The current cost of a Boston Dynamics Spot is around a year's income, give or take whose income you're measuring against.
If it were able to do any human task at the same rate as a human — and yes, I know it isn't, this is just a anchoring point for the discussion — a group of them would be able to extract and process enough resources in a year sufficient to double their population, all the way from rocks in the ground to a finished deliverable.
n years later, there are 2^n robots. Sure, sure, that's a whole 33 years to go from one total to one per human, not the numbers the other person gave which would need a much shorter (but not wildly implausible) reproduction time of 5-8 weeks, but the point is still valid.
That exponential stops only when some un-substitutable resource is fully exploited, so I'm not sure what the upper limit actually is, but given we exist I assume 8 billion robots is also possible.
I'm just finding it funny thinking of 15 Spots queuing for a flixbus / greyhound bus because they need to go get some raw material across the continent.
If that's humans, humans have work[0]. If humans don't have work, the only alternative to robots is, what, well-trained squirrels?
> I'm just finding it funny thinking of 15 Spots queuing for a flixbus / greyhound bus because they need to go get some raw material across the continent.
Me too.
But I expect actual logistics to be much like current logistics, so it would be more like a few Spots loading a standardised intermodal shipping container, a Tesla semi taking it to the port (which is guarded by drones), an automated gantry that puts the container on a cargo ship, a few more robots guarding the cargo ship from pirates (who may well also be robots), the same in reverse at the other end.
[0] You may point to the current conditions of cobalt mines and go "this is bad"; but once was a time when other forms of mining were seen as good solid work, and those with those jobs protested against their mines being closed down. Almost broke the UK when they did that protest, too.
Just explain why AGI will be in our near. Frankly, I don’t see how from what we have now.
Strange response…
> Just explain why AGI will be in our near. Frankly, I don’t see how from what we have now.
…but OK.
The AI we have now, is already capable of learning from what we do. It's very stupid, in the sense that it takes a huge number of examples, but surveillance is trivially cheap so a huge number of examples is actually very easy to get.
As I wrote in 2016:
"""We don’t face problems just from the machines outsmarting us, we face problems if all the people working on automation can between them outpace any significant fraction of the workforce. And there’s a strong business incentive to pay for such automation, because humans are one of the most expensive things businesses have to pay for.""" - https://kitsunesoftware.wordpress.com/2016/04/12/the-singula...
Compute cost is also important: more compute makes better GenAI images, allows larger models in general, turns near-real-time into actual-real-time (many robot demos you see on YouTube have sped-up footage).
Here's something I wrote in 2018 anchored on iPhone compute cost and some random guesstimate I found for what it would take to have an uploaded human brain running in real time, though I can't remember if I've ever compared it with actual compute improvements since then: https://kitsunesoftware.wordpress.com/2018/10/01/pocket-brai...
So, while I don't know what you mean by "near", I'd put "the economy is going to change radically due to AI" somewhere between "imminent" and "20 years" (±3σ, skew normal distribution with a mode somewhere around 2028-2030).
It’d be like me making a prediction that we will have begun to colonize another galaxy in 10 or 20 years time and then only talking about how there’ll be trade between Earth and the colonies and maybe even wars and revolutions. Meanwhile completely skipping over how our spacefaring technology will advanced to the point we can even travel those distances in a reasonable timeframe.
I’m not an expert on LLMs but there doesn’t seem to be very much about them that is even approaching AGI. They’re a useful tool and it’ll definitely disrupt certain sectors of the economy mostly white collar jobs, but we’re in the middle of a peak of inflated expectations. This has happened before with other technologies.
Invert your causality.
The discussion so far was "oh no, oh woe, we shall have no jobs!" — this can only happen if AI is good enough to do all that humans can do. Until that point, we're fine, it's the status quo, and also it doesn't matter how long we stay in this state. I'm not making any strong claim about the start date of the transition (I have a 20 year spread which I think is pretty vague), only the duration of such a transition.
When AI can do that, when, then it's obvious they can do things like "build a robot body", which is obvious because we can, and the definitional requirement of there not being any more work for humans is that the robots can do all the things we can. It's a necessary precondition for the scenario, not a prediction.
> Obviously these robots doing that is predicated on them having AGI
No, it isn't. "AGI" isn't even a well-defined term, each letter of the initialism means a different thing to different people.
And self-replication has much, much lower brain power requirements than full AGI, even for simple definitions of AGI: an AI-and-robot combo with all the intellect of the genome of E. coli is also capable of self-replication. The hard part of self-replication right now isn't the brain power.
So again, invert your causality: the brain power to replace all human workers includes the knowledge of how to self-replicate, but the knowledge of how to self-replicate does not require the brain power to replace all human workers.
