tomjohnneill
Joined 9 years ago
Karma
341
Submissions
69
Member since
Feb 22
Recent submissions
Comment · 1 month ago
Now you've successfully trained the next model not to put anything suspicious in its chain of thought. And unfortunately that's exactly what OpenAI and Anthropic seem to be doing (sometimes accidentally)
Comment · 1 year ago
Did the agents you built work? I think we're doing something similar in the UK and intrigued to know how it went for you (https://www.plinth.org.uk)
Comment · 1 year ago
I'd be very interested to hear more about how the non-for-profit agents platform went/anything else you learnt.
Comment · 1 year ago
And it's ASML more than TSMC.
Or maybe there's a highly profitable role for all the different parts of the value chain.
Comment · 2 years ago
Plinth (https://plinth.org.uk/) | London (UK) | Senior Engineer (mostly Frontend) | Onsite
At plinth, we've built a software/AI platform for charities to help them measure their impact and get more funding for the work they're doing. We work with both charities and funders (e.g. foundations, Government), providing a way to easily collect, visualise and report on client and impact data.
We're looking for an engineer with experience with React, NextJS etc, who wants to spend almost all their time heads down writing code, and not spending time in meetings.
To apply, try our puzzle: https://www.plinth.org.uk/puzzle
Comment · 2 years ago
I think it's a case of tech bubble vs the rest of the world. Most people are not subscribing to the paid version of ChatGPT, but a lot of people who spend a lot of time with these things are.
Comment · 2 years ago
It's the default for the free version of ChatGPT no? That's what the majority of people use.
Comment · 2 years ago
I'm not sure. The figures I've seen suggest that GPT3 required 10x more energy to train than GPT2 (e.g. https://www.nnlabs.org/power-requirements-of-large-language-....), so I think a roughly 1-2 order of magnitude increase in energy usage from GPT2 to GPT3.5 makes sense.
Comment · 2 years ago
I can definitely imagine they're not covering the amortised cost of the training with the cost per individual inference request. It seems less likely to me that they're making a significant loss on each subsequent request, but again no source from me on that either.
Looking a bit more into this, I found this paper: https://arxiv.org/pdf/2311.16863.pdf. It references a table saying that text generation uses 0.047 kWh per 1000 inferences, which is 1-2 orders of magnitude lower than my estimate. Though that is for GPT2, so possibly tracks to something roughly in the ~0.001 kWh per inference for GPT3.5.
Comment · 2 years ago
That's fair about ignoring the training cost. I did write a bit more going into that in a follow up piece here: https://notfunatparties.substack.com/p/ai-is-good-for-the-pl...
Do you have any better sources for the power usage stats? It would be good to get a bit closer on that front. Having said that, even if the cost share is closer to 80%, that still puts it on par with a laptop for an average person.
Comment · 4 years ago
Maybe decoding Herculaneum scrolls? https://en.wikipedia.org/wiki/Herculaneum_papyri#Virtual_unr...
Comment · 4 years ago
It's been done (unsurprisingly): https://www.theaccessgroup.com/en-gb/products/mosaic/
Emails: tom@ at the startup domain