At Epoch, helping to clarify when and how transformative AI capabilities will be developed.
Previously a Research Fellow on the AI Governance & Strategy team at Rethink Priorities.
Ben Cottier
Karma: 329
Data on AI
Announcing Epoch’s newly expanded Parameters, Compute and Data Trends in Machine Learning database
To be clear (sorry if you already understood this from the post): Running BLOOM via an API that someone else created is easy. My claim is that someone needs significant expertise to be able to run their own instance of BLOOM. I think the hardest part is setting up multiple GPUs to run the 176B parameter model. But looking back, I might have underestimated how straightforward it is to get the open-source code to run BLOOM working. Maybe it’s basically plug-and-play as long as you get an appropriate A100 GPU instance on the cloud. I did not attempt to run BLOOM from scratch myself.
I recall that in an earlier draft, my estimate for how many people know how to independently run BLOOM was higher (indicating that it’s easier). I got push-back on that from someone who works at an AI lab (though this person wasn’t an ML practitioner themselves). I thought they made a valid point but I didn’t think carefully about whether they were actually right in this case. So I decreased my estimate in response to their feedback.
Trends in the dollar training cost of machine learning systems
Conclusion and Bibliography for “Understanding the diffusion of large language models”
Questions for further investigation of AI diffusion
Implications of large language model diffusion for AI governance
Publication decisions for large language models, and their impacts
Drivers of large language model diffusion: incremental research, publicity, and cascades
The replication and emulation of GPT-3
GPT-3-like models are now much easier to access and deploy than to develop
Background for “Understanding the diffusion of large language models”
Understanding the diffusion of large language models: summary
Modeling Failure Modes of High-Level Machine Intelligence
Personal AI assistants seem to have one of the largest impacts (or at least “presence”) mainly due to the number of users. The impact per person seems small—making life slightly more convenient and productive, maybe. Not sure if there is actually much impact on productivity. I wonder if there is any research on this. I haven’t looked into it at all.
Relatedly, chatbots are certainly used a lot, but I’m uncertain about its current impacts beyond personal entertainment and wellbeing (and uncertain about the direction of the impact on wellbeing).
What 2026 looks like has a few relevant facts on the current impacts, and interesting speculation about the future impacts of personal assistants and chatbots. E.g. facts:
“in China in 2021 the market for chatbots is $420M/year, and there are 10M active users. This article claims the global market is around $2B/year in 2021 and is projected to grow around 30%/year.”
I don’t feel surprised by those stats, but I also hadn’t really considered how big the market is.
Nice! A couple things that this comment pointed out for me:
Real time is not always (and perhaps often not) the most useful way to talk about timelines. It can be more useful to talk about different paths, or economic growth, if that’s more relevant to how tractable the research is.
An agenda doesn’t necessarily have to argue that its assumptions are more likely, because we may have enough resources to get worthwhile expected returns on multiple approaches.
Something that’s unclear here: are you excited about this approach because you think brain-like AGI will be easier to align? Or is it more about the relative probabilities / neglectedness / your fit?
Modeling the impact of safety agendas
I’m excited about this project. I’ve been thinking along similar lines about inducing a model to learn deception, in the context of inner alignment. It seems really valuable to have concrete (but benign) examples of a problem to poke at and test potential solutions on. So far there seem to be less concrete examples of deception, betrayal and the like to work with in ML compared to say, distributional shift, or negative side effects.
Previous high level projects have tried to define concepts like “trustworthiness” (or the closely related “truthful”) and motivated the AI to follow them. Here we will try the opposite: define “betrayal”, and motivate the AIs to avoid it.
Why do you think the betrayal approach is more tractable or useful? It’s not clear from the post.
What is the source for the “JP Morgan note”?