I love how the 3D visualization and game selection make their research immediately relatable - right down to the cute little avatars! "We’ve shown that agents

I love how the 3D visualization and game selection make their research immediately relatable - right down to the cute little avatars! "We’ve shown that agents can learn sophisticated tool use in a high fidelity physics simulator" I always suspected to evolve intelligence you need an environment rich in complexity. Intelligence we're familiar with (e.g. humans) evolved in a primordial soup packed with possibilities and building blocks (e.g. elaborate rules of physics, amino acids, etc). It's great to see this concept being explored. It reminds me of Adrian Thompson's experiments in the 90's running generational genetic algorithms on a real FPGA instead of mere simulations [1]. After 5000 generations he coaxed out a perfect tone recognizer. He was able to prune 70% of the circuit (lingering remnants of earlier mutations?) to find it still worked with only 32 gates - an unimaginable feat! Engineers were baffled when they reverse-engineered what remained: if I recall correctly, transistors were run outside of saturation mode, and EM effects were being exploited between adjacent components. In short, the system took a bunch of components designed for digital logic but optimized them using the full range of analog quirks they exhibited. More recent attempts to recreate his work have reportedly been hampered by modern FPGA's which make it harder to exploit those effects as they don't allow reconfiguration at the raw wiring level [2]. In Thompson's own words: "Evolution has been free to explore the full repertoire of behaviours available from the silicon resources provided, even being able to exploit the subtle interactions between adjacent components that are not directly connected.... A 'primordial soup' of reconfigurable electronic components has been manipulated according to the overall behavior it exhibits" --- [1] Paper: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=6691182CC83AE8577D7C44EB9D847DA1?doi=10.1.1.50.9691&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=669... Less technical article: https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/ [2] https://www.reddit.com/r/MachineLearning/comments/2t5ozk/what_ever_happened_with_the_evolutionary/cnxfg1s?utm_source=share&utm_medium=web2x https://www.reddit.com/r/MachineLearning/comments/2t5ozk/wha...