> All these points are valid, and OpenAI did a great job identifying potential risks, especially misuse and biases, at an early stage.
Many of the OpenAI employees who were focused on these risks in GPT-2 later founded Anthropic, notably Dario [1]. Since the beginning and continuing through today Anthropic describes itself as an "AI safety and research company" [2]
I'm not sure if the OpenAI of today has the same focus on safety, or if they do the minimum to not look irresponsible given Anthropic's effort.
>The ARR were fine but showing skewed quarterly profitability numbers by slowing down research due to hitting compute capacity suggests otherwise.
I have to say, I find this really puzzling. We know for a fact that Anthropic are making bank on metered inference. That's their biggest source of profitability, we are seeing software companies start to majorly adopt coding agents over just the last few months.
Right as the biggest driver of enterprise adoption is accelerating, and it's tied to their biggest profit vector, you find it suspect that their profits are increasing significantly?
Also, can you clarify what you mean by "slowing down research" exactly? Do you mean they're not doing big pretraining runs? Less compute available for researchers? Scaled back RL?
>Also just to confirm, AI subscriptions are definitely being sold at a loss how big I don't know but these models are much harder to run.
Maximum usage of AI subscriptions is a loss, but do we actually know how that nets out? Has anyone done any research to try to figure that out?
> can you clarify what you mean by "slowing down research"
He is claiming that they have been investing less in R&D and that this is juicing their numbers in an unsustainable way given how close the competition is to catching up. His evidence is the content and cadence of model releases recently. (I'm not taking a position one way or the other, just clarifying for you.)
> Maximum usage of AI subscriptions is a loss, but do we actually know how that nets out?
They almost certainly don't have to care. All the enterprise accounts use the API pricing AFAIK and that appears to be profitable and is expected to be the vast majority of the usage in the medium to long term (if it isn't already).
On the surface, that's quite fair. However, there's one problem: it is much easier to make statements than to verify them, and that asymmetry is part of why the internet has been slowly eroding society.
It's useful/necessary to use past writing/arguments from an author to say whether they should actually receive any further critical evaluation, or be dismissed. We shouldn't say definitively "they're always wrong, so they're wrong now". However, it's reasonable to say: the author has a demonstrated lack of credibility, so we can probably assume they're wrong here, particularly if they have been wrong in this domain so many times before. Or if they happen to be correct, it's probably not strongly demonstrated by their work.
I like how in spite of the author explaining why (father of two small children that occupy his free time), you jumped to the most negative set of possibilities. Instead, it sounds like when he's with his children, he is focusing on them instead of on productivity, which is the opposite of what you're suggesting.
Also, if he instead chose to occupy his drive time with listening to a comedy podcast, or NPR, or even a technical podcast, I can't help but imagine you wouldn't give it a second thought, in spite of that being just as "productive" and "avoiding thinking about the tough things".
I will say, I find it fascinating that there are some philosophers and consciousness researchers who seem to be less certain. I just listened to Chris Hayes interview David Chalmers this week, whose position seemed to be that it's probably not conscious, but that we can't be certain. And more than that: he seemed open to the idea that they may become conscious under further scaling/training/advancements.
Funny enough, the models seemingly go insane and decohere into noise output in the absence of sensory input, which is remarkably similar to what would happen to a human.
That said, I'm not sure I follow what you're actually asking here? I'll also note that I'm not taking a position one way or the other, just sharing a podcast and noting that an extremely reputable scholar on the subject of consciousness seems to have a bit more uncertainty and humility than many commenting here. ;)
LLMs just wait for a prompt, so they do nothing and are just frozen in place.
I'll find time to listen to your link, it sounds interesting. My objection is the strange idea that humans are automatons that are keyed off input like a clockwork machine and operate sequentially. This is clearly not the case.
>LLMs just wait for a prompt, so they do nothing and are just frozen in place.
I'm not sure that's a compelling argument. Humans can be put into a similar state where they are unconscious and not thinking. Think of someone in a coma, for example, where we actually measure and confirm that there is no brain activity where they're in that state.
They are not actively conscious, but that doesn't nullify their consciousness from when they were awake, right?
>My objection is the strange idea that humans are automatons that are keyed off input like a clockwork machine and operate sequentially. This is clearly not the case.
Well, a few thoughts here. First, it's worth noting that the argument isn't necessarily that AI are conscious in the way that humans are, nor that humans are strictly automatons.
