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edit: OK, I re-read this, it does read sort of confrontational. I actually probably agree with like 90% of what you are saying. Probably not really worth it to

by mlsu 4y ago

edit: OK, I re-read this, it does read sort of confrontational. I actually probably agree with like 90% of what you are saying. Probably not really worth it to quibble on made up examples like this. I just want to emphasize that robotics in uncontrolled environments is very very hard. What I'm getting at is, why do the hard robotics work to get a new stream of data when there are many streams that are already computer legible, probably give very similar insights *, and are more compact. The data is not useless, certainly not, but it's actually quite hard to obtain, even with the sensor kit this thing has. If you were to focus your efforts on starving Amazon's insatiable consumer data appetite, the robot stuff they're talking about here should be very low on your priority list. Your broader point definitely stands. (*not precisely the same insights; I certainly see where you are going there!) ----------------------------- I'm being overly specific to illustrate a point. Try doing the shoe rack thing, I mean really try it. Is it really that easy? These shoes don't have barcodes the way things at the warehouse do, and the robots already screw that stuff up in the warehouse all the time. How much money will you make improving shoe rack recommendations by 2.5%? How could you come up with that number, which might justify such a project to Amazon executives, who are deciding what you work on? Why spend so much effort on the shoe thing when your robot is already running into the shoes!? The big thing that I probably should have made clear is that robotics is really hard. Every single separate physical thing your robot runs into is difficult, and they're all different. It would take a lot of $$$ to nail the problem of determining that the thing you ran into is a pair of shoes, or that this area is the front door, or that you even ran into anything at all. Seriously! Now, imagine putting the same number of statistics people, such as yourself, on a team in Amazon Healthcare. Or even just on a team that gets just 200 more people to sign up for Amazon Healthcare. I would be willing to bet that munging the data that comes out of that is simultaneously far easier to work with (no pesky physical reality!) and an order of magnitude more lucrative than any of these robot projects. Amazon has five hundred open positions for data scientists. I assure you, none of them will be working on something like this. Not for a long time.

deleted 4y ago
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godelski 4y ago
> I just want to emphasize that robotics in uncontrolled environments is very very hard. I'm not sure what convinced you I was making this argument. My example was illustrative as a counter to *your example* of determining a specific dog breed. I'm trying to show an easier problem. But "easier" is comparative and doesn't mean the problem is "easy." I'm guessing this is the confusion? Let's be real, figuring out the dog breed by determining dog hair from other debris is substantially harder and I'm pretty sure you'd need a DNA scan. It would be near impossible (probably entirely) to differentiate hair of any type with current sensors. I don't actually think the shoe example is extremely hard considering these robots have lidar on them. Lidar can in fact tell you that something is roughly shoe shaped, so I'm not sure what your gripe is. No need for DNA scanning. No need for cameras or microphones. The problem is likely solvable given *existing instruments on commercially sold products.* This, again, doesn't mean the problem is "easy" though. Just that it can be solved. > How much money will you make improving shoe rack recommendations by 2.5%? Okay, but I'm assuming there's more than exactly one singular problem that the mapping can be solve. So I'm not sure what your point is. Let's try a trivial example. One that *is easy* and will demonstrate that we can sell things beyond shoe racks with said data. Knowing the (approximate) square footage of your house is useful. For example, if you live in a 500sqft place, you can tell a lot about income because we know this is likely a small apartment. You probably can't afford a lot of stuff and Amazon probably shouldn't advertise luxury goods to you, especially large ones. > Now, imagine putting the same number of statistics people, such as yourself, on a team in Amazon Healthcare. I'm not sure why you're pigeon holling me and making this a zero sum game. I'm actively arguing an "and" position, not an "or" position. Yeah, health care is lucrative. But Amazon has an insane amount of wealth, more than they actually know what to do with. They are perfectly capable of being able to hire more than 200 positions for their robotics department, which is what they advertise on their robotics site. They are perfectly capable of increasing this number without taking away any funding from their health care side (not a zero sum game). I'd also assume that there's a significant amount of domain overlap considering that Roombas. Mapping warehouses is a pretty similar problem to mapping houses, so I'm not sure what the problem is.

by mlsu 4y ago

deleted 4y ago

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godelski 4y ago

> I just want to emphasize that robotics in uncontrolled environments is very very hard. I'm not sure what convinced you I was making this argument. My example was illustrative as a counter to *your example* of determining a specific dog breed. I'm trying to show an easier problem. But "easier" is comparative and doesn't mean the problem is "easy." I'm guessing this is the confusion? Let's be real, figuring out the dog breed by determining dog hair from other debris is substantially harder and I'm pretty sure you'd need a DNA scan. It would be near impossible (probably entirely) to differentiate hair of any type with current sensors. I don't actually think the shoe example is extremely hard considering these robots have lidar on them. Lidar can in fact tell you that something is roughly shoe shaped, so I'm not sure what your gripe is. No need for DNA scanning. No need for cameras or microphones. The problem is likely solvable given *existing instruments on commercially sold products.* This, again, doesn't mean the problem is "easy" though. Just that it can be solved. > How much money will you make improving shoe rack recommendations by 2.5%? Okay, but I'm assuming there's more than exactly one singular problem that the mapping can be solve. So I'm not sure what your point is. Let's try a trivial example. One that *is easy* and will demonstrate that we can sell things beyond shoe racks with said data. Knowing the (approximate) square footage of your house is useful. For example, if you live in a 500sqft place, you can tell a lot about income because we know this is likely a small apartment. You probably can't afford a lot of stuff and Amazon probably shouldn't advertise luxury goods to you, especially large ones. > Now, imagine putting the same number of statistics people, such as yourself, on a team in Amazon Healthcare. I'm not sure why you're pigeon holling me and making this a zero sum game. I'm actively arguing an "and" position, not an "or" position. Yeah, health care is lucrative. But Amazon has an insane amount of wealth, more than they actually know what to do with. They are perfectly capable of being able to hire more than 200 positions for their robotics department, which is what they advertise on their robotics site. They are perfectly capable of increasing this number without taking away any funding from their health care side (not a zero sum game). I'd also assume that there's a significant amount of domain overlap considering that Roombas. Mapping warehouses is a pretty similar problem to mapping houses, so I'm not sure what the problem is.