I can confirm - I woke up to the resolution to my two hardest problems during PhD. Three, if you count "I should look for this kind of inequality" (which did turn out to exist), but I think that's more of an _idea_ than a solution.
The hard part is paying attention to it. With enough attention your mind will fix it.
Thanks for sharing your opinion. The first difficulty in real world (even idealized) politics is voicing a consistent independent stance and thinking through the implications. Many cannot even make it that far.
The second and greater difficulty is that realizing that a solution that is politically untenable is not a solution, it's a campaign slogan. I don't know how we get people to move past this difficulty.
I assume you are suggesting that a tax on the rich is not politically viable.
When the structural violence that permeates our society finally manifests in the only violence the lower class can execute - this non-viability might change.
It will be a harrowing time- and I hope we avoid this.
The billionaires will cease to exist one way or another.
I did this too! For months (almost a year) I used descriptions, pictures, and measurements of food to get rough calorie counts. My diet is pretty simple and repetitive.
I would occasionally check the estimates, maybe once every few days for meals I wasn't already pretty sure of, and it was generally accurate. Where it was extremely inaccurate was on portions, and anyone who has dealt with computer vision could tell you, you can't get scale from a picture. So I'd have to weigh some meals or ingredients, which would generally make things more accurate again.
So, I think it's possible, but you need multimodal data and grounded with regular checks.
About once a week I ask ChatGPT to give me a reasonable diet for recomp with weight loss. It consistently insisted I have at least 7 meals consisting of at least 30g of protein per meal, but the protein source can't be whey or casein. When I ask "why" it cites a bunch of studies ... but most of those "studies" are N=1 of a college or Olympic level athlete. If, instead, I grab a large scale lateral analysis, it says "3 meals" with about 1/2 of the protein.
It'll defend both sides (mutually contradictory) to the death. NOTHING will budge it from its initial stance.
To be fair this is a reflection of the general state of nutritional science and the actual answer seems to be "it depends on your genes".
Some people do well on 6 small meals, others do well on no breakfast and two large ones. Studies can't tell you anything useful about that, you have to experiment and find out what works best for you
The answer does not really depend on genes. There are personal preferences, there are sex differences (women prefer more carbs), and the biggest component is where you are and in which direction do you want to go to.
But in terms of physiology the answer is quite clear:
1. The protein is the most important macro to get, no matter if bulking or cutting. It is the building block.
2. Whatever the amounts (0.8g-1.8g/kg of bodyweight, depends a bit on a situation and the willingness to lose some potential marginal gains), try to divide your daily protein somewhat evenly between meals.
3. Pareto principle, you get the most benefits by having 3 meals. 4 if you really care about small differences and want to optimize. 5 meals give negligible additional benefits, for professional athletes who want to be anal.
4. So basically eat at least 3 meals and up to whatever works for you practically speaking.
Optimum meal timing in particular I believe is heavily influenced by genes - I have friends who never eat breakfast, survive on black coffee until 1PM, then eat a lot in the evening and feel good doing it. If I do that I feel terrible.
So yes eat 2g/kg protein but the best way to time that in terms of meals, best specific foods to eat etc is definitely influenced by your genes
But physiologically speaking there is no difference in the optimal recommendation in terms of protein synthesis: protein should be evenly divided between evenly spaced meals.
If people decide that this non-optimal way of life fits them better, then go for it. But let's stop with the "I feel better this way, therefore biologically my body works differently than any other human body." BS.
I think the analogy (and it is not to be taken literally) is that of "commoditized processes".
Nowadays we don't build bridges to suit the site, we choose sites to accommodate bridges that we basically build identically via a few designs.
Connecting back to s/w AI can do the standard stuff ok as long as you test around the outside of it, so you might want to hone your judgment about how you build systems so it uses the stuff AI can do well, vs "building for the site". The gains are productivity. The losses are efficiency (the problem must go through some extra steps to meet the process where it works). Same as any engineering problem at scale.
Yes I believe GP was focused on the catastrophe part. It's very likely correct that our CO2 emissions are warming the atmosphere ocean etc, but it's not clear that runaway warming is inevitable or that life or geology have feedback mechanisms that turn an exponential into an S curve. That is, after all, basically what natural selection tends to do. Turning the table again, even if there are corrective factors humans might have immense suffering before it stabilizes. So we don't know.
You didn't ask, but my opinion on it is that we'll probably stabilize on a cleaner energy source and find natural countermeasures when suffering ticks up. Any top down pressure to change things whole cloth seems doomed, no matter how benevolent. We're closed loop creatures.
If you think a 2 year old is doing deep learning, you're probably wrong.
But if you think natural selection was providing end to end loss optimization, you might be closer to right. An _awful lot_ of our brain structure and connectivity is born, vs learned, and that goes for Mice and Men.
Why not both? A pre-trained LLM has an awful lot of structure, and during SFT, we're still doing deep learning to teach it further. Innate structure doesn't preclude deep learning at all.
There's an entire line of work that goes "brain is trying to approximate backprop with local rules, poorly", with some interesting findings to back it.
Now, it seems unlikely that the brain has a single neat "loss function" that could account for all of learning behaviors across it. But that doesn't preclude deep learning either. If the brain's "loss" is an interplay of many local and global objectives of varying complexity, it can be still a deep learning system at its core. Still doing a form of gradient descent, with non-backpropagation credit assignment and all. Just not the kind of deep learning system any sane engineer would design.
I don't know what you mean by end to end loss optimization in particular, but if you mean something that involves global propagation of errors e.g. backpropagation you are dead wrong.
Predictive coding is more biologically plausible because it uses local information from neighbouring neurons only.
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