> so it’s pointless to talk about what these AGI robots will be doing if we haven’t established that AGI is even possible in the near term or at all.
The specific things an AI needs to do, is learn. That's all. And they already can. The weaknesses of current models still, even if left unresolved, result in AI learning to do each thing humans do eventually when given enough examples, which limits humans to the role of teaching the machines. This is still a form of employment, so it's not economic game-over.
> It’d be like me making a prediction that we will have begun to colonize another galaxy in 10 or 20 years time and then only talking about how there’ll be trade between Earth and the colonies and maybe even wars and revolutions. Meanwhile completely skipping over how our spacefaring technology will advanced to the point we can even travel those distances in a reasonable timeframe.
No. That would require a change of the laws of physics. We don't need a change to the laws of physics for AI, because no matter what definition is used and whether or not current models do or don't meet any given standard, the chemistry in our own bodies definitely demonstrates the existence of human-level intelligence.
> I’m not an expert on LLMs
Are not the only kind of AI. You can't use an LLM for OCR, tagging photos, blurring the background of a video call, driving a car, forecast the weather, or predict protein foldings, and shouldn't use one for route finding or playing chess (although they're surprisingly good at the latter two, all things considered). Other AI do those things very well.
But LLMs will translate between languages as a nice happy accident. And they can read the instructions and use other AI as tools. And, indeed, write those other AI, because one of the things they can translate is English to python.
> but there doesn’t seem to be very much about them that is even approaching AGI.
Then you are one of many whose definition of "approaching" and "AGI" is one I find confusing and alien.
Between all AI, every single measure of what it means to be intelligent that I was given growing up has been met. Can machines remember things? Perfectly. How big is their vocabulary? Every word ever recorded. How many languages do they speak? Basically all of them. Are they good at arithmetic? So good that computers small enough and cheap enough to be given away for free, glued to the front of magazines, beat all humans combined and still would even if everyone was trained to the level of the current world record holder. How well do they play chess? Better than the best humans, by a large margin. Go? Ditto. Can they compose music? Yes, at any level from raw sound pressure levels to sheet music. Can they paint masterpieces? Faster than the human eye's flicker fusion rate. Can they solve Rubik's cubes? In less than the blink of an eye. Can they read and follow instructions, such they can use tools? Yeah, now we have LLMs, they can do that great. Can they make software tools? Again, thanks to LLMs, yes. Do they pass law school exams, or medical exams, can they solve puzzles from the International Mathematical Olympiad? Yup.
We're having to invent new tests in order to keep claiming "oh, no, turns out it's not smart".
> They’re a useful tool and it’ll definitely disrupt certain sectors of the economy mostly white collar jobs, but we’re in the middle of a peak of inflated expectations. This has happened before with other technologies.
LLMs, probably so. I often make the analogy with DOOM, released 30 years back, and the way games journalists kept saying each new 3D engine was "amazing" or "photorealistic", and yet we've only just started to really get that over the last decade. Certainly all the open source models are gushing over each other as "ChatGPT clones" or "ChatGPT killers" in the same way games were "DOOM clones" or whatever the noun was in the cliché "${noun} killers".
And yet the field of AI as a whole, including but not limited to GenAI, has been making rapid progress and doing things which are "decades or centuries away" every few couple of years since I graduated in 2006. Even just the first half of the 2010s was wild, and the rate of change has only gone up since then, this last 18 months has felt like more than that entire decade.
The rest of this is just sci-fi speculation.
If that's all they did, it would be just another change to the nature of work, and humans would simply find other roles to fill.
Rapid change is scary, but can be managed, has been managed before — this kind of thing has historically grown the metaphorical economic pie, making everyone better off. If it's either one of "just muscle power" or "just brain power", the other leaves opportunities for humans.
Only a total replacement of all human work causes such a break that we're fumbling around in the dark. ("Fumbling around" is how I see the discussion of UBI: even if it turns out to be right, we don't yet have anything like a good enough model for the details).
> It seems far more likely than humans will maintain control of the corporations that manufacture the robots.
Will they, though? We've already got people (unwisely IMO) putting AI on boards of directors. Yes, it's as much a stunt as anything else, the law prevents them from being treated as "people", but the effect is the same: https://asia.nikkei.com/Business/Artificial-intelligence-get...
Not per year, total. And it's not really an assumption, just a demonstration of how fast exponential growth is.
> I assume robots will be similar to high end cars in regards to the complexity of manufacturing.
Agreed. This is also the framing Musk uses for Tesla's androids.
