We do not send any webcam / audio data back to a server, all of the computation is totally client side. The storage API requests are just downloading weights of a pretrained model.
We're thinking about releasing a blog post explaining the technical details of this project, would people be interested?
There's something fantastically entertaining about this. It's stupidly simple (from the outside) but interacting with the computer in such a different way is weirdly fun.
It's like when you turn on a camera and people can see themselves on a TV. A lot of people can't help but make faces at it.
Why does it not work in Edge? Please keep the web open, do not make stuff that does not work in a modern browser. Also always give an option to try it anyway.
Pretty neat! Good overview without overwhelming right off the bat. Would be cool if they showed off common pitfalls like over fitting, or even segued into general statistics!
How long before I can teach my computer gestures that are mapped to real computer functions? For example, scroll up/down, switch apps, save document, cut/copy/paste, etc.
One could probably map each gesture to a regular USB device that acts as a second keyboard and mouse? The hard part is identifying enough unique gestures?
It's working great because they're using a state of the art model (SqueezeNet https://github.com/DeepScale/SqueezeNet) and also the samples / experiments you do are often only on yourself, in the same lighting, same clothes, etc. So it gives a nice idealized playground environment that mostly eliminates annoying details like this.
there are 3 default classes, so you train according to each class(e.g. hand waving, sitting still, etc) you take examples of each(using your camera). you map the input data from your camera to some output data(e.g. if i used the green button to take photos of me waving), display a GIF of a cat that's waving. instead of a GIF you can use sound too
The value-add for this demo is amazing, it's going to be many people's first approachable experience of ml, or things just like this will be. I expect a lot more of this stuff to appear in UI/UX. It's fun, intuitive, and a game changer away from dumb screens to fully interactive machines with their own knowledge graph.
To use azure which places a too high bar on students. I mean I've tried to argue for graduated restrictions so basically students with .edu emails should be able to do some things without entering a credit card number but the fact that it is not possible suggests this isn't a priority for azure.
Google says this finds on your browser so there's little infrastructure cost for this demo, right?
Can you clarify on what you did with it? I'd love to start dabbling in solving problems with ML, but am a bit intimidated by getting started. Is it fairly easy for a novice to do the things you did?
Does anyone know what this uses under the hood? I loved the demo, but I would like a similarly easy way to get started locally with Python, for example.
Is there an ML library that can easily start capturing images from the webcam so you can play around with training a model?
be aware, at least in Chrome, once you give teachablemachine.withgoogle.com permission to use you camera, unless you revoke that permission is has permission to use your camera without further permission including from iframes. In other words every ad from and analytics from Google could start injecting camera access.
I wish chrome would give the option to only give permission "this time" and I wish it didn't allow camera access from cross domain iframes.
Are you serious? Do you realize that Chrome is also written by Google and they could theoretically already run arbitrary code on your computer? The potential reputation damage and legal risk for Google would be way too high pull off something like that.
If this happened, the Google Chrome tab would show a camera. Many webcams have adjacent LEDs that identify that they are activated.
Google could theoretically release compromised versions of Google Chrome and only use the permission on devices where webcam LEDs are unlikely (e.g. smartphones), but this is going deep into tin-foil-hat territory.
It works on mobile, it's just slow. Every time we read and write from memory we have to pack and unpack 32 bit floats as 4 bytes without bit shifting operators >.>
Hmm. I wonder if one could train this with dick pics and embed into popular messenger apps client-side... "this picture was classified as a penis", to counter morons sending their dick as first message.
Claims like these make privacy-focused efforts less valuable, and I wish people wouldn't make them.
What value is there in taking care to store biometric data only locally, in a separate chip inaccessible even to the OS, if people will simply claim it's equivalent to keeping a remote database of millions of faces?
Facebook beat them to it... that's the whole reason for tagged images imo. Then they can relate identities with each other and with exif gps data to track their movements over time.
I am pretty sure that Apple does not save your image data in any database. Apple is really trying to differentiate itself on privacy.
Also, I don't think that this sends any data to Google, since it trains the neural net in the browser. You could even verify this yourself by looking at the source code.
[+] [-] nsthorat|8 years ago|reply
We do not send any webcam / audio data back to a server, all of the computation is totally client side. The storage API requests are just downloading weights of a pretrained model.
We're thinking about releasing a blog post explaining the technical details of this project, would people be interested?
[+] [-] amelius|8 years ago|reply
And some quick questions:
What network topology do you use, and on what model is it based (e.g. "inception")?
