Comment by swalsh
1 day ago
My understanding is GPT 6 works via synaptic space reasoning... which I find terrifying. I hope if true, OpenAI does some safety testing on that, beyond what they normally do.
1 day ago
My understanding is GPT 6 works via synaptic space reasoning... which I find terrifying. I hope if true, OpenAI does some safety testing on that, beyond what they normally do.
From the recent New Yorker piece on Sam:
“My vibes don’t match a lot of the traditional A.I.-safety stuff,” Altman said. He insisted that he continued to prioritize these matters, but when pressed for specifics he was vague: “We still will run safety projects, or at least safety-adjacent projects.” When we asked to interview researchers at the company who were working on existential safety—the kinds of issues that could mean, as Altman once put it, “lights-out for all of us”—an OpenAI representative seemed confused. “What do you mean by ‘existential safety’?” he replied. “That’s not, like, a thing.”
No chance an openAI spokesperson doesnt know what existential safety is
I did not read the response as...
>Please provide the definition of Existential Safety.
I read:
>Are you mentally stable? Our product would never hurt humanity--how could any language model?
The absolute gall of this guy to laugh off a question about x-risks. Meanwhile, also Sam Altman, in 2015: "Development of superhuman machine intelligence is probably the greatest threat to the continued existence of humanity. There are other threats that I think are more certain to happen (for example, an engineered virus with a long incubation period and a high mortality rate) but are unlikely to destroy every human in the universe in the way that SMI could. Also, most of these other big threats are already widely feared." [1]
[1] https://blog.samaltman.com/machine-intelligence-part-1
Why are these people always like this.
Amusing! Even if they believe that, they should know the company communicated the opposite earlier.
Likely an improvement on:
> We study a novel language model architecture that is capable of scaling test-time computation by implicitly reasoning in latent space. Our model works by iterating a recurrent block, thereby unrolling to arbitrary depth at test-time. This stands in contrast to mainstream reasoning models that scale up compute by producing more tokens. Unlike approaches based on chain-of-thought, our approach does not require any specialized training data, can work with small context windows, and can capture types of reasoning that are not easily represented in words. We scale a proof-of-concept model to 3.5 billion parameters and 800 billion tokens. We show that the resulting model can improve its performance on reasoning benchmarks, sometimes dramatically, up to a computation load equivalent to 50 billion parameters.
<https://arxiv.org/abs/2502.05171>
Oh you mean literally the thing in AI2027 that gets everyone killed? Wonderful.
AI 2027 is not a real thing which happened. At best, it is informed speculation.
Funny if you open their website and go to April 2026 you literally see this: 26b revenue (Anthropic beat 30b) + pro human hacking (mythos?).
I don’t think predictions, but they did a great call until now.
1 reply →
That's sounds really interesting. Do you have some hints where to read more?
Oh, of course they will /s