If someboy posts something on X...I just assume its a performative act, you dont want to reach an audience and I just ignore. Never creating an account to hear an opinion ....
Aren't most opinions locked behind an account, usually with a pay wall in front?
I don't mean new tech, speaking of traditional ones.
Politicians or whomever, interview with a journalist locked behind a cable subscription or a newspaper or magasine? Someone sharing their thoughts in their latest book bought at one of 3 places with one of the 2 available transactions companies that link you to the final "content".
I think subway rail systems do have "real live location", but whether that information is consistently provided to public distribution for use through apps is a question mark.
I suspect most systems are a mix of your platinum, gold and silver (and kidding me).
Lots of systems have some sort of real location data, but it can be... shaky.
In Dublin, train 'realtime location' is, AIUI, based on arrival at stations plus time-based estimate. This _usually_ works okay. Buses have transponders which register when they pass a stop. Or, ah, well, buses usually have transponders, and those transponders sometimes work. New buses sometimes lack them, as do very old buses used for covering capacity problems. If a bus without a transponder is used, they seem to just estimate where it is based on the schedule, which is useless. If a bus without a transponder is inserted out of schedule (they sometimes do this at peak time) it's totally invisible to the realtime system. Sometimes, the transponder isn't picked up, and then the bus will appear to hang around for a while and then abruptly teleport a few stops forward. Sometimes, a bus's transponder doesn't seem to start working when the bus starts up for the first few stops.
Still, it's better than when I was a kid, when you really just had the printed schedule, assuming the rain hadn't gotten to it.
I remember how the "live map" for public transport in Berlin was actually a map with where the transport should be, not where it actually was. At least that was the case a couple of years ago with VBB =)
I do see some isolated improvements whenever I travel to Germany (mostly to Frankfurt, but what is pain other than Frankfurt am Main), but yeah I sympathize here, and do hope the pushes toward better systems continue
"For a cost comparison, during our controlled testing, human participants were paid $115 per 90-minute session, plus $5 per game completed. Participants attempted approximately nine games per session, roughly $12.78 per attempted game before bonuses.
Most of this fee pays for the participant’s time and willingness to take the test, rather than the energy their brain uses (a closer proxy to compare with AI). If we look at only the brain’s energy, and price it as electricity, the estimate drops to about 0.6 cents per session, or 0.067 cents per game attempted."
Well I dont know about all of you, but I am celebrating meat based humans...
I think raw brain energy is not a fair comparison. Humans are not willing and able to serve requests at identical competence all hours of the day. You have to invest considerable resources to get a person to even do so for part of the day.
Don't know if you're referring to the headline or the body (which is paywalled). The current headline reads "OpenAI says it has overtaken Anthropic with its latest AI model". Which makes me wonder whether FT itself changed a headline along the lines of what you wrote in the past few minutes?
Apparently Greg Brockman said that as far as he's concerned it may be AGI, or something along those lines.
OTOH OpenAI have their own meaningless definition of AGI as "able to do most commercially valuable work" or somesuch, which I'm sure is not true, and is also not what I'd call AGI.
If it can't learn for itself, then it's certainly not AGI.
The biggest significance of anyone at OpenAI calling it AGI is their contract with Microsoft, giving MSFT access to all their IP, is based around them having achieved AGI, but presumably they can't just declare that unilaterally.
Maybe they see getting out of the Microsoft contract as necessary to IPO.
>OTOH OpenAI have their own meaningless definition of AGI as "able to do most commercially valuable work" or somesuch, which I'm sure is not true, and is also not what I'd call AGI.
It's not meaningless. Specifically it is - "highly autonomous systems that outperform humans at most economically valuable work". In fact, it's one of the most meaningful definitions out there.
>If it can't learn for itself, then it's certainly not AGI.
Now this is meaningless.
>The biggest significance of anyone at OpenAI calling it AGI is their contract with Microsoft, giving MSFT access to all their IP, is based around them having achieved AGI, but presumably they can't just declare that unilaterally.
They can't declare it unilaterally, and those deals have changed significantly. Microsoft’s license to OpenAI models and products runs through 2032, and explicitly includes post-AGI models.
