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We accidentally solved robotics by watching 1M hours of YouTube

ksagar.bearblog.dev

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Re: We accidentally solved robotics by watching 1M hours of YouTube

#12

Post by perching_aix »

mouse_ wrote: post #6
okdood64 wrote: post #2

Does YouTube allow massive scraping like this in their ToS?

Probably not. Who cares at this point? No one is stopping ML sets from being primarily pirated. The current power is effectively dismantling copyright for AI related work.

> The current power is effectively dismantling copyright for AI related work.

Out of the loop apparently, could you elaborate? By "the current power" I take you mean the current US administration?

Re: We accidentally solved robotics by watching 1M hours of YouTube

#16

Post by perching_aix »

ReptileMan wrote: post #11
rzzzt wrote: post #3

Friendly unit conversion man at your service: 114 years.

So a half zoom meeting... or 1/3 Teams one.

I genuinely wish there was a cost estimation feature built into them. Doesn't even have to be even remotely close to the true cost if it's anything like the meetings I attend, there will be enough people and it will go on for long enough to make up for it.

Re: We accidentally solved robotics by watching 1M hours of YouTube

#18

Post by ahmedbaracat »

contingencies wrote: post #8

This is interesting for generalized problems ( "make me a sandwich" ) but not useful for most real world functions ( "perform x within y space at z cost/speed" ). I think the number of people on the humanoid bandwagon trying to implement generalized applications is staggering right now. The physics tells you they will never be as fast as purpose-built devices, nor as small, nor as cheap. That's not to say there's zer…

Well, there’s a middle ground, kinda. Using more specialized hardware (ex: cobots) but deploy state-of-art Physical AI (ML/Computer Vision) on them. We’re building one such startup at ko-br (https://ko-br.com/) :))

Re: We accidentally solved robotics by watching 1M hours of YouTube

#19

Post by jjangkke »

contingencies wrote: post #8

This is interesting for generalized problems ( "make me a sandwich" ) but not useful for most real world functions ( "perform x within y space at z cost/speed" ). I think the number of people on the humanoid bandwagon trying to implement generalized applications is staggering right now. The physics tells you they will never be as fast as purpose-built devices, nor as small, nor as cheap. That's not to say there's zer…

Very good point! This area faces a similar misalignment of goals in that it tries to be a generic fit-all solution that is rampant with today's LLMs.

We made a sandwich but it cost you 10x more than it would a human and slower might slowly become faster and more efficient but by the time you get really good at it, its simply not transferable unless the model is genuinely able to make the leap across into other domains that humans naturally do.

I'm afraid this is where the barrier of general intelligence and human intelligence lies and with enough of these geospatial motor skill database, we might get something that mimics humans very well but still run into problems at the edge, and this last mile problem really is a hinderance to so many domains where we come close but never complete.

I wonder if this will change with some sort of computing changes as well as how we interface with digital systems (without mouse or keyboard), then this might be able to close that 'last mile gap'.

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