Greenhouse gas emissions shouldn't be the only factor people consider for sustainability of their food. In the case of fish, this very article talks about the issues with farmed fish. Even a plant-based diet can be filled with unsustainable sources, such as plantations that destroy endangered habitats for palm oil, or industrial farming operations that spray lots of pesticides to harm the insect population and allow lots of fertilizer runoff into natural waterways. We're still polluting and depleting resources for many many vegetarian foods in the world.
I'd argue that if we're looking for a full top-to-bottom sustainable food system, animals will play a role. But we need to be cognizant of the whole system, not playing whack-a-mole with issues.
According to your source, there are 15 sources of protein that emit less greenhouse gases (GHGs) per 100g of protein than farmed fish, including poultry and eggs, and 16 sources that emit more (including items that are not known for their protein content like coffee, apples, and dark chocolate). Being highly charitable, farmed fish is squarely in the middle.
Additionally, farmed fish emits twice the GHGs of tofu, and almost 22 times that of nuts. So just comparing placements on the list paints a misleading picture.
As for "not willing to go full vegetarian": you may as well say "not willing to stop eating fish", because they are equally unserious limitations when discussing these topics. "Not being willing" is only a slightly more mature version of a child saying "I don't want to".
I don't think it's "unserious" to recognize that >85% of the world's population eats meat.
If you're quibbling about wording, all I meant was: farmed fish and chicken are among the most sustainable meat sources.
I'm not making a statement that people should eat meat, but many people do eat meat, so it's worth comparing which meat sources are better than others. I think it would be great if more people knew that beef produces 10x the greenhouse gases than chicken/fish do.
It's not "quibbling" to correct your mischaracterization of the truth.
If you'll forgive me borrowing your logic: "I'm not saying that people should eat beef, but many people do eat beef, so it's worth comparing which beef sources are better than others."
Plant-based diets are a very good answer to the problems caused by animal agriculture. If someone takes issue with that answer, I'd need a better reason than their personal pleasure to take them seriously in the conversation.
I agree it’s worth comparing beef sources! That was my point about within-category differences and harm reduction. Saying "tofu is cleaner" doesn’t make beef comparisons pointless - just like the existence of bicycles doesn’t make car fuel economy comparisons pointless. We should compare across categories and within them, so people who aren’t switching today still choose the lower-impact option.
I hesitate to use the word "quibbling" now, but it seems like a poor use of time to compare beef when even the most environmentally-friendly beef is multiple times worse than alternatives.
I think this harm-reduction approach might make more sense from a governmental policy perspective, but is otherwise silly for us to take as individuals because we have such comparatively little influence over each other's choices. I wouldn't waste that small influence encouraging someone to make a slightly less bad choice.
The comparison of food to transportation is a bad one. Nutrients are nutrients, and everything else is personal pleasure. In other words, you can easily hit your same macros by replacing animal products with plant products without even having to change grocery stores. You cannot easily transport a mattress on a bicycle instead of a car.
You started this by objecting to my wording ("among the most") when I said fish/chicken are the most sustainable meat options. They are, by a wide margin. Beef’s footprint is roughly 10× higher, so swapping a beef meal for chicken or fish cuts ~90% of those emissions. That’s not a "slightly less bad choice".
Calling harm reduction "silly" because tofu exists just shifts the target. We can hold two thoughts at once: (1) plant-heavy diets are best, and (2) for the vast majority who aren’t going vegan tomorrow, steering from beef to chicken/fish dramatically reduces damage right now. Dismissing that because it’s not maximal purity guarantees we leave real cuts on the table.
Farmed seafood is among the worst garbage you can eat. Tons of antibiotics, growth hormones, fish are fed utter cheap junk so ie salmon meat has more like pork composition than a wild salmon, shrimp are even worse. If you ever saw a shrimp 'factory' and grow pond/cage and its surroundings in a typical 3rd world country where most come from, you wouldn't eat it for a long time if ever again. Literally nothing lives around those places.
That take’s outdated. In the US/EU, routine antibiotics in fish farming are banned [1]. Growth hormones aren’t used in edible fish. Farmed salmon’s feed changed (more plant oils), but it still delivers high omega-3s and usually less mercury than wild [2].
OK thats a good development. But overall difference in quality of meat is even visible - farmed salmon looks like a completely different fish than wild one (if you even can get one) - akin to difference between say boar and domesticated pig (lean muscle vs tons of fatty wobbly tissue). It doesn't scream 'healthy' but that may be just emotions playing old tune.
Also what I wrote about shrimp from any 3rd world country is valid - I've seen such place this summer in Indonesia, and from what I've heard whole south east Asia is exactly like that, or worse. Getting shrimp from some western democracy with strong consumer protection rights ain't possible in many parts of Europe, not sure about other places
There is not strong consensus on the meaning of the term. Some may say “human level performance” but that’s meaningless both in the sense that it’s basically impossible to define and not a useful benchmark for anything in particular.
