Natasha Jaques - Learning via Social Awareness - Creative AI meetup
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This talk by Natasha Jaques from MIT Media Lab on "Learning via Social Awareness: Improving a deep generative sketching model with facial feedback" was presented on 10th September 2018 at IDEA London as part of the Creative AI meetup.
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Natasha Jaques - Learning via Social Awareness - Creative AI meetup
- 1. Learning via Social Awareness Improving a deep generative sketching model with facial feedback Natasha Jaques, Jennifer McCleary, Jesse Engel, David Ha, Fred Bertsch, Douglas Eck, Rosalind Picard
- 2. Deep learning works… …in limited domains
- 3. Intrinsic motivation for deep learning
- 4. Intrinsic motivation for deep learning ● Curiosity (Pathak et al. 2017) ● Empowerment (Capdepuy et al., 2007) ● …?
- 5. What are other ways people learn?
- 6. Humans learn through implicit social feedback ● Emotion recognition important to cognitive development (Kujawa et al., 2014) ● Social learning theory (Bandura & Walters, 1977) ● Social learning -> cultural evolution (van Schaik & Burkart, 2011)
- 7. Making an AI agent socially aware will make it smarter Alexa, what’s the right way to Walmart? The right way to spell Walmart is W-A-L-M-A-R-T Ugh... Better not do that again...
- 8. ML systems should learn what people want
- 9. Optimizing for what people want – AI safety
- 11. ● Quality is subjective, no good objective metrics ● does not describe better art ● does not get you a more creative story or a more beautiful song
- 12. When generating creative content designed to appeal to human aesthetic preferences, human feedback is necessary
- 13. Providing feedback is cumbersome, doesn’t scale
- 14. Social feedback is rich, ubiquitous, natural
- 15. What is currently one of the best ways to detect social feedback?
- 16. Project idea Generate samples from a deep learning model, show them to users Detect user’s facial expression response Improve the model using social feedback
- 17. What would generate a facial expression response? Music / melodies Style transfer SketchesGAN images Text (dialog / poems / stories) Youtube recommendations Magenta models exist ???? MemesJokes
- 18. Sketch RNN
- 21. Collecting data
- 26. UX research - the good news Average contentment; r=.58, p < .001 Average amusement; r=.54, p < .002 Average concentration; r=-.58, p < .001 Max concentration; r=-.40, p < .05 *Must normalize within each user first* PerceivedqualityPerceivedquality PerceivedqualityPerceivedquality
- 27. The bad news ● High variance between users ○ Resting “concentration” face ● Extremely noisy ● Some people do not emote
- 28. The bad news ● Users don’t just smile at good sketches ● Significant correlations between the # sketches viewed and emotions o Sadness goes up: r(751) = .248, p < .001 o Concentration goes down: r(751) = -.158, p < .001
- 29. Learning from facial feedback
- 31. GAN Generative Adversarial Network + VAE Variational Autoencoder
- 32. What was a GAN again?
- 33. What was a VAE again? KL( encoder(z | X) || N(0,I) ) z ~ N(0,I)
- 34. Sketch RNN is already a VAE!
- 36. D G Z ~ N(0,I) Latent constraints model Step 1: Collect data Sketch RNN VAE decoder Step 2: Train discriminator Z Step 3: Train generator D Z’ Z ~ N(0,I) + + - - -
- 37. Results
- 38. Latent constraints results N = 69 Sketch RNN prior: Latent constraints: Sketch RNN prior: Latent constraints:
- 39. Latent constraints results N = 63 N = 68 Sketch RNN prior: Latent constraints: Sketch RNN prior: Latent constraints:
- 40. Latent constraints evaluation ● Randomly interspersed ● Double blind ● In “the wild” ● Sampled 100s of sketches from the latent constraints models and the prior
- 41. Evaluation results Amusement and sadness are significant at p < .05
- 42. Evaluation results People significantly prefer the latent constraints model, p < .0001
- 43. Conclusion First paper to show that a deep learning model can be improved with implicit social reactions • Demonstrated that a deep generative model producing creative content can be improved with facial expressions • Showed a link between human facial expressions and their preferences
- 44. Next steps
- 45. Training with Reinforcement Learning (RL) ● Convert Sketch RNN to a discrete version to enable Q-learning ● Model the reward over time, train a supervised model to approximate ● Deep RL from human faces DiscretizedGround truth 100 200 300 400 …. Reward
- 46. Questions?