> It's not meaningless. In fact, it's one of the more meaningful definitions out there.
Well, it's commercially meaningful, but it's not meaningful it terms of how close we are to achieving human intelligence.
But which one you care about is up to you. If you are all about the money and don't care about human intelligence then for sure go with OpenAI's definition.
It's a lot more than just commercially meaningful. Even if you don't care about money, "highly autonomous systems that outperform humans at most economically valuable work" has a lot more weight on "how close we are to achieving human intelligence" than most of the 'AGI' thresholds I've seen yet.
Traditionally, AGI means being capable of learning everything (not necessarily at once) that can be learned by the same AI agent. It differs from the commercially meaningful definition in that a standard specific-purpose-built AI can still do most tasks if you give it enough specific purposes, but it will be just as incapable of human intelligence as ever.
No - the term "AGI" was really coined to distinguish general intelligence from narrow intelligence(s). There's an obscure earlier usage, but it basically became mainstream as the title of a book edited by Ben Gortzel who had in turn got it from Shane Legg.
Shane Legg would a few years later go on to co-found DeepMind, with creating AGI as their declared mission. Legg's personal definition of AGI is not just generalist AI, but specifically human-level generalist.
Right now we have just massively jagged intelligence that performs stunning feats in math and single-shotting three.js games, and falls flat on it's face in many every day scenarios.
All intelligence is jagged. Human intelligence is jagged, so is Cetacean intelligence, so is Ape intelligence, so is Corvid intelligence. You could re-orient that statement for any species of intelligence relative to the other, or even individuals within a species. The big problem here is people being so convinced of their specialness they'll just keep sticking their heads in the sand, until we essentialy create God, and by then none of this useless pedantry will even matter. If you can't see we've already learnt to walk then there's nothing more to discuss here.
I'm not sure it's accurate to call human intelligence jagged - it seems (perhaps largely thanks to language) that we're able to make progress on anything we set our minds to, and of course we're now in process of building AI that will eventually be able to do things that we ourselves couldn't, whether due to computational demands, or memory capacity, or having sensory inputs that we don't have, etc. But at the end of the day, AI is a tool we've built, so maybe it will make us less jagged, and certainly more capable.
In any case, human intelligence as a goal is special since we live in a human world, and one of the prime goals of AI is to be able to do human jobs. If a human can do something and AI can't then that is a limitation, while if a human can't and AI can't either, that doesn't matter for the time being.
Yeah, I shouldn't have said AI can't walk yet, but most of what it can do is just due to scale - we're still just building big transformers, basically using the same 10 year old architecture that accidentally set us off on this path. I'm not sure we're really progressing towards human/animal type intelligence, just learning how much of our own capability can be realized by automating language.
I'm not sure it's meaningful to compare across different types of intelligence--but I don't think human intelligence is so special that we can pretend it's much less jagged than all other animals for sure. Our scales of models are also not far off biological, probably less efficient but not like astronomically so i would guess.
But anyhow I agree that inference-time learning doesn't exist and it's a big issue still I think.
There seems to be very few cases where ICL is really doing something that can be considered as learning (albeit ephemeral) rather than just utilizing in-context data via induction heads.
Yes, I'm aware of the linear regression example, and a few others, but these appear to just be specific capabilities that were learnt during pre-training, presumably pursuant to reducing errors on similar-but-different training samples, not any kind of generic run-time learning capability.
> The biggest significance of anyone at OpenAI calling it AGI is their contract with Microsoft, giving MSFT access to all their IP, is based around them having achieved AGI, but presumably they can't just declare that unilaterally.
>
>Maybe they see getting out of the Microsoft contract as necessary to IPO.
I don’t have a link handy, but they already loosened up that contract significantly earlier this year, surely because they planned to start claiming AGI to pump their IPO and didn’t want to be beholden to all of those commitments.
>If it can't learn for itself, then it's certainly not AGI.
What does this even mean, exactly? For example if it can filter new information to put into the next version of itself does that qualify? If not, explain exactly why that's the case.
Ask 100 people what their definition of AGI is and you'll probably get 100 different answers.
My definition is closer to DeepMind's "can do any [computer-based] task that a human can do", and certainly a human intern can learn on the job and do better on day #2 than day #1.
