This…actually seems like a good use of AI? I generally think AI is being shoehorned into a lot of use cases where it doesn’t belong but this seems like a proper place to use it. It’s serving a specific and defined purpose rather than trying to handle unfiltered customer input or do overly generic tasks,
Eh, I doubt that. Bin packing is a very well-researched problem. It’s one of those nasty NP ones but we already have very good algorithms giving very good approximations in very short amounts of time the chance that throwing machine learning at the problem helps is not zero, but close to it. What that kind of approach certainly won’t get you is guarantees, those approximation algorithms can be configured to spit out solutions that are at most 1% or whatever you want worse than the optimal solution.
I doubt this actually has anything to do with Amazon’s logistics operations it’s just their marketing team wanting to hype up Amazon for AI.
But then again, using literal tera-watts-hours of compute power to save on the easiest actually recyclable material known to man (cardboard), maybe that’s just me, maybe I’m too jaded, but it sounds like a pretty bad overall outcome.
It isn’t a bad deal for Amazon, tho, who is likely to save on costs, that way, since energy is still orders of magnitude cheaper than it should be[1], and cardboard is getting pricier.
if we were to account for the available supply, the demand, and the future (think sooner than later) need for transition towards new energy sources… Some that simply do not have the same potential. ↩︎
So this may be a more efficient use of computing power. Brute force calculation of combinations is costly because there are so many possibilities. A learning model can be fed data from brute force calculations and from humans tasked with packing efficiently to develop an empirical model (this is the AI part) of how to package assorted items. That model could take much less computing power than the Brite force method.
I don’t actually think so. A100 GPUs in server chassis have a 400 or 500W TDP depending on the configuration, and even if I’m assuming 400, with 4 per watercooled 1U chassis, a 47U rack with those would consume about 100kW with power supply efficiency and whatnot.
Running those for a day only would be 2.4GWh.
Now, I’m not assuming Amazon would own 100s of those racks at every DC, but they probably would use at least a couple of such racks to train their model (time is money, right?). And training them for a week with just two of those would be 35GWh, and I can only extrapolate from there.
So I don’t think that going to TWh is such an overstatement.
[…] and understating the amount of cardboard Amazon uses
That, very possibly.
I have seldom used Amazon ever, maybe 5 times tops, and I can only remember two times. Those two times, I ordered a smartphone and a bunch of electronics supplies, and I don’t remember the packaging being excessive. But I know from plentyofmemes that they regularly overdo it. That, coupled with the insane amount of shit people order online… And yes, I believe you are right on that one.
Even so, as long as it is cardboard, or paper, and not plastic and glue, it isn’t a big ecological issue.
However, that makes no difference to Amazon financially, cost is cost, and they only care about that.
But let’s not pretend they are doing a good thing then. It is a cost effective measure for them, that ends up worsening the situation for everyone else, because the tradeoff is good economically, and terrible ecologically.
If they wanted to do a good thing, they could use machine learning to optimise the combining of deliveries in the same area, to save on petrol, and by extension, pollution from their vehicles, but that would actually worsen the customer experience, and end up costing them more than it would save them, so that’s never gonna happen.
This…actually seems like a good use of AI? I generally think AI is being shoehorned into a lot of use cases where it doesn’t belong but this seems like a proper place to use it. It’s serving a specific and defined purpose rather than trying to handle unfiltered customer input or do overly generic tasks,
Eh, I doubt that. Bin packing is a very well-researched problem. It’s one of those nasty NP ones but we already have very good algorithms giving very good approximations in very short amounts of time the chance that throwing machine learning at the problem helps is not zero, but close to it. What that kind of approach certainly won’t get you is guarantees, those approximation algorithms can be configured to spit out solutions that are at most 1% or whatever you want worse than the optimal solution.
I doubt this actually has anything to do with Amazon’s logistics operations it’s just their marketing team wanting to hype up Amazon for AI.
Yeah, it is one of the least bad uses for it.
But then again, using literal tera-watts-hours of compute power to save on the easiest actually recyclable material known to man (cardboard), maybe that’s just me, maybe I’m too jaded, but it sounds like a pretty bad overall outcome.
It isn’t a bad deal for Amazon, tho, who is likely to save on costs, that way, since energy is still orders of magnitude cheaper than it should be[1], and cardboard is getting pricier.
if we were to account for the available supply, the demand, and the future (think sooner than later) need for transition towards new energy sources… Some that simply do not have the same potential. ↩︎
I think you’re overstating the compute power and understating the amount of cardboard Amazon uses
So this may be a more efficient use of computing power. Brute force calculation of combinations is costly because there are so many possibilities. A learning model can be fed data from brute force calculations and from humans tasked with packing efficiently to develop an empirical model (this is the AI part) of how to package assorted items. That model could take much less computing power than the Brite force method.
I don’t actually think so. A100 GPUs in server chassis have a 400 or 500W TDP depending on the configuration, and even if I’m assuming 400, with 4 per watercooled 1U chassis, a 47U rack with those would consume about 100kW with power supply efficiency and whatnot.
Running those for a day only would be 2.4GWh.
Now, I’m not assuming Amazon would own 100s of those racks at every DC, but they probably would use at least a couple of such racks to train their model (time is money, right?). And training them for a week with just two of those would be 35GWh, and I can only extrapolate from there.
So I don’t think that going to TWh is such an overstatement.
That, very possibly.
I have seldom used Amazon ever, maybe 5 times tops, and I can only remember two times. Those two times, I ordered a smartphone and a bunch of electronics supplies, and I don’t remember the packaging being excessive. But I know from plenty of memes that they regularly overdo it. That, coupled with the insane amount of shit people order online… And yes, I believe you are right on that one.
Even so, as long as it is cardboard, or paper, and not plastic and glue, it isn’t a big ecological issue.
However, that makes no difference to Amazon financially, cost is cost, and they only care about that.
But let’s not pretend they are doing a good thing then. It is a cost effective measure for them, that ends up worsening the situation for everyone else, because the tradeoff is good economically, and terrible ecologically.
If they wanted to do a good thing, they could use machine learning to optimise the combining of deliveries in the same area, to save on petrol, and by extension, pollution from their vehicles, but that would actually worsen the customer experience, and end up costing them more than it would save them, so that’s never gonna happen.
They may also save costs on trucking. Smaller boxes => less full truck.
I wonder what would be the benefit of using AI over writing a script that calculates these things.