4 ms·

Damn, how many years of experience do you need to be able to come up with workable solutions as fast as this?

MariuszGalus 9y ago
6 months, here's why: They random forested height data, everyone random forests these days. It's quick and dirty and yields good accuracy. Not only that, everyone seems to support a built in Random Forest training model, programs from pandas/numpy/scikit to R has it built in. Height data seems the easiest to go by, to short and it has a kink that may cause cracking in the future as the metal oxidizes. What were their 3 models? Who knows? But their idea of combining 3 models, that 3 different teams have made is standard in all DataScience competitions. This is called ensembling. ArToolkit for Unreal Engine, someone else made that and they probably just connected a bunch of blocks together with Unreal's script engine. Honestly, 6 months max and you can do this in a day yourself.
- Drdrdrq 9y ago
  Multiple years, here's why: you need to decide what will work and what will not, as you don't have time to go the wrong route. And that takes experience which it would be difficult to gain in 6 months.
- altstar 9y ago
  Yeah right: We used two convolutional neural networks, one for each side of the weld. Features extracted by these nets are then concatenated, as well as the meta data. Two fully connected layers are then applied on this vector, giving the final prediction. Everything was trained on the raw images resized to 20x150 pixels.
onebonsai 9y ago
I am no expert into AI. The AR part we learned because we are busy in VR. It is a natural evolution in that case, as we can use the workflow we are used to in VR. The AI part however is more complex, as it requires understanding of the concepts and methodologies behind it. We used Deep Learning methodologies for this project. This is an upcoming field in AI, which evolves very quickly. If you are interested in it, it's a great time to start learning the basics of it!

6 months, here's why: They random forested height data, everyone random forests these days. It's quick and dirty and yields good accuracy. Not only that, everyone seems to support a built in Random Forest training model, programs from pandas/numpy/scikit to R has it built in. Height data seems the easiest to go by, to short and it has a kink that may cause cracking in the future as the metal oxidizes. What were their 3 models? Who knows? But their idea of combining 3 models, that 3 different teams have made is standard in all DataScience competitions. This is called ensembling. ArToolkit for Unreal Engine, someone else made that and they probably just connected a bunch of blocks together with Unreal's script engine. Honestly, 6 months max and you can do this in a day yourself.

Drdrdrq 9y ago
Multiple years, here's why: you need to decide what will work and what will not, as you don't have time to go the wrong route. And that takes experience which it would be difficult to gain in 6 months.
altstar 9y ago
Yeah right: We used two convolutional neural networks, one for each side of the weld. Features extracted by these nets are then concatenated, as well as the meta data. Two fully connected layers are then applied on this vector, giving the final prediction. Everything was trained on the raw images resized to 20x150 pixels.

onebonsai 9y ago

I am no expert into AI. The AR part we learned because we are busy in VR. It is a natural evolution in that case, as we can use the workflow we are used to in VR. The AI part however is more complex, as it requires understanding of the concepts and methodologies behind it. We used Deep Learning methodologies for this project. This is an upcoming field in AI, which evolves very quickly. If you are interested in it, it's a great time to start learning the basics of it!