The path to whatever goalpost you want to set is not going to be more and more intelligence. It’s going to be system frameworks for stateful agents to freely operate in environments in continuous time rather than discrete invocations of a matrix with a big ass context window.
I agree with @ActivePattern and thank you for your help in answering.
Supplement for @f_devd:
During training, the K outputs share the stem feature from the NN blocks, so generating the K outputs costs only a small amount of extra computation. After L2-distance sampling, discarding the other K-1 outputs therefore incurs a negligible cost and is not comparable to discarding K-1 MoE experts (which would be very expensive).
You are probably right, although it's not similar to a GAN at all, it is significantly more like diffusion (although maybe not latent, the main reason I assumed so is because the "features" are passed-through but these can just be the image).
The ahead-of-time sampling doesn't make much sense to me mechanically, and isn't really mentioned much. But I will hold my judgement for future versions since the FID performance of this first iteration is still not that great.
It doesn't play nice with a lot of popular Python libraries. In particular, many popular Python libraries (NumPy, Pandas, TensorFlow, etc.) rely on CPython’s C API which can cause issues.
A “sufficiently smart compiler” can’t legally skip Python’s semantics.
In Python, p.x * 2 means dynamic lookup, possible descriptors, big-int overflow checks, etc. A compiler can drop that only if it proves they don’t matter or speculates and adds guards—which is still overhead. That’s why Python is slower on scalar hot loops: not because it’s interpreted, but because its dynamic contract must be honored.
In Smalltalk, p x * 2 has that flow that as well, and even worse, lets assume the value returned by p x message selector, does not understand the * message, thus it will break into the debugger, then the developer will add the * message to the object via the code browser, hit save, and exit the debugger with redo, thus ending the execution with success.
Somehow Smalltalk JIT compilers handle it without major issues.
Smalltalk JITs make p x * 2 fast by speculating on types and inserting guards, not by skipping semantics. Python JITs do the same (e.g. PyPy), but Python’s dynamic features (like __getattribute__, unbounded ints, C-API hooks) make that harder and costlier to optimize away.
You get real speed in Python by narrowing the semantics (e.g. via NumPy, Numba, or Cython) not by hoping the compiler outsmarts the language.
Python'a JIT could do the same, it could check if __getattribute__() is the default implementation and replace its call with p x directly. This would work only for classes that have not been modified at runtime and that do not implement a custom __getattribute__
First, we need to add the word 'only': "not ONLY because it’s interpreted, but because its dynamic contract must be honored." Interpreted languages are slow by design. This isn't bad, it just is a fact.
Second, at most this describes WHY it is slow, not that it isn't, which is my point. Python is slow. Very slow (esp. for computation heavy workloads). And that is okay, because it does what it needs to do.
It's a OpenAI researcher that's worked on some of their most successful projects, and I think the criticism in his X thread is very clear.
Systems that can learn to play Atari efficiently are exploiting the fact that the solutions to each game are simple to encode (compared to real world problems). Furthermore, you can nudge them towards those solutions using tricks that don't generalize to the real world.
Right, and the current state of tech - from accounts I’ve read, though not first hand experienced - is the “black box” methods of AI are absolutely questionable when delivering citations and factual basis for their conclusions. As in, the most real world challenge, in the basic sense, of getting facts right is still a bridge too far for OpenAI, ChatGPT, Grok, et al.
See also: specious ethics regarding the training of LLMs on copyright protected artistic works, not paying anything to the creators, and pocketing investor money while trying to legislate their way around decency in engineering as a science.
Carmack has a solid track record as an engineer, innovator, and above the board actor in the tech community. I cannot say the same for the AI cohort and I believe such a distinction is important when gauging the validity of critique or self-aggrandizement by the latter, especially at the expense of the former. I am an outlier in this community because of this perspective, but as a creator and knowledgeable enough about tech to see things through this lens, I am fine being in this position. 10 years from now will be a great time to look back on AI the way we’re looking back at Carmack’s game changing contributions 30 years ago.
I am quite confident that an LLM will never beat a top chess engine like Stockfish. An LLM is a generalist -- it contains a lot of world knowledge, and nearly all of it is completely irrelevant to chess. Stockfish is a specialist tuned specifically to chess, and hence able to spend its FLOPs much more efficiently towards finding the best move.
The most promising approach would be tune a reasoning LLM on chess via reinforcement learning, but fundamentally, the way an LLM reasons (i.e. outputting a stream of language tokens) is so much more inefficient than the way a chess engine reasons (direct search of the game tree).
Wouldn't the extra stamina have been rewarded, assuming creatine allowed you to perform extra repetitions? All exercises were done to repetition maximum.