But I think the more interesting thing is that our understanding about consciousness has evolved quite a bit in just the last fifty to one hundred years. We used to think that only humans were conscious, but assumed that primates, cows, dogs, and other mammals were just automatons. Then we started to think: okay, maybe primates are conscious. Then eventually: well, dogs also seem to have consciousness, and then rodents, etc.
This has continued such that most people in the study of consciousness think all mammals are conscious, and the debate is shifting down to insects and other creatures that we do think/have thought of more as automatons. We don't actually know where to draw the line, because it's essentially impossible to really feel/know the inner states of other living beings.
In the face of all this uncertainty, Chalmers just points out that since we understand consciousness so little, that ultimately we should probably be less definitive in pronouncing which things do or do not have it.
> I'm not sure that's a compelling argument. Humans can be put into a similar state where they are unconscious and not thinking. Think of someone in a coma, for example, where we actually measure and confirm that there is no brain activity where they're in that state.
He was responding to your comment
> Funny enough, the models seemingly go insane and decohere into noise output in the absence of sensory input
The assumption being that "sensory input" is a prompt. What did you mean by sensory input?
Yeah, I have to admit to finding it somewhat ironic that some individuals accuse the "pro AI" folks of magical thinking, when it seems that escalating levels of magical thinking are being used by the "anti" crowd to suggest that the models can never achieve something akin to human intelligence (particularly in light of the fact that they have on certain dimensions done exactly that).
It's pretty clear that there are significant differences between their intelligence and human intelligence. But that doesn't mean there isn't some sort of intelligence here.
>If anything AI will be used to correct all the crappy human made code that is still being pushed due to the vanity of coders still pretending that they are better than AI at coding.
In my organization, this is already happening. We've been using LLMs to boost our test coverage without touching our human code, then use that as a scaffold to let it go through and refactor, clean up, and optimize, and then validating against both our tests and gold standard test datasets.
In our case, it's made a legacy codebase far more readable to our junior engineers, and the performance improvements (from using an autoresearch-style approach) has resulted in a six figure decrease in our compute spend for the production service we trialed this on.
I'm not sure an article that gives one paragraph summaries of the common anti-LLM talking points is really a substantial contribution to the conversation. This is essentially a snarked up version of a "Criticisms" section one would expect to find in a Wikipedia article on modern generative AI. It's fairly hollow unless you've had your head in the sand and are just getting up to speed on the current conversation.
Your statement seems to be implying (correctly) that LLMs can program, but just not as well as humans. If they're able to program presumably without "thinking" as you seem to be (implicitly) narrowly defining it, then why do you think that limits them to always being sub-par?
It seems like if they can do it, that there's no reason they can't eventually be trained to do it better up to and beyond human performance. It seems strange to suggest that thinking unlocks some nominal margin of "better" specifically that can't be overcome.
All of that aside, even if they can't outperform the top human programmers...what if they get to within a margin where they're still better than most? Isn't a 95th percentile programmer that can run 24/7 and continuously refine its work still going to ultimately come out on top?
I'm more interested in the conclusion that programming doesn't require thinking. And that's where the argument breaks. It seems so obvious, but sometimes the most obvious things are the least true.
>I'm more interested in the conclusion that programming doesn't require thinking.
I suspect it largely has to do with how one defines "thinking". It seems like people like to implicitly define it in such a way as to require a human (or animal), but there are many examples of thinking/intelligence in nature that don't require a brain or even neurons.
I'm genuinely curious: without using the word "think" with all of its ambiguity, can you articulate what it is that we're doing that these models are not capable of? Because it's pretty clear (to me, at least) from the research, particularly a lot of the mechanistic interpretability work coming out of Anthropic, that the models are at least doing something akin to what we think of as thinking, even if it appears foreign to us.
From the link:
> They summarized their findings from the nine months:
> 1. Humans find GPT-2 outputs convincing.
> 2. GPT-2 can be fine-tuned for misuse.
> 3. Detection is challenging (detection rates of ~95% for detecting 1.5B GPT-2-generated text by RoBERTa).
> We’ve seen no strong evidence of misuse so far.
> We need standards for studying bias.
>
> All these points are valid, and OpenAI did a great job identifying potential risks, especially misuse and biases, at an early stage.
reply