Carl Shulman: evaluation

Transcript context

…Okay so there's the investment in hardware, there's the hardware technology itself, and there's the software progress itself. The AI is getting better because we're spending more money on it because our hardware itself is getting better over time and because we're developing better models or better adjustments to those models. Where is the loop here? The work involved in designing new hardware and software is being done by people now. They use computer tools to assist them, but computer time is not the primary cost for NVIDIA designing chips, for TSMC producing them, or for ASML making lithography equipment to serve the TSMC fabs. And even in AI software research that has become quite compute intensive we're still in the range where at a place like DeepMind salaries were still larger than compute for the experiments. Although more recently tremendously more of the expenditures were on compute relative to salaries. If you take all the work that's being done by those humans, there's like low tens of thousands of people working at Nvidia designing GPUs specialized for AI. There's more than 70,000 people at TSMC which is the leading producer of cutting-edge chips. There's a lot of additional people at companies like ASML that supply them with the tools they need and then a company like DeepMind, I think from their public filings, they recently had a thousand people. OpenAI is a few hundred people. Anthropic is less. If you add up things like Facebook AI research, Google Brain, other R&D, you get thousands or tens of thousands of people who are working on AI research. We would want to zoom in on those who are developing new methods rather than narrow applications. So inventing the transformer definitely counts but optimizing for some particular businesses data set cleaning probably not. So those people are doing this work, they're driving quite a lot of progress. What we observe in the growth of people relative to the growth of those capabilities is that pretty consistently the capabilities are doubling on a shorter time scale than the people required to do them are doubling. We talked about hardware and how it was pretty dramatic historically. Like four or five doublings of compute efficiency per doubling of human inputs. I think that's a bit lower now as we get towards the end of Moore's law although interestingly not as much lower as you might think because the growth of inputs has also slowed recently. On the software side there's some work by Tamay Besiroglu and collaborators; it may have been his thesis. It's called Are models getting harder to find? and it's applying the same analysis as the “Are ideas getting harder to find?” and you can look at growth rates of papers, from citations, employment at these companies, and it seems like the doubling time of these like workers driving the software advances is like several years whereas the doubling of effective compute from algorithmic progress is faster. There's a group called Epoch, they've received grants from open philanthropy, and they do work collecting datasets that are relevant to forecasting AI progress. Their headline results for what's the rate of progress in hardware and software, and growth in budgets are as follows — For hardware, they're looking at a doubling of hardware efficiency in like two years. headline results for what's the rate of progress in hardware and software, and growth in budgets are as follows — For hardware, they're looking at a doubling of hardware efficiency in like two years. It's possible it’s a bit better than that when you take into account certain specializations for AI workloads. For the growth of budgets they find a doubling time that's something like six months in recent years which is pretty tremendous relative to the historical rates. We should maybe get into that later and then on the algorithmic progress side, mainly using Imagenet type datasets right now they find a doubling time that's less than one year. So when you combine all of these things the growth of effective compute for training big AIs is pretty drastic.…

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