Agreed. If you need to calculate rectangles ML is not the right tool. Now do the comparison for an image identifying program.
If anyone’s looking for the magic dividing line, ML is a very inefficient way to do anything; but, it doesn’t require us to actually solve the problem, just have a bunch of examples. For very hard but commonplace problems this is still revolutionary.
I think the joke is that the Jr. Developer sits there looking at the screen, a picture of a cat appears, and the Jr. Developer types “cat” on the keyboard then presses enter. Boom, AI in action!
The truth behind the joke is that many companies selling “AI” have lots of humans doing tasks like this behind the scene. “AI” is more likely to get VC money though, so it’s “AI”, I promise.
I think it’s still faster than actual solutions in some cases, I’ve seen someone train an ML model to animate a cloak in a way that looks realistic based on an existing physics simulation of it and it cut the processing time down to a fraction
I suppose that’s more because it’s not doing a full physics simulation it’s just parroting the cloak-specific physics it observed but still
I suppose that’s more because it’s not doing a full physics simulation it’s just parroting the cloak-specific physics it observed but still
This. To I’m sure to a sufficiently intelligent observer it would still look wrong. It’s just that we haven’t come up with a way to profitably ignore the unimportant details of the actual physics, relative to our visual perception.
In the same vein, one of the big things I’m waiting on is somebody making a NN pixel shader. Even a modest network can achieve a photorealistic look very easily.
Agreed. If you need to calculate rectangles ML is not the right tool. Now do the comparison for an image identifying program.
If anyone’s looking for the magic dividing line, ML is a very inefficient way to do anything; but, it doesn’t require us to actually solve the problem, just have a bunch of examples. For very hard but commonplace problems this is still revolutionary.
I think the joke is that the Jr. Developer sits there looking at the screen, a picture of a cat appears, and the Jr. Developer types “cat” on the keyboard then presses enter. Boom, AI in action!
The truth behind the joke is that many companies selling “AI” have lots of humans doing tasks like this behind the scene. “AI” is more likely to get VC money though, so it’s “AI”, I promise.
Spot on: https://www.theverge.com/features/23764584/ai-artificial-intelligence-data-notation-labor-scale-surge-remotasks-openai-chatbots?src=longreads
This is also how a lot (maybe most?) of the training data - that is, the examples - are made.
On the plus side, that’s an entry-level white collar job in places like Nigeria where they’re very hard to come by otherwise.
It’s also Blockchain and uses quantum computers somehow. /s
I think it’s still faster than actual solutions in some cases, I’ve seen someone train an ML model to animate a cloak in a way that looks realistic based on an existing physics simulation of it and it cut the processing time down to a fraction
I suppose that’s more because it’s not doing a full physics simulation it’s just parroting the cloak-specific physics it observed but still
This. To I’m sure to a sufficiently intelligent observer it would still look wrong. It’s just that we haven’t come up with a way to profitably ignore the unimportant details of the actual physics, relative to our visual perception.
In the same vein, one of the big things I’m waiting on is somebody making a NN pixel shader. Even a modest network can achieve a photorealistic look very easily.