Learning is basically THE hallmark of intelligence. Being able to learn from experience and use that to do better in the future.
If you could somehow take everything the LLM learnt today and "put it into" an update for tomorrow that would be better than nothing, but the intern on the job is probably learning dozens of things per day that stack upon each other, so that's going to be a S-L-O-W learning AI intern !
Leaving aside the technical issue how you could store a days learning, or what that would even mean, you've also got the privacy and merge issues if this were to be done in the cloud without every customer having their own personalized model.
For real continual learning we need to get past gradient descent-based batch training and develop a new learning algorithm so the model itself learns incrementally as it runs (animal-like predict, observe, learn cycle), rather than being reliant on some external alien to come in, equipped with it's entire learning curriculum, and program its weights.
OK, let's say we get continuous learning today, what does tomorrow look like?
I won't argue that AI can do some learning as context sizes are still terribly small and expensive to iterate.
What does the world like in a week? A month? A year?
How long before these models drift to their own languages? To their own set of morals? To their own alignment?
None of these questions are answered and I'd rather stay at AGI-lite until they are as having a billion agents going off in their own directions seems like a recipe for disaster.
With current models and their general knowledge self learning just seems like you'd have a few ASIs crop up really quick.
Yeah, I'm fascinated by AGI, but not very enthusiastic about it. I've yet to see anyone, notably not even the people trying to build it, articulate a future where it seems to be a net positive for society.
If it's a choice between everyone living on food stamps or reducing the infinite prime gap to 140, then I guess I'll be happy with a prime gap of whatever it was yesterday.
If/when we do eventually build a more animal-like true human level AGI - build a brain, not just a language model, then potentially we'd be able to build in some of the safeguards that millions of years of co-evolution have built into ourselves, but I expect that is still decades away, and then it'd be capable of doing even more jobs!
The best AI outcome I could wish for is where it is extremely heavily regulated, and AI replacing any human job paying under $1M/yr is banned.
I also think it's moving the goalpost significantly. If you asked me what AGI looks like in 2024, I would have said "smarter on tasks that can be done via text than the average random human you'd meet in a NY bodega", and by that standard, it's long in the rear view mirror.
Now I think people are asking a different question, which is "better than any human at any task that can be done via text", which imo is superhuman, not artificial general intelligence.
It's not moving the goalposts - it's just having a different goal in the first place. This has been Shane Legg and Demis Hassabis' definition of AGI since DeepMind was founded, or before (way before OpenAI appeared), and also for me the goal of AI has always been human level (really a human brain, complete with emotions, etc, but we can start with basic learning/prediction).
I guess I fundamentally disagree, because on general knowledge they beat any human, and on processing speed they beat any human, and on problem solving likewise. I just don't see a domain besides "retain change over time" that they fail on, and that is something you can do in the system rather than the model - coding agents can and do build their own memory system and use it, like an amnestic human writing notes.
I think what I'm objecting to is the idea that if it can't do every task that every human can do better than any human, it's not AGI - I think we have to allow that this is a fundamentally different form of cognition, so requiring a 100% match feels uncharitable. Every human has cognitive gaps that AI doesn't have.
I'm not sure there is anything to disagree on here - just different definitions of what we personally associate the term "AGI" with. There is no magic to the name, and however you choose to define it there will be less and more capable levels of AI that precede and follow it.
Separate from what you choose to call any given set of AI capabilities, learning is generally more than retaining change over time. Learning isn't the same as memorization - it's the difference between memorizing a stack of how-to books and going out and practicing the skill for yourself, and all that entails.
A learnt skill needs to be internalized such that in the future it's what is driving behavior, which means it needs to be in the weights. Imagine a robot trying to learn to play tennis where rather than updating it's connections between perception and action it's trying to store everything as a bunch of notes!
It's not just physical tasks where this applies - the same would be true to trying to teach a multimodal model to recognize different types of mushrooms from photos, or even learning a new language. If a system can truly learn for itself then it shouldn't need to be pre-trained with language, but rather should be able to learn by immersion and practice the same way we do, and the same way a dog learns to follow commands. You can't do this by leaving notes to yourself - the learning mechanism needs to be animal-like and update the systems weights in response to prediction failure - a runtime version of the way SGD-based pre-training updates weights in response to prediction errors.
