[HN Gopher] How to build a working AI only using synthetic data ...
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How to build a working AI only using synthetic data in just 5
minutes
Author : jetbiscuits
Score : 69 points
Date : 2022-06-13 11:43 UTC (11 hours ago)
(HTM) web link (www.danrose.ai)
(TXT) w3m dump (www.danrose.ai)
| m3kw9 wrote:
| Training images today is equivalent of launching a web server
| using one line of code
| UncleOxidant wrote:
| I've worked in the AI space, but mostly on the backend tweaking
| algorithms so I guess I'm asking this as (mostly) a laymen: Isn't
| using AI to generate training data to train another AI fraught
| with peril?
| mactournier wrote:
| Don't waste your time. There are so many statistical atrocities
| in this article that it makes me shiver.
| kadoban wrote:
| Where? I don't see any statistics in the article.
| mdp2021 wrote:
| Probably meant that using synthetic data is recycling within
| a closed system.
| kadoban wrote:
| I guess that's possible, but it's not clear to me that that
| matters. It'll still work right? What do we care?
| Imnimo wrote:
| >In a short while, it has gone from being an experimental
| technology. To something, I would hesitate to use for production
| AI solutions.
|
| What?
| Liveanimalcams wrote:
| Have you seen Roboflow Universe? They have thousands of projects
| from their community already labeled for download and use. I
| always start there when I want t to start a new model. Recently
| for my trash picking up robot I found a good litter dataset to
| start from. https://universe.roboflow.com/
| YeGoblynQueenne wrote:
| Title: "How to build a working AI (...)".
|
| Text: How to build an apple-or-banana image classifier.
|
| Ah. I see. It's an allegory for the state of modern machine
| learning research.
| drcode wrote:
| Interesting post, but it seems fragile enough that I'm not sure
| it could work if you try to classify a yellow apple.
| mudrockbestgirl wrote:
| I think a more appropriate title would be: How to click an upload
| data button on a website.
| [deleted]
| axpy906 wrote:
| And... I am still waiting with baited breath for something like
| this that would work for tabular data.
| master_yoda_1 wrote:
| First define what is a "working AI" even mean Be careful AI is
| not an iPhone app you will not find any funding for BS in this
| env.
| towaway15463 wrote:
| I'm curious why you would need to generate the images using
| another AI. There are masses of free high quality 3d models out
| there and even if you can't find the model you need you could
| always use photogrammetry to create one from a real world
| example. After you have the subject you can render it in a game
| engine like Unreal Engine from as many different perspectives and
| lighting conditions as you want. Since the engine is programmable
| you could even automate this part to a large degree and adding
| other confounding objects or backgrounds would be simple as well.
| ollysb wrote:
| What's really interesting with 3d models is that you could give
| the classifier motor control within it's environment i.e. it
| could "step to the left" and see how the banana changes in it's
| visual field. This would allow the classifier to integrate it's
| movement with the visual stimulus and build a far richer model.
| It also gives the classifier access to an active learning
| strategy: predict how the appearance of the banana changes when
| it "steps to the left", try it, then evaluate the difference
| and refine.
| tehsauce wrote:
| It should not take you 5 minutes to make an image classifier in
| 2022. 30 seconds is a more reasonable amount of time. Dalle is
| trained using CLIP which you can just use as a zero shot
| classifier directly, no need to waste time generating images or
| training a model at all. Just type in the names or descriptions
| of your classes and your done! Way easier than this :)
| minimaxir wrote:
| Normally, this type of comment is Hacker News reductiveness,
| but yes, image classification via CLIP is that easy, especially
| with Hugging Face's API for it:
| https://huggingface.co/docs/transformers/model_doc/clip
|
| I created a Python package to generate image embeddings from
| CLIP's vision model without requiring a ML framework
| (https://github.com/minimaxir/imgbeddings ), and a simple
| linear classifier on those embeddings does the trick, demo
| here:
| https://github.com/minimaxir/imgbeddings/blob/main/examples/...
| version_five wrote:
| How big of a model is CLIP? If you're building a phone app that
| classifies dogs, you may not want to require that it runs some
| multi-billion parameter monstrosity to perform its
| comparatively simple task. There is lots of value in building a
| compact model. "Just" typing in the names ignores the compute
| you need to have behind the scenes.
| tehsauce wrote:
| There are various sizes of CLIP, many are not enormous. For
| example, one of the base models is just a standard resnet50.
| So very usable on a mobile device.
| minimaxir wrote:
| A compact model is a constraint that changes the problem
| entirely and doesn't discredit the quick-but-effective
| approach that works for nearly every other use case.
| genewitch wrote:
| Are you saying that DALL-E is impressive? On fediverse it's
| used for jokes and memes, because it's really, uh, ugly?
| simplistic? using obvious components in each image. To my eye,
| it looks like trickery. Maybe the "full, paid, commercial"
| model and outputs are better; i'm not sure.
|
| I'm actually looking for a decent classifier / object
| recognition platform to sort on the order of millions of images
| coarsely - as it stands all of the ones i've tried can't
| determine if an image is drawn/painted or a photograph, for
| instance, which reduces my enthusiasm of the whole field.
|
| On the other hand, audio AI/ML stuff - such as spleeter -
| impresses me, as i can't do that stuff by hand.
| ShamelessC wrote:
| The original DALL-E was never released. This is a smaller
| model made by volunteers.
|
| Did you consider using CLIP like parent comment said?
| lumost wrote:
| I've heard such claims for a long time, I can likewise create a
| classifier out of a simple dice role. It doesn't say anything
| about how good it is.
|
| Most software applications have low tolerance for error rates,
| the ones that do have big money being spent on ensuring their
| accuracy is better than everybody else's.
|
| So while you can make a classifier out of anything in Y time,
| that doesn't say anything about whether it's of any practical
| use.
| minimaxir wrote:
| No, CLIP is indeed that good. The robustness of its
| embeddings is the entire reason why VQGAN+CLIP works and can
| stablely generate images close to the text prompt.
| lumost wrote:
| I'm sure it's better than a dice role, but does it beat a
| modern classifier trained on the domain specific data?
|
| EDIT: I raise this issue as over-promises are the death
| nell for software. Overpromising capability leads to
| disappointment.
| ShamelessC wrote:
| It tends to, yes. I suggest reading the paper as they
| discuss this very thing in detail.
| minimaxir wrote:
| See the zero-shot performance of CLIP:
| https://openai.com/blog/clip/
|
| It's definitely better performance than what you'd get
| working in 5 minutes from more conventional approaches.
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