dannyobrien
Born on October 07, 2010•5030 Karma
But in the long term you probably don't want two fully featured p2p networking stacks in your dependencies.
In a previous life, I'd been a writer for the original You Don't Know Jack game (the UK variant), where the job was to crank out as many funny quips about a topic as you could, and then use a handful of them in the recording of the game itself. Some of the later JackBox games are like that, but for the players -- you're given a set piece, have to come up with little funny improvisations within a time limit.
As an experiment, I tried the set-up lines with the OpenAI API, and see whether it could come up with some responses. Of course, 90% of them were unfunny or incoherent, but 1/10 were not bad, or even pretty good.
I'm not sure that would have been impressive to anyone else -- but remember, I'd had this as a job, and sat in a writer's room, where everyone did this, for hours. In that environment, you expect a large proportion to be duds: the discipline is keep pumping them out, and not flagging creatively until you find a rich vein. I realised that this was a tool that would have been the perfect complement to that work -- and it was a pretty good JackBox player too.
Also, I thought the response by Benn Jordan on Bluesky was informative. https://blog.andymasley.com/p/contra-benn-jordan-data-center...
> When low-frequency sound becomes strong enough to be heard or otherwise felt, it can cause annoyance, discomfort, and sleep disruption like any other normal noise pollution.
So which is it? Sure, I don’t really believe that there is magical super special harmful noise from a datacenter, but are these monster datacenters emitting disruptive amounts of low frequency sound or are they not?
Ad hominem would be if shimman had said something like "don't post rebuttals from people who are stupid meanyheads". Identifying a characteristic of the posters that affects their incentives is a perfectly legitimate reason to discredit their posts, or at least call their impartiality into question.
>Ad hominem (Latin for 'to the person'), short for argumentum ad hominem ('an argument to the person'), refers to when a speaker attacks the character, motive, or some other attribute of the person making an argument rather than the substance of the argument itself.
>motive
Emphasis mine.
He posted a general update today on LinkedIn which I think gives the wider context:
https://www.linkedin.com/feed/update/urn:li:activity:7463481...
> Not even half-way through this hashtag#curl release cycle we are already at 11 confirmed vulnerabilities - and there are three left in the queue to assess and new reports keep arriving at a pace of more than one/day.
> 11 CVEs announced in a single release is our record from 2016 after the first-ever security audit (by Cure 53).
> This is the most intense period in hashtag#curl that I can remember ever been through.
See the definitions of "O" and related glyphs for a good example[2].
[1] https://github.com/be5invis/PatEL
[2] https://github.com/be5invis/Iosevka/blob/main/packages/font-...
> Do we really think things will move this fast? Sort of no - between the beginning of the project last summer and the present, Daniel’s median for the intelligence explosion shifted from 2027 to 2028. We keep the scenario centered around 2027 because it’s still his modal prediction (and because it would be annoying to change). Other members of the team (including me) have medians later in the 2020s or early 2030s, and also think automation will progress more slowly. So maybe think of this as a vision of what an 80th percentile fast scenario looks like - not our precise median, but also not something we feel safe ruling out. [2]
I don't think this changes your observation that he is "personally invested" (i.e. believes this trendline will continue), but I'm pretty sure when AGI doesn't appear in 2027, many people will believe that this invalidates the arguments being made here (or in the report). The actual report was intended to give a feel for what a near-future "disaster" AGI scenario, and settled on a date to give that some concrete immediacy. The collective review that gave that as a possible, but not inevitable date is still ongoing (they originally pushed their best estimate out a bit further, but now they think, judging by the goals that are being hit, their scenario was a little too conservative). [3]
[1] https://freddiedeboer.substack.com/p/im-offering-scott-alexa... [2] https://www.astralcodexten.com/p/introducing-ai-2027 [3] https://blog.aifutures.org/p/grading-ai-2027s-2025-predictio...
LLMs are nothing close to AGI and not going to lead to it, they can’t distinguish right from wrong, they can’t count, they can’t reason, they generate plausible text from a vast databank of connected text.
Apparently that is enough to fool many people but it’s nothing close to AGI which would require internal models of the world, reasoning etc.
We are nowhere close to AGI and the fools who predicted we were will unfortunately keep lying about their stated timelines when it inevitably doesn’t arrive. You’re already hedging and trying to caveat previous predictions, as OpenAI did with their AGI predictions which they’re now furiously back-pedalling on.
Do you mean "token" as in the LLM sense?
