6 items tagged “jack-clark”
2024
The main innovation here is just using more data. Specifically, Qwen2.5 Coder is a continuation of an earlier Qwen 2.5 model. The original Qwen 2.5 model was trained on 18 trillion tokens spread across a variety of languages and tasks (e.g, writing, programming, question answering). Qwen 2.5-Coder sees them train this model on an additional 5.5 trillion tokens of data. This means Qwen has been trained on a total of ~23T tokens of data – for perspective, Facebook’s LLaMa3 models were trained on about 15T tokens. I think this means Qwen is the largest publicly disclosed number of tokens dumped into a single language model (so far).
GPT-2 five years later. Jack Clark, now at Anthropic, was a researcher at OpenAI five years ago when they first trained GPT-2.
In this fascinating essay Jack revisits their decision not to release the full model, based on their concerns around potentially harmful ways that technology could be used.
(Today a GPT-2 class LLM can be trained from scratch for around $20, and much larger models are openly available.)
There's a saying in the financial trading business which is 'the market can stay irrational longer than you can stay solvent' - though you might have the right idea about something that will happen in the future, your likelihood of correctly timing the market is pretty low. There's a truth to this for thinking about AI risks - yes, the things we forecast (as long as they're based on a good understanding of the underlying technology) will happen at some point but I think we have a poor record of figuring out a) when they'll happen, b) at what scale they'll happen, and c) how severe their effects will be. This is a big problem when you take your imagined future risks and use them to justify policy actions in the present!
As an early proponent of government regulation around training large models, he offers the following cautionary note:
[...] history shows that once we assign power to governments, they're loathe to subsequently give that power back to the people. Policy is a ratchet and things tend to accrete over time. That means whatever power we assign governments today represents the floor of their power in the future - so we should be extremely cautious in assigning them power because I guarantee we will not be able to take it back.
Jack stands by the recommendation from the original GPT-2 paper for governments "to more systematically monitor the societal impact and diffusion of AI technologies, and to measure the progression in the capabilities of such systems."
2023
GPT-4, like GPT-3 before it, has a capability overhang; at the time of release, neither OpenAI or its various deployment partners have a clue as to the true extent of GPT-4's capability surface - that's something that we'll get to collectively discover in the coming years. This also means we don't know the full extent of plausible misuses or harms.
The 21st century is being delayed: We’re stuck with corporations building these incredible artifacts and then staring at them and realizing the questions they encode are too vast and unwieldy to be worth the risk of tackling. The future is here – and it’s locked up in a datacenter, experimented with by small groups of people who are aware of their own power and fear to exercise it. What strange times we are in.
— Jack Clark, on MusicML
2022
These kinds of biases aren’t so much a technical problem as a sociotechnical one; ML models try to approximate biases in their underlying datasets and, for some groups of people, some of these biases are offensive or harmful. That means in the coming years there will be endless political battles about what the ‘correct’ biases are for different models to display (or not display), and we can ultimately expect there to be as many approaches as there are distinct ideologies on the planet. I expect to move into a fractal ecosystem of models, and I expect model providers will ‘shapeshift’ a single model to display different biases depending on the market it is being deployed into. This will be extraordinarily messy.
All these generative models point to the same big thing that’s about to alter culture; everyone’s going to be able to generate their own custom and subjective aesthetic realities across text, video, music (and all three) in increasingly delightful, coherent, and lengthy ways. This form of fractal reality is a double-edged sword – everyone gets to create and live in their own fantasies that can be made arbitrarily specific, and that also means everyone loses a further grip on any sense of a shared reality. Society is moving from having a centralized sense of itself to instead highly individualized choose-your-own adventure islands, all facilitated by AI. The implications of this are vast and unknowable. Get ready.