Four short links: 1 November 2018
Data Science, AI Ethics, Coded for Curiosity, and Worm Parking
- How to Decide Which Data Science Projects to Pursue (Hilary Mason) — data science projects are not independent from one another. With each completed project, successful or not, you create a foundation to build later projects more easily and at lower cost. Some good advice on how to build a non-sucky data strategy.
- AI Ethics, Impossibility Theorems, and Tradeoffs — There is no policy choice that satisfies all ethical principles. A data scientist takes us through the options and the math that makes this statement true.
- Reinforcement Learning with Prediction-Based Rewards (Open AI) — We’ve developed random network distillation (RND), a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity, which for the first time exceeds average human performance on Montezuma’s Revenge. RND achieves state-of-the-art performance, periodically finds all 24 rooms, and solves the first level without using demonstrations or having access to the underlying state of the game.
- C. Elegans Can Park a Car — it only took 12 neurons, and yet you look down any city street and…*sigh*.