> If a system can truly learn for itself then it shouldn't need to be pre-trained with language, but rather should be able to learn by immersion and practice the same way we do, and the same way a dog learns to follow commands. You can't do this by leaving notes to yourself - the learning mechanism needs to be animal-like and update the systems weights in response to prediction failure - a runtime version of the way SGD-based pre-training updates weights in response to prediction errors
I am not sure why any of that is necessarily true. I think you are crediting the human brain with a lot more than it actually does - memory is something in the direction of a fine tune on existing neural hardware, it's really not anything special, and I absolutely think that "just taking notes", when taken to a sufficient fidelity, is enough to pass that bar.
You can't take notes before you've learned language, so if that's how you propose to learn then you are doomed to build something with less learning capability than an human brain.
Maybe you don't care - if you just want to push to see how far an LLM can go that's fine. but it's not going to be superhuman-level, or even human-level, if it doesn't have the learning ability of a human. If it relies on language and notes to itself then it is just an LLM.
Okay, nowhere did I say it would be before they learned language. I said we could use models that can trivially build notetaking systems to auto-improve themselves by taking, refining, and updating notes as a memory system to exceed the x token context threshold.
> but it's not going to be superhuman-level, or even human-level, if it doesn't have the learning ability of a human. If it relies on language and notes to itself then it is just an LLM.
Why do you think that? You do a lot of assuming a thing to be true without any clear reasoning or evidence. I'm talking about a system, including a LLM, that acts in a way that is equivalent to or better than an average human at a large range of tasks, such that is generally capable of learning and problem solving similar to the degree of success humans have. All these specific criteria about what is "actually human" is not the point. We know how to make more humans, I'm talking about a form of intelligence that we are creating that is genuinely alien, so holding to "well it doesn't work the same as a human" is an impossible bar. If you reverse the situation, humans are by no means LLM-equivalent intelligences - perfect memory of long form content, rapid calculation, instantaneous tool calling - a hypothetical silicon based lifeform would dismiss us as "really not intelligent at all, they can't even do multiple floating point calculations per second in their head", and I think that would be similarly silly.
OK, so we're basically talking about different things. I'm talking about building something that has animal/human intelligence and learning capability, and would be able to first learn language for itself (no pre-trainiing), and then proceed from there.
You're talking about building an "alien" intelligence, apparently something based on an LLM, where language is baked in from the start and can therefore be used as the basis of some type of learning (or at least memorization).
Sure an LLM can be super-human at specifc things like math where the traditional computer strengths of compute, memory, etc apply, but in the context of AGI (generality, not a bag of narrow intelligences) you can't call something super-human if it can't at least also do the majority of things that a human can do, and not having learning ability as powerful as a human seems to me to be a pretty massive omission.
If WE didn't have ability to learn language, then we'd not be here talking about it, and there would be no LLMs. Having a fundamental ability to learn, isn't just useful to learn language from scratch, or for learning all the non-linguistic skills a human is capable of, but would also support an AI that can learn things that we cannot (e.g. could give it additional "senses" like global pressure/temperature inputs so it could "see" the weather and learn global patterns).
But is the world a better place with that particular understanding being the norm. Would we, as a whole, be better served if function was what actually mattered? Wouldn't that be nice?
Yeah, I'm with Theo on this one. Conventional OS security between Ring-0 and everything else is well understood; the problem has become too much code in Ring-0, a great fraction of which has its own interfaces across the security boundary, and the Unix security model just doesn't scale.
No capabilities, or even a sane and useful way of adding capabilities with everything in ring 0, and the flat integer namespacing of users and groups just doesn't work for what userspace needs to do today - hence namespaces, which have introduced their own problems, because (no surprise) trying to graft a tree structure onto a flat integer namespace after the fact is a mess.
Virtualization tried to sidestep all that, but to make it fast the cost has been more driver interfaces to host ring-0 - remember what the original was? - and screwing around a whole bunch with particularly arcane facets of the core ring-0 security boundary, e.g. page tables.
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