> Once a company develops a prototype with AGI
I don't think it needs a complete solution to AGI, as other people use the term. First, all three letters of that initialism mean different things to different people — by my standard, ChatGPT is already this because it's general over the domain of text, and even if you disagree about specific definitions (as almost everyone reading this will), I think this is the right framing, as you'd "only" need something general over the domain of factory work to be a replacement factory worker, or general over mining and tunnels to be a miner, or general over the domain of roads and road users to be a driver.
This isn't to minimise the complexity of those domains, it's just that something as general as ChatGPT has been for text is probably sufficient.
> The first million will be built by humans. The initial robots will take over the manufacturing only later.
Perhaps, perhaps not. The initial number made by humans is highly dependant on the overall (not just sticker-price) cost and capabilities, so a $200k/year TCO robot that can do 80% of human manual labor tasks is very impressive, but likely to be limited to only a few roles, probably won't replace anyone in its own factory; while one which has total costs of $80k/year and can do 90% might well replace most (but not all) of the humans in its own factory; and one costing $20k/year all-in and which can do 95% might well replace all the factory workers but none of the cobalt miners or the truck drivers.
"Fully general" is the end-state, not the transition period. But with fully-general, which is a necessary condition for nobody having any more work, we get a very fast transition from the status quo to having one robot per human.
> Setting up manufacturing that will be able to produce a million units in 2-3 years is possible. Let's say 5 years for a more plausible situation for a million robots to be built.
Agreed on both.
> Also there is no reason to believe it will be 2^n, it can also be 3^n or 1.1^n or any arbitrary number.
It's a definitional requirement of exponential growth, 2^n units after n doubling periods. I anchored on a the doubling period being a year just by reference to the cost of an example existing robot, and using that dollar cost as a proxy for equivalent human labor, and I specifically noted that the other poster's estimate corresponded to a 5-8 week doubling period which didn't seem unreasonable to me. Some robot can do each specific task 4.2 times slower against the wall clock and still be just as fast as a human overall because it's working 24/7 rather than 8/5.
What I want to convey is that the growth function will be somewhat similar to y= c + ax^n (ignoring/collapsing into c the linear and higher order terms) rather than just y= ax^n.
The c here is robots produced via humans. I predict c will easily touch a million in 5 years with or without human help.
Even if the later bots can do only 50% the work of humans, we will still exponentially grow the robots until the humans become a bottleneck. And that 50% capability is also expected to grow exponentially.
Gemini 1.5 pro already beats most humans in most benchmarks, combine it with Sora which has a great visual world model, add some logical reasoning(architecture or scale), memory and embodiment(so it can experiment and test) and you pretty much have the seeds for an agi.
My optimistic/most probable prediction about the growth rate say it's
Regarding the last part:
My bad, I speed read your comment and didn't focus on the exponential calculations. An exponential growth is just x^n. Both x(multiplication rate(?)) and n(units of time) can be manipulated.
> Even if the later bots can do only 50% the work of humans, we will still exponentially grow the robots until the humans become a bottleneck. And that 50% capability is also expected to grow exponentially.
I think most automation since the dawn of the industrial revolution has done 50% or more of the task it was automating, and although yes the impact there is exponential growth, humans are a very rapid bottleneck until the next thing gets automated — Amdahl's law, rather than Moore's.
My optimistic/most probable predictions about the growth rate say it's wild. Like within 5-10 years with the multiple exponentials among different fields you can easily do away with most jobs unless there is a major bottleneck (I don't think there is, there is so much low hanging fruit). I guess that's what the singularity is all about. I don't think this will take multiple decades or centuries in any scenario other than the equivalent of ww3
I get the feeling less "conventional" robots might be needed someday, rather than more.
If you can sell stock, or leverage it for loans, then you get access to future profits as soon as there is a reasonable expectation they will eventually show up.
Amazon's growth model was based entirely on this effect for many years. No need for profits, company's value skyrocketed as anticipation compounded.
Of course, there's a whole bunch of other differences, like the machines don't need to wait 18 years after construction for physical labour, they don't need to be trained for 21 years for mental labour, they don't sleep, and the "working lifespan" target is about 47 years rather than the full 84 years average human life expectancy at birth in developed nations.
Because they're not built to, presumably because manufacturers are greedy and want to sell you a new one, and buying a new one is encouraged by consumerist culture.
Let's say effective human lifespan for physical labor is ~40 years (20 to 60). On average, commercial aircraft last 20-30 years, meanwhile the US military plans to keep B-52s in service at least through 2050s, which will make them roughly 100 on average.
It's not that machines can't last a century with proper care and maintenance, it's that they're not designed to.
Slow down, sure, but not reverse.