain emulation vs training).
One more, related to your first point: I wouldn’t expect all mesaoptimizers to have the same signature, since they could take very different forms. What does the distribution of mesaoptimizer signatures look like? How likely is it that a novel (undetectable) mesaoptimizer arises in training?
Measuring the composition of fryer oil at different times certainly seems like a good way to test both the original hypothesis and the effect of altitude.