What kind of data have you used to pretrain the model?
[+] [-] Splines|8 years ago|reply
It's like when you turn on a camera and people can see themselves on a TV. A lot of people can't help but make faces at it.
[+] [-] sydd|8 years ago|reply
[+] [-] haser_au|8 years ago|reply
[+] [-] godelmachine|8 years ago|reply
[+] [-] celim307|8 years ago|reply
[+] [-] melling|8 years ago|reply
One could probably map each gesture to a regular USB device that acts as a second keyboard and mouse? The hard part is identifying enough unique gestures?
[+] [-] amelius|8 years ago|reply
[+] [-] wildebaard|8 years ago|reply
[+] [-] amelius|8 years ago|reply
[+] [-] IanCal|8 years ago|reply
It's a really well put together demo & tutorial.
I held a pen up next to me and held the green button.
Then did the same with a mouse.
It would flick between the two if I was holding nothing, so I held the orange button for a bit while holding nothing.
Worked pretty much every time.
Training is fast enough with a few hundred images per class that I didn't notice any delay.
[+] [-] makmanalp|8 years ago|reply
[+] [-] gfredtech|8 years ago|reply
[+] [-] crypticlizard|8 years ago|reply
[+] [-] lelima|8 years ago|reply
I've solve regression, classification and recommendation problems with it and the best part is it deploys an web service with a few clicks.
[+] [-] thanksgiving|8 years ago|reply
1 a working phone
2 a valid credit card
To use azure which places a too high bar on students. I mean I've tried to argue for graduated restrictions so basically students with .edu emails should be able to do some things without entering a credit card number but the fact that it is not possible suggests this isn't a priority for azure.
Google says this finds on your browser so there's little infrastructure cost for this demo, right?
[+] [-] shostack|8 years ago|reply
[+] [-] StavrosK|8 years ago|reply
Is there an ML library that can easily start capturing images from the webcam so you can play around with training a model?
[+] [-] make3|8 years ago|reply
and you could do worse then this https://github.com/fchollet/keras/blob/master/examples/mnist...
[+] [-] icc97|8 years ago|reply
[+] [-] hnarayanan|8 years ago|reply
(It is not a Python application but a Java application, but still as fun!)
[+] [-] greggman|8 years ago|reply
I wish chrome would give the option to only give permission "this time" and I wish it didn't allow camera access from cross domain iframes.
[+] [-] ma2rten|8 years ago|reply
[+] [-] jamesmishra|8 years ago|reply
Google could theoretically release compromised versions of Google Chrome and only use the permission on devices where webcam LEDs are unlikely (e.g. smartphones), but this is going deep into tin-foil-hat territory.
[+] [-] azinman2|8 years ago|reply
[+] [-] haser_au|8 years ago|reply
[+] [-] addedlovely|8 years ago|reply
[+] [-] netcraft|8 years ago|reply
[+] [-] nsthorat|8 years ago|reply
[+] [-] f00_|8 years ago|reply
if you like this I would highly recommend looking at openframeworks.
the interactive browser part excites me want to try to make something with deeplearn.js
[+] [-] mschuster91|8 years ago|reply
[+] [-] peepopeep|8 years ago|reply
[+] [-] moduspol|8 years ago|reply
What value is there in taking care to store biometric data only locally, in a separate chip inaccessible even to the OS, if people will simply claim it's equivalent to keeping a remote database of millions of faces?
[+] [-] xyrnoble|8 years ago|reply
[+] [-] ma2rten|8 years ago|reply
Also, I don't think that this sends any data to Google, since it trains the neural net in the browser. You could even verify this yourself by looking at the source code.
[+] [-] glass_of_water|8 years ago|reply
[+] [-] jamesmishra|8 years ago|reply
- Gmail / Google Plus / Google Apps profile pictures
- Google Street View
- Google Hangouts
- implementing a primitive Face ID or Snapchat-style camera on Google Android
- the large mass of face pictures that they index with Google Images
[+] [-] danso|8 years ago|reply
[+] [-] icc97|8 years ago|reply
Also I think a lot of the processing is done in the browser using deeplearning.js, so I don't know how much is sent back to Google.
[+] [-] 4684499|8 years ago|reply
[+] [-] fancyfacebook|8 years ago|reply
[+] [-] irascible|8 years ago|reply
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[+] [-] eggie5|8 years ago|reply
[+] [-] nsthorat|8 years ago|reply
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[+] [-] irascible|8 years ago|reply
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