Or are you thinking that thoughts in the human brain are also constructed out of some sort of underlying "token" even though the abstract thought happens and is held before any words are used to try to communicate that thought to an external party?
Human's don't operate the same way, the thought happens and then the words are generated to reasonably describe that thought.
Thoughts don't happen in a vacuum, they are triggered by external or internal stimuli, and these stimuli/thought precursors could very easily be tokens (dense info packets), which then map to latent space vectors, which very well could be thoughts.
Claims like "humans don't operate the same way" has no basis. Not only do we literally not know how humans operate mechanistically, and so we literally don't know the logical structure of human thought, but any system that is Turing complete is so easy to create that many wildly different mechanistic systems are fundamentally equivalent/interconvertible.
Yes, possible, that's why I asked you above if that's what you meant by "token". Someone else responded and I didn't notice it wasn't you.
> Claims like "humans don't operate the same way" has no basis. Not only do we literally not know how humans operate mechanistically, and so we literally don't know the logical structure of human thought, but any system that is Turing complete is so easy to create that many wildly different mechanistic systems are fundamentally equivalent/interconvertible.
I think this position is too extreme, we do have some information.
We know how LLM's work when generating a sequence of words and I know that my brain does not work the same way for word generation because I am fully aware of the complete thought in advance of any words getting generated by me externally or internally.
I know prior to generating words that my thought is X and the words I'm about to produce need to express that thought.
But with LLM's we know that the essence of what they produce is not known in advance, that it must complete the word generation process to fully realize the end result and that multiple different end results are possible.
Additionally "from learned stats" doesn't disambiguate between a wider variety of things. I'm not aware of any other way to acquire knowledge from measurements. I'd bet that humans do this differently, based on the fact the humans can get further with less training data and that they learn actively during operation, but not so differently that 'learning stats' would be an inaccurate description.
If that were the case, then the systems would generate words based on the fully resolved idea, but that is not how the LLM systems currently work (per vendors descriptions).
They choose words sequentially and both the specifics of the input as well as the chosen output words significantly impacts not just the rest of the output but the very correctness of the output.
> but not so differently that 'learning stats' would be an inaccurate description.
Agreed, humans are generalizing using some mechanism that can be modeled with math.
But the execution of our reasoning and thought processes is not obviously similar to LLM's next word generation based on probabilities.
Anthropic says of the their model[0]:
"""Claude sometimes thinks in a conceptual space that is shared between languages, suggesting it has a kind of universal “language of thought.”
{...}
Claude will plan what it will say many words ahead, and write to get to that destination. We show this in the realm of poetry, where it thinks of possible rhyming words in advance and writes the next line to get there. This is powerful evidence that even though models are trained to output one word at a time, they may think on much longer horizons to do so."""
Anthropic also created 'golden gate claude'[1] by identifying the region of its architecture that corresponded to the concept of the golden gate bridge and activating it. What would such a region exist for if claude could only think one token at a time?
>the execution of our reasoning and thought processes is not obviously similar to LLM's
"Not obviously similar" I can agree with. I don't think you've identified a way in which they are obviously different, though.
[0] https://www.anthropic.com/research/tracing-thoughts-language...
They can predict likely sentences but not evaluate truth or logic. They can fairly reliably record facts about the world but not construct internal models of the world.
They do probabilistically. So do humans as a matter of fact. The best of us are better at it than LLMs, but that's not persuasive evidence of anything meaningful really.
> They can fairly reliably record facts about the world but not construct internal models of the world.
You don't know that, unless your presuppose a very specific definition of world model that necessarily precludes emergent ones.
You’re constructing a post-hoc fantasy of human thought based on how LLMs work because you are desperate for some reason to believe that they are thinking like humans, but they are not. The process is very different and the results are also different.
Argument?
Are LLMs close to being able to significantly help AGI researchers?
Lewis is right that most of these principles were described before the manifesto, but I can vouch for the near-impossibility in many contexts of convincing anyone who wasn't a coder (and a lot of coders too) why these might be sensible defaults.
For every person burned by a subsequent maladaptive formalization of these principles, there was someone horribly scarred before the agile manifesto by being forced to go through a doomed waterfall process.
Ask anyone with 30 years in the industry whether "agile", for all its problems, was a force for good or bad, and the answer will be an emphatic Good!
If nothing else, it gave us ammunition to argue against the impossibility of delivering a fixed thing in a fixed amount of time - which was the universal view from senior stakeholders of what competent software delivery looked like.