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AI tools for a quick trial

The following resources were used in Italy to quickly discover and test AI tools during webinars in the experimentation phase.

Teachable Machine

  • What: "Teachable Machine is a web-based tool available to anyone, which allows to create machine learning models quickly and easily."
  • Produced by: Google Creative lab
  • Access to the resource: https://teachablemachine.withgoogle.com

QuickDraw

  • What: "This game is based on machine learning. You draw, and a neural network tries to guess what your drawing represents. Of course, it doesn't always succeed, but the more you play, the better the network gets. So far we have taught it a few hundred concepts, and we hope to add more over time. This game shows that machine learning can be fun".
  • Produced by: Google Creative lab
  • Access to the resource: https://quickdraw.withgoogle.com

AutoDraw

Sketch_RNN

  • What: "An interactive web experiment that lets you draw together with a recurrent neural network model called sketch-rnn. We taught this neural net to draw by training it on millions of doodles collected from the Quick, Draw! game. Once you start drawing an object, sketch-rnn will come up with many possible ways to continue drawing this object based on where you left off."
  • Produced by: Google
  • Try the first demo

This person does not exist

  • What: "Random Face Generator - Generate random human face in 1 click and download it! AI generated fake person photos: man, woman or child". "The AI face generator is powered by StyleGAN, a neural network from Nvidia developed in 2018."
  • Produced by: This Person Does Not Exist
  • Access to the resource: https://this-person-does-not-exist.com/en

Let's play paper, scissors, stone

  • What: "Play Paper; scissor and stone against your computer". "The demo is built on (a)GPU-accelerated TensorFire library for fully in-browser deep learning. It's fast enough to perform real-time client-side classification of live webcam video, and we're showing it off here with a cute little game."
  • Produced by: Tensor Fire - "A group of recent MIT graduates who all think this whole "deep learning" thing is pretty neat".
  • Access to the resource: https://tenso.rs/demos/rock-paper-scissors

Beat the Crocodile