In multiple threads now you've claimed I was wrong and then ended the conversation without any assertion, claim, or argument I could rebut. You've ended those conversations with snarky comments clearly designed to shut down debate. This is not how hackernews benefits.
Edit: Nevermind, realized he's just an uneducated troll just looking for his kicks. Comment flagged.
This is a failure to engage the clear arguments and claims made. If you don't want to debate something, why bother commenting at me? Was I supposed to just cede ground and accept your framing wholesale? I'm putting forth very clear and open-to-rebuttal assertions which is what you should do.
Edit: Nevermind, realized he's just an uneducated troll just looking for his kicks. Comment flagged.
Can you explain how 'recursive self-improvement' functions without 'endless benchmark chasing'? I mean, RSI is literally that.
What do you think they're improving on? How would a model self-improve without some metric/data of some kind to check? When you have metrics+data, that is a benchmark. And yes, simulations and or soft-verification like LLM judges are still a kind of benchmarking. Maybe its not a static benchmark they can easily hack.
Folks -- RSI does not mean the self-improvement is them going to therapy and seeking inner peace to overcome trauma.
"For the past 300,000 years, Earth has had only one form of advanced intelligence on it: humans. With the recent advent of AI foundation models, some believe we are at the dawn of a new kind of intelligence. As AI continues to evolve, we may witness the proliferation of diverse intelligent lifeforms coexisting with us."
I am not even going to link more than one thing I think I've made my point
It's natural for literally any AI lab to end up doing auto-research, since 'auto-research' is literally just 'autonomous AI' which is the whole darn point of all of this. I'm not going to hand out genius brownie points to folks working on RSI because of course its powerful. How about we hand brownie points to the folks who do things that are not hype and end up being important/powerful?
> was posed specifically under the framing of there needing to be more fundamental research beyond squeezing as much as we can out of relatively vanilla transformer stacks.
Not to be contentious, but this is so broad of a description that it could include literally thousands of papers in the last year or two. I'm imagining double digits or more if we go back the full decade.
I'm saving brownie points for people who deserve them
OpenAI is not Sam Altman
Anthropic is not Dario Amodei
and Sakana is not David Ha
Organizations, especially businesses, are not individuals. If the implication is that David Ha has always been doing this, and will always be doing this, and that Sakana is David Ha ... then that's a far worse insult to the employees at Sakana than my little tweaking.
Wait are you saying that because the Government lied and blocked corporations from exercising freedom of speech and commerce that therefore the government couldn't possibly be seen to be collecting the funds? Your logic is that if the Government lies we are assumed to have believed it and therefore have no recourse. Most people (not all) are nowhere near as dumb as you seem to think they are.
Thank goodness we have you passing judgment on the internet; otherwise who else would be around for us to do it? I'm glad you're willing to destroy someone for a mistake rather than letting them learn and change. We all know that arbitrary and harsh punishments solve everything.
Shall We Play a Game? Language Models for Open-ended Wargames
Wargames are simulations of conflicts in which participants' decisions influence future events. While casual wargaming can be used for entertainment or socialization, serious wargaming is used by experts to explore strategic implications of decision-making and experiential learning. In this paper, we take the position that Artificial Intelligence (AI) systems, such as Language Models (LMs), are rapidly approaching human-expert capability for strategic planning -- and will one day surpass it. Military organizations have begun using LMs to provide insights into the consequences of real-world decisions during _open-ended wargames_ which use natural language to convey actions and outcomes. We argue the ability for AI systems to influence large-scale decisions motivates additional research into the safety, interpretability, and explainability of AI in open-ended wargames. To demonstrate, we conduct a scoping literature review with a curated selection of 100 unclassified studies on AI in wargames, and construct a novel ontology of open-endedness using the creativity afforded to players, adjudicators, and the novelty provided to observers. Drawing from this body of work, we distill a set of practical recommendations and critical safety considerations for deploying AI in open-ended wargames across common domains. We conclude by presenting the community with a set of high-impact open research challenges for future work