Like a number of Google projects, this one lives on without any clear direction. It probably will get axed some day, but the technology in Teachable Machine today is so “old school” already that I don’t think it would be that hard for someone to recreate or improve upon.
I think this is more like fine tuning an existing model to recognize features you specifically intend it to, and be light enough to run locally in a browser.
It's not even fine tuning, it's creating a model from scratch. This isn't like our modern huge models either, these tiny single-purpose models have been around for ages and are quite versatile. They're so small you can't just easily run them in the browser, but train them effectively, which is what this project lets you play around with! Super cool stuff.
The teaching part is what matters, it’s training (tuning in this case) a model, not just using a model already trained for inference (which is what I assume you mean). You’re providing new data that is used to update the model. Inference across an existing multimodal model doesn’t change how it classifies in any way.
v1 used a very limited (albeit very easy and already quite impressive) form of transfer learning, e.g. take a pretrained network's 1000dim vector outputs given a bunch of images belonging to three sets (since the original was trained on Imagenet), and then just use K-NN to predict what a set "new" image falls into.
v2 does actually finetune weights of a pretrained network. At the time, it was a nice showcase how fast fast JS ML libraries were evolving.
I've done my share of research on MediaPipe[1], but had never heard of Teachable Machine. I'm curious if these efforts are related, as these products looks like they were almost intended to be used together.
I am definitely excited to see that Google is investing into more "ML at the edge" use cases, especially in the browser. If you've never heard of MediaPipe before, but this caught your eye, definitely check it out. It has seen large uptake in the VTubing community especially as it has a very performant implementation of body + face + hand pose tracking driven by BlazePose.
31 comments
[ 3.2 ms ] story [ 73.1 ms ] threadIts last real update (AFAIK) was in 2019: https://www.theverge.com/2019/11/7/20953095/google-ai-traine...
Like a number of Google projects, this one lives on without any clear direction. It probably will get axed some day, but the technology in Teachable Machine today is so “old school” already that I don’t think it would be that hard for someone to recreate or improve upon.
What’s different here?
Even the smallest multimodal LLM would be wayyyyyyy bigger than an exported model from this
https://www.tensorflow.org/lite
TF Lite has first-class Android support with hardware acceleration if I'm not mistaken.
I considered the older version but it's very limited:
> The original Teachable Machine only let you train 3 classes, whereas now you can add as many classes as you like.
I'm curious to see how far this scales, for example can I have a few hundred thousand classes? If so, what are the consequences, if any?
v2 does actually finetune weights of a pretrained network. At the time, it was a nice showcase how fast fast JS ML libraries were evolving.
It runs locally in your browser, without sending your training data to any servers.
Unless you choose to save it to Google Drive.
If you choose to host the model with Google, they get a copy of your weights, but they still don't see your training data.
Or you can host it yourself with tensorflow.js
And you can also download everything in a zip file, training data and weights, and Google never sees any of it.
If you want the source, it's here -> https://github.com/googlecreativelab/teachablemachine-commun...
https://github.com/googlecreativelab/teachable-machine-v1
and in 2019, Google released v2 https://blog.google/technology/ai/teachable-machine/
The tasks are limited, good for kick starter. I think the platform is not fastly developed (?)
Teachable Machine: Teach a machine using your camera, live in the browser - https://news.ycombinator.com/item?id=15399132 - Oct 2017 (90 comments)
https://fairpixels.pro/work1/index.html
I am definitely excited to see that Google is investing into more "ML at the edge" use cases, especially in the browser. If you've never heard of MediaPipe before, but this caught your eye, definitely check it out. It has seen large uptake in the VTubing community especially as it has a very performant implementation of body + face + hand pose tracking driven by BlazePose.
1: https://developers.google.com/mediapipe