191–200 of 243 posts
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what's your definition? AGI original definition is median human across almost all fields which I believe is basically achieved. If superhuman (better than best expert) I expect <2030 for all nonrobotic tasks and <2035 for all tasks
A "median human" can run a web search and report back on what they found without making stuff up, something I've yet to find an LLM capable of doing reliably.
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The fact is that not all people exhibit the described behavior. So the actions of corporations cannot be considered unambiguously bad. For example, it will help to cleanse the human gene pool of genes responsible for addictive behavior.
I never suggested they were unambiguously bad, I meant to propose that it is a valid concern to talk about. In addition, with your argument, should you not legalize all drugs in the quest for maximising profits to a select few shareholders? AFAIK, the workings of addiction is not fully known, I.e. it’s not only those with dopaminergetic dispositions that get ”caught”. Upbringing, socioeconomic factors and mental heal…
So we not only improving our pool of genes, but we also conduct a selection of effective cultural practices
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You can get use out of a hammer without understanding how the strong force works. You can get use out of an LLM without understanding how every node works.
Hammer is not a perfect analogy because of how simple it is, but sure let's go with it. Imagine that occasionally when getting in contact with the nail it shatters to bits, or goes through the nail as it were liquid, or blows up, or does something else completely unexpected. Wouldn't you want to fix it? And sure, it might require deep understanding of the nature of the materials and forces involved. That's what I'd d…
We're also pretty good at working around human 'hallucinations' and other inaccuracies. Whether it be someone having a bad day, a brain fart, or individual clumsiness. eg in a (bad) organisation, sometimes we do it with layers of reviews and committees, much like layers of LLMs judging each other.
I think too much is attached to the notion of "we don't understand how the LLM works". We don't understand how any complicated intelligence works, and potentially won't for the forseeable future.
More generally, a lot of society is built up from empirical understanding of black box systems. I'd claim the field of physics is a prime example. And we've built reliable systems from unreliable components (see the field of distributed systems).
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via what paradigm then? What out there gives high enough confidence to set a date like that?
While we don’t know an enormous amount about the brain, we do know a pretty good bit about individual neurons, and I think it’s a good guess, given current science, to say that a solidly accurate simulation of a large number of neurons would lead to a kind of intelligence loosely analogous to that found in animals. I’d completely understand if you disagree, but I consider it a good guess. If that’s the case, then the…
Firstly, by some researchers in the big labs (some of which I'm sure are funded to try random moonshot bets like the above), at non-product labs working on hard problems (eg World Labs), and especially within academia where researchers have taken inspiration from biology before, and today are even better funded and hungry for new discoveries.
Certainly at my university, some researchers are slightly detached from the hype cycle of NeurIPS publications and are trying interdisciplinary approaches to bigger problems. Though, admittedly less than I'd have hoped for). I do think the pressure to be a paper machine limits people from trying bets that are realistically very likely to fail.
It's surprising to me the number of people I consider smart and deep original thinkers who are now parroting lines and ideas (almost word-for-word) from folks like Andrej Karpathy and Sam Altman, etc.
But, of course, "Show me the incentive and I will show you the outcome" never stops being relevant.
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> Why bother developing chatbots Maybe it is the reverse? It is not them offering a product, it is the users offering their interaction data. Data which might be harvested for further training of the real deal, which is not the product. Think about it: They (companies like OpenAI) have created a broad and diverse user base which without a second thought feeds them with up-to-date info about everything happening in th…
> No one in the history of mankind ever had such a holistic view, almost gods eye. I distinctly remember search engines 30 years ago having a "live searches" page (with optional "include adult searches" mode)
My question is this - once you achieve AGI, what moat do you have, purely on the scientific part? Other than making the AGI even more intelligent. I see a lot of talk that the first company that achieves AGI, will also achieve market dominance. All other players will crumble. But surely when someone achieves AGI, their competitors will in all likelihood be following closely after. And once those achieve AGI, academia…
> The only things that will be out of reach for most, is compute - and probably other expensive things on the infrastructure part. That is the moat. That, and training data. Even today, compute and data are the only things that matter. There is hardly any secret software sauce. This means that only large corporations with a practically infinite amount of resources to throw at the problem could potentially achieve AGI…
In a way, all the hype can only indicate that AGI is still a distant illusion. If it were really around the corner we'd be hearing different stories.
Observe what the AI companies are doing, not what they are saying. If they would expect to achieve AGI soon, their behaviour would be completely different. Why bother developing chatbots or doing sales, when you will be operating AGI in a few short years? Surely, all resources should go towards that goal, as it is supposed to usher the humanity into a new prosperous age (somehow).
> it is supposed to usher the humanity into a new prosperous age (somehow). More like usher in climate catastrophe way ahead of schedule. AI-driven data center build outs are a major source of new energy use, and this trend is only intensifying. Dangerously irresponsible marketing cloaks the impact of these companies on our future.
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I guess a counter, is that we don't need to understand how they work to produce a useful output. They are a magical black box magic 8 ball, that more likely than not gives you the right answer. Maybe people can explain the black box, and make the magic 8 ball more accurate. But at the end of the day, with a very complex system it will always be some level of black box unreliable magic 8 ball. So the question then is…
THAT. This is what I don't get. Instead of fixing a complex system let's build more complex system based on it knowing that it might not always work. When you have a complex system that does not always work correctly, you start disassembling it to simpler and simpler components until you find the one - or maybe several - that are not working as designed, you fix whatever you found wrong with them, put the complex sys…