3 ms·

Generating 30,000 unique images of artillery pieces hiding in underbrush to train autonomous drone cameras.

junon 2y ago
It's probably not that, but who knows. The real answer is probably way, way more mundane - generating images for marketing, etc.
- TechDebtDevin 2y ago
  well considering an element of their access is the lifting of safety guardrails, I'd assume the scope includes, to some degree, the processing or generation of nsfw/questionable content
  - junon 2y ago
    Perhaps. I still think it's more "we don't need to guard the government from itself" sort of thing.
  - apetresc 2y ago
    The guardrails in question are around generating images of weapons, military installations, etc. Not run-of-the-mill NSFW stuff.
- m4rtink 2y ago
  Never underestimate the military PowerPoint[1] industry! [1] https://media.wired.com/photos/5933e578714b881cb296c6ef/master/w_5066,h_3299,c_limit/atl_wall_chart.jpg https://media.wired.com/photos/5933e578714b881cb296c6ef/mast...
cortesoft 2y ago
If the model can generate the images, can't it already recognize them?
- Falimonda 2y ago
  The model they're training to perform detection/identification out in the field would presumably need to be much smaller and run locally without needing to rely on network connectivity. It makes sense, so long as the openai model produces a training/validation set that's comparable to one that their development team would otherwise need to curate by hand.
Barrin92 2y ago
I don't really understand the logic here. All the actual signal about what artillery in bushes look like is already in the original training data. Synthetic data cannot conjure empirical evidence into existence, it's as likely to produce false images as real ones. Assuming the military has more privileged access to combat footage than a multi-purpose public chatbot I'd expect synthetic data to degrade the accuracy of a drone.
- johndough 2y ago
  Generative models can combine different concepts from the training data. For example, the training data might contain a single image of a new missile launcher at a military parade. The model can then generate an image of that missile launcher hiding in a bush, because it has internalized the general concept of things hiding in bushes, so it can apply it to new objects it has never seen hiding in bushes.
- rovr138 2y ago
  If you're building a system to detect something, usually you need enough variations. You add noise to the images, etc. With this, you could create a dataset that will by definition have that. You should still corroborate the data, but it's a step ahead without having to take 1000 photos and adding enough noise and variations to get to 30k.
- IanCal 2y ago
  I'm not arguing this is the purpose here but data augmentation has been done for ages. It just kind of sucks a lot of the time. You take your images and crop, shift, etc them so that your model doesn't learn "all x are in the middle of the image". For text you might auto replace days of the week with others, there's a lot of work there. Broadly the intent is to keep the key information and generate realistic but irrelevant noise so that you train a model that correctly ignores the noise. You don't want to train your model identifying some class of ship to base it on how choppy the water is, just because that was the simple signal that correlated well. There was a case of radiology results that detected cancer well but actually was detecting rulers in the image because in images with tumors there was often a ruler so the tumor could be sized. (I think it was cancer, broad point applies if it was something else).
gmerc 2y ago
Unreal, Houdini and a bunch of assets do this just fine and provide actually usable depth / infrared / weather / fog / TOD / and other relevant data for training - likely cheaper than using their API See bifrost.ai and their fun videos of training naval drones to avoid whales in an ethical manners
krzat 2y ago
Interesting. Let's say we have those and also 30k real unique images, my guess is that real ones would have more useful information in them, but is this measurable? And how much more?
- wahnfrieden 2y ago
  See IDF’s Gospel AI - the goal isn’t always accuracy, it’s speed of assigning new bombing targets per hour

It's probably not that, but who knows. The real answer is probably way, way more mundane - generating images for marketing, etc.

TechDebtDevin 2y ago
well considering an element of their access is the lifting of safety guardrails, I'd assume the scope includes, to some degree, the processing or generation of nsfw/questionable content
- junon 2y ago
  Perhaps. I still think it's more "we don't need to guard the government from itself" sort of thing.
- apetresc 2y ago
  The guardrails in question are around generating images of weapons, military installations, etc. Not run-of-the-mill NSFW stuff.
m4rtink 2y ago
Never underestimate the military PowerPoint[1] industry! [1] https://media.wired.com/photos/5933e578714b881cb296c6ef/master/w_5066,h_3299,c_limit/atl_wall_chart.jpg https://media.wired.com/photos/5933e578714b881cb296c6ef/mast...

cortesoft 2y ago

If the model can generate the images, can't it already recognize them?

Falimonda 2y ago
The model they're training to perform detection/identification out in the field would presumably need to be much smaller and run locally without needing to rely on network connectivity. It makes sense, so long as the openai model produces a training/validation set that's comparable to one that their development team would otherwise need to curate by hand.

Barrin92 2y ago

I don't really understand the logic here. All the actual signal about what artillery in bushes look like is already in the original training data. Synthetic data cannot conjure empirical evidence into existence, it's as likely to produce false images as real ones. Assuming the military has more privileged access to combat footage than a multi-purpose public chatbot I'd expect synthetic data to degrade the accuracy of a drone.

johndough 2y ago
Generative models can combine different concepts from the training data. For example, the training data might contain a single image of a new missile launcher at a military parade. The model can then generate an image of that missile launcher hiding in a bush, because it has internalized the general concept of things hiding in bushes, so it can apply it to new objects it has never seen hiding in bushes.
rovr138 2y ago
If you're building a system to detect something, usually you need enough variations. You add noise to the images, etc. With this, you could create a dataset that will by definition have that. You should still corroborate the data, but it's a step ahead without having to take 1000 photos and adding enough noise and variations to get to 30k.
IanCal 2y ago
I'm not arguing this is the purpose here but data augmentation has been done for ages. It just kind of sucks a lot of the time. You take your images and crop, shift, etc them so that your model doesn't learn "all x are in the middle of the image". For text you might auto replace days of the week with others, there's a lot of work there. Broadly the intent is to keep the key information and generate realistic but irrelevant noise so that you train a model that correctly ignores the noise. You don't want to train your model identifying some class of ship to base it on how choppy the water is, just because that was the simple signal that correlated well. There was a case of radiology results that detected cancer well but actually was detecting rulers in the image because in images with tumors there was often a ruler so the tumor could be sized. (I think it was cancer, broad point applies if it was something else).

gmerc 2y ago

Unreal, Houdini and a bunch of assets do this just fine and provide actually usable depth / infrared / weather / fog / TOD / and other relevant data for training - likely cheaper than using their API See bifrost.ai and their fun videos of training naval drones to avoid whales in an ethical manners

krzat 2y ago

Interesting. Let's say we have those and also 30k real unique images, my guess is that real ones would have more useful information in them, but is this measurable? And how much more?

wahnfrieden 2y ago
See IDF’s Gospel AI - the goal isn’t always accuracy, it’s speed of assigning new bombing targets per hour