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I don't think it's fair to include the billion years of human evolution as a cost on the human side of the chart but not include it in the AI side. AIs didn't e

I don't think it's fair to include the billion years of human evolution as a cost on the human side of the chart but not include it in the AI side. AIs didn't evolve themselves out of the primordial ooze, they were built by humans and required herculean human effort to develop and improve. They stand on our shoulders, yet they're still in infancy when it comes to capability. I have yet to see any evidence of an LLM being able to replace an accountant, or an HR person, never mind a trash collector. The best use of LLMs that I've seen so far is as a boilerplate-producing autocomplete system. Considering that we have better ways to automate this (better programming languages that can abstract away the boilerplate), this is not very high praise. Edit: If you look at the total footprint (considering not just the efficiency of the neurons, but the whole animal) the figures are very close - the llama model card indicates that v2 70B caused the emission of ~300T of CO2, and an average human in the US emits 16T of CO2 a year, so a human would emit ~320T of CO2 in 20 years. I assume children don't have as high a CO2 output, but even so it seems like it's the same order of magnitude. If you're going to include the whole animal on the human side, you need to include the whole supply chain on the LLM side. The cost of building all the fabs and doing all the R&D to develop and manufacture model training-specific computers (matrix multiplier hardware). Just like with crypto, these resources had to be diverted away from other things (e.g. causing the price of gamers' graphics cards to skyrocket). It's only fair to interrogate the ROI.

garblegarble 3y ago
>The best use of LLMs that I've seen so far is as a boilerplate-producing autocomplete system. Considering that we have better ways to automate this (better programming languages that can abstract away the boilerplate), this is not very high praise. I think it's in our nature as software people to look at their ability to work with code, but they're quite good when applied to general language tasks. I've been using them for summarisation and reading comprehension and they're quite effective. I've also been working with a teacher friend on seeing if they can generate well-scoring essays on highschool English essays (as always, the problem is prompting and context). On code, GPT-3.5 (moreso GPT-4) seems to have a good ability to generate and translate smaller scale code problems (hundreds of lines in low-boilerplate languages) but yes, they're like an eternally junior engineer whose work you're constantly having to oversee for subtle bugs, and I don't know that it actually saves time. I'm pretty sure people are working on different approaches to applying them to code, with better prompting+context from larger codebases, and multi-step processing (i.e. rather than just a single prompt->response, letting the model iterate through a few steps independently, possibly guided by other adversarial/supervisor agent instances, testcase generators, etc.) >If you're going to include the whole animal on the human side, you need to include the whole supply chain on the LLM side. The cost of building all the fabs and doing all the R&D to develop and manufacture model training-specific computers (matrix multiplier hardware). Just like with crypto, these resources had to be diverted away from other things (e.g. causing the price of gamers' graphics cards to skyrocket). It's only fair to interrogate the ROI. That's fair, although it gets complicated to work out numbers because we don't train many LLMs, whereas we're constantly training humans, each of whom cost the planet tons of CO2 emissions every year... and, of course, your point that LLMs just aren't very good yet. I fear that they're good enough (or appear to be to the layperson) that execs will replace customer support staff with them, even if the outcomes overall aren't as good.
- chongli 3y ago
  That's fair, although it gets complicated to work out numbers because we don't train many LLMs, whereas we're constantly training humans, each of whom cost the planet tons of CO2 emissions every year... and, of course, your point that LLMs just aren't very good yet. I fear that they're good enough (or appear to be to the layperson) that execs will replace customer support staff with them, even if the outcomes overall aren't as good. I'm not as fearful. When customer support gets too expensive companies already outsource it to India. Indian customer support workers cost far less than Westerners in terms of energy and CO2 emissions, both in terms of training and ongoing costs. India is about 2T/person in CO2 emissions, compared to 15T for North Americans. And that number is averaged over the whole country. I would imagine the poorer areas of the country have much lower emissions and the bulk of CO2 comes from the wealthier big cities.

garblegarble 3y ago

>The best use of LLMs that I've seen so far is as a boilerplate-producing autocomplete system. Considering that we have better ways to automate this (better programming languages that can abstract away the boilerplate), this is not very high praise. I think it's in our nature as software people to look at their ability to work with code, but they're quite good when applied to general language tasks. I've been using them for summarisation and reading comprehension and they're quite effective. I've also been working with a teacher friend on seeing if they can generate well-scoring essays on highschool English essays (as always, the problem is prompting and context). On code, GPT-3.5 (moreso GPT-4) seems to have a good ability to generate and translate smaller scale code problems (hundreds of lines in low-boilerplate languages) but yes, they're like an eternally junior engineer whose work you're constantly having to oversee for subtle bugs, and I don't know that it actually saves time. I'm pretty sure people are working on different approaches to applying them to code, with better prompting+context from larger codebases, and multi-step processing (i.e. rather than just a single prompt->response, letting the model iterate through a few steps independently, possibly guided by other adversarial/supervisor agent instances, testcase generators, etc.) >If you're going to include the whole animal on the human side, you need to include the whole supply chain on the LLM side. The cost of building all the fabs and doing all the R&D to develop and manufacture model training-specific computers (matrix multiplier hardware). Just like with crypto, these resources had to be diverted away from other things (e.g. causing the price of gamers' graphics cards to skyrocket). It's only fair to interrogate the ROI. That's fair, although it gets complicated to work out numbers because we don't train many LLMs, whereas we're constantly training humans, each of whom cost the planet tons of CO2 emissions every year... and, of course, your point that LLMs just aren't very good yet. I fear that they're good enough (or appear to be to the layperson) that execs will replace customer support staff with them, even if the outcomes overall aren't as good.

chongli 3y ago
That's fair, although it gets complicated to work out numbers because we don't train many LLMs, whereas we're constantly training humans, each of whom cost the planet tons of CO2 emissions every year... and, of course, your point that LLMs just aren't very good yet. I fear that they're good enough (or appear to be to the layperson) that execs will replace customer support staff with them, even if the outcomes overall aren't as good. I'm not as fearful. When customer support gets too expensive companies already outsource it to India. Indian customer support workers cost far less than Westerners in terms of energy and CO2 emissions, both in terms of training and ongoing costs. India is about 2T/person in CO2 emissions, compared to 15T for North Americans. And that number is averaged over the whole country. I would imagine the poorer areas of the country have much lower emissions and the bulk of CO2 comes from the wealthier big cities.