Noise is everywhere. Whether you’re sampling accelerometer data for a mobile game or trying to measure the temperature of a room, noise will be there. Even if you could remove all the noise from an input device, you’ll still have a certain degree of uncertainty. If a player has tapped on the screen, where did they really wanted to tap? All these scenarios forces to re-think about how we gather and preprocess data.
In the last few months the Internet has been flooded with deep dreams: images augmented by neural networks which look incredibly trippy. Deep dreams have the potential to become the new fractals; beautifully backgrounds everyone knows are related to Maths, but no one knows really how. What are deep dreams, how are they generated and what can they teach us?
Our journey to harness the power of evolution is coming to an end. In the previous three parts of this tutorial we have constructed a bipedal body and a mutable genome that determines its behaviour. What’s left now is to actually implement the evolutionary computation that will find a successful walking strategy.
- Part 1. The Evolution Loop
- Part 2. The Simulation
- Part 3. The Fitness Evolution
- Part 4. Improvements
- Conclusion & Downloads
When we are looking at a problem through the lens of evolution, we always have to take into account its two faces: the phenotype and genotype. The previous post focused on creating the body of the creature, together with its brain. It is now time to focus on the genotype, which is the way such information is represented, transmitted and mutated.