But how is an individual supposed to "prepare" for AGI?
The outcome of further automation will be to move even more capital under the control of an even smaller number of hands. It's that increasing inequality which is the problem, not AGI.
The outcome of further automation will be to move even more capital under the control of an even smaller number of hands. It's that increasing inequality which is the problem, not AGI.
1) Mental preparation - understanding your current career is finite (be it 5 more years or 20, its not gonna last forever) and broaden and deepen other things in your life such as family, friends and hobbies. I've contemplated the loss of my career hundreds of times already if not thousands, I do it almost on a daily basis (not for hours, just a few moments of a passing thought). It's kind of the opposite of denial and it seems helpful for me to do it.
2) Financials - an obvious one. We should all accept that quite possibly our standard of living will be lower 10 years from now. It's not a certainty, but the likelihood is high enough that we need to be more frugal - which means both trying to save money as much as we can and also learning to enjoy things that don't cost a lot of money.
3) Possible career pivot - there will be opportunities. Some parts of the labour market are starving for employees and I don't mean just the trades - there's a constant need for nurses, teachers, care etc. The problem is these jobs are notorious for burning their workers out; I don't have a solution for that.
These jobs are also not scalable and not directly profitable, they require money from other sources to even exist at a large scale. In this potential AI-dominated world where there is less money being earned by individuals, consequently less taxes being paid, who exactly is going to be paying for nurses, teachers, care, etc.?
It's not in the current sociopolitical/economical zeitgeist for governments to step in and create jobs, since the 80s we've moved towards privatisation, even very socially oriented governments like Western/Northern Europe are in it. To undo this flow into the other direction will take another generation or two of people under a new ideology voting for it.
We've been fed neoliberal policies for way too long, the contemporary Western world has been molded in it, I don't believe such a huge shift will happen fast enough if AI does actually develop that fast to replace jobs. We will live through a limbo of pain until newer generations fight against it, usually that only happens when our collective pain is way above the uncomfortable threshold.
> Even now at least half of economic activity is government created in many developed countries
I wasn't aware of this fact, where could I find statistics about it (if you have sources easily, if not I can do my own research).
Look at countries like France, quite crazy.
I assume we're currently seeing a lot of smoke and mirrors on the AI controlling those robots, but the mere physical form factor isn't enough any more.
Nurses may have longer than plumbers because many of us will want the human connection, and servos behind rubber masks are currently deeply in uncanny valley — at least, they are for me, but I've noticed from the breadth of responses to GenAI that uncanny valley is in a different place for different people.
Robots that not only have AGI (running locally, under battery power?!) but also the dexterity, flexibility, etc to match that of a plumber seem a long, long way off, and would of course require something a lot more advanced that today's LLM based AI - would need to have full blown brain-like capabilities, obviously including the mechanisms to learn a craft.
They've improved a lot since the ones decades ago. When ASIMO was state of the art, that meant being able to walk on a flat surface and climb stairs.
Now, it's more like this: https://www.youtube.com/watch?v=WlUFoZstcWg
(Though again, as this is a sales video, I expect it to be implying more than it can actually do, in the same vein as the Tesla Optimus robot bartenders but not necessarily the same specifics)
> Robots that not only have AGI (running locally, under battery power?!)
Assuming batteries are a must and we can't e.g. use mains, that means the gap between AI reaching the power-performance envelope needed for level 5 self driving cars and the one needed for such robots would be about 5 years. Get the former in x years from now, the latter in x+5 years.
> but also the dexterity, flexibility, etc to match that of a plumber seem a long, long way off
Single humanoid hand, solving a Rubik's Cube, real time video from 5 years ago: https://www.youtube.com/watch?v=kVmp0uGtShk
> and would of course require something a lot more advanced that today's LLM based AI
The future existence of a sufficiently more advanced AI is baked into the assumptions in the question at the top of this thread. It's the goal and driving purpose of the field.
Dunno when it comes; if I did, that would make it easier to prepare for.
> would need to have full blown brain-like capabilities, obviously including the mechanisms to learn a craft.
How confident are you about what specifically they need and don't have, and why?
I've seen people confidently say AI would need brain-like capabilities to play Chess, then when Deep Blue beat Kasparov they were saying it about Go, then when AlphaGo beat Lee Sedol they were saying it about natural language and creating pictures, then ChatGPT happened and various Transformer and Diffusion models got so good that collectively they've become a fraud risk.
Based on the observation that LLMs could get up to the level of interns or mid-university students, I suspect we've already got AI capable of learning a craft to that (mediocre) level just by training on all the YouTube DIY videos.
Based on all the sim2real work, I think it's at least plausible (not certain, only plausible) that some mediocre plumber AI bootstrapped by YouTube would also be able to improve substantially in this fashion: https://arxiv.org/search/?query=sim2real&searchtype=all&sour...
I don't think we need to solve, e.g., the question of why humans seem to be able to learn from so much less data than our AI require to reach any given skill level. Likewise, we do we don't need to agree on what "consciousness" is, nor do we need to then give that to the machines.
If we're relying on pre-training for skill/knowledge acquisition, then the training set would need to include robot-POV training data for every task and scenario is was expected to succeed at, which seems essentially impossible even given a potential world simulator in which to train them.
Without continual learning, or being pre-trained for every task, past or future, it'd be perpetual groundhog day where you coach your robo-plumber to do a task one day, and have to coach it again every time the same task comes up. Of course most consumers aren't expert plumbers, so there is really no alternative than have robo-plumber come pre-trained for all eventualities or be able to learn on the job the same way an inexperienced plumber would do.
There are many other things needed to replicate animal intelligence other than continual learning (e.g. traits like curiosity & boredom, in order to drive an autonomous system to experiment and learn), but continuous learning is a big one. We've been stuck on "whole dataset SGD-train, then deploy" since the advent of neural networks, despite many smart folk like Hinton trying to find something better.
As far as things like flexibility, the bar is pretty high. The robo-plumber needs to be able to lie on it's back in a pool or spray of water while contorting itself in the cabinet under your kitchen sink to fix a leak when the water couldn't be shut off 100% ... the real world is infinitely more messy and challenging than any simulation is going to be, and the simulation isn't going to prepare the robot for the wet/greasy/slippery/etc physical environment in which it'd be working.
Never mind figuring how to build human level AGI (with learning, etc), having it operate in real time and be battery powered is a massive challenge. A car at least has the battery being charged continuously by the engine/alternator. Real-time response by a multi-modal LLM currently requires multiple H100's or similar - probably a few kilo-watts. There's no reason to suppose that even in theory it's possible to build a compact battery (or super-capacitor) technology capable of delivering that sort of power output for an 8-hour shift. There's more hope that future cognitive architectures, and realizations (dataflow vs synchronous?) might reduce power needed to what's available from a battery, but that'd be many decades away.