One of the main goals at the moment is to keep thinking human legible. You can imagine how much harder it’d be to do root cause analysis on the recent OpenAI event if we couldn’t even tell what they’re thinking.
Could you please elaborate a bit more for my understanding?
What in particular about this method breaks correct token boundaries?
On my first read I read your comment as there are special tokens that require multiple tokens to emit, hence you can't get certain tokens emitted alone - but I don't think that's what you're getting at on a second read?
Interesting that you've found similarities between "d" and the hidden tokens for opening an xml tag, pressing caps lock and the other hidden tokens of note. I haven't run into any trouble extracting "d" tokens, is it a particular model that you see create that pattern?
That's " 'd ".strip(), an english contraction suffix. it's 1 token, but using this echo approach you will be served the apostrophe and the subsequent letter for the first time in different steps.
I couldn't reproduce this behavior with Sonnet 4, and Sonnet 3.7 has been deprecated since I messed with this stuff. You can try tokenizing the string "<hello> </hello>"
I think the correct tokenization of the string will not have any tokens that contain mixed punctuation and letters, but the result of this approach does contain such claimed tokens.
I'm talking about per-request model remember? With extensive prompt you realistically can have one request every 10 minutes because the agent will be busy for at least 10 minutes executing it. They aren't rate limiting that.
There’s a lot of tradeoffs to play with, those inference ASICs may not carry the gradient but they are still optimised for larger batches and to run any model. They need enough memory for the weights, wide batch inference, and ideally leftovers for kv cache efficiency.
For personal inference you’re given a lot more room to play in - much of it poorly explored today - enough to concern an argument of cost advantages evaporating
You can ablate surprisingly large chunks of a model with near to no effect, you can try this easily - download an open weight model in torch.
Obviously it’s not ideal but you could likely have single digit % of all weights affected and still have a useful model (many caveats here: e.g. locality of damaged weights matters, distribution of errors matters, fail high/low matters, …)
I can’t speak for the states, but in AU I clearly see a massive displacement of undergrad and junior roles (only in AI exposed domains).
I say this as both someone who works with many execs, hearing their musings, and someone who no longer can justify hiring junior roles themselves.
Irrespective of that; if we take this strategy of only taking action once it is visible to the layman - our scope of actions available will be invariably and significantly diminished.
Even if you are not convinced it is guaranteed and do not believe what myself and others see. I would ask you is your probability of it happening now really that close to 0? If not then would it not be prudent to take the risk seriously?
Politics - proper guardrails, adapting the legal framework to accommodate AI and make sure it doesn't benefit only preselected few.
Something that can and should be done yesterday is to stop the capital drain out of the economy and into accelerated, war-motivated AI development - there's no need for war-AI per se but clearly it's the most likely reason for the capital drain and rush.
Once the rush and wars stop, and some capital is made available for the rest of the economy, the latter can adapt to the introduction of AI at a normal pace, that should include legislative safeguards to support competition and prevent monopolization of AI and information sources.
