Guest
Simulation Link (Unity 3D): DNA based life reproduces exponentially and because of the limited local resources, it will hit a wall. Internal struggle arises leading to optimization, hence, the survival of the fittest becomes the objective of life. The purpose of this simulation is not to present the state-of-the-art algorithms but to bridge and link the terms that are used in the Machine Learning world, biology, and politics by making these toy simulations digestible to all related parties. I believe the Machine learning world can give so much to the other fields because life in essence is a survival optimization problem where everything is complicated by being stuck at a Local Maximum. Important Note: Absolute Global Maxima might not exist but at any point there might be several Maxima where one of them is relatively better than the others, hence, we label the best one a Global Maximum and the others Local Maxima. Some experts say that Local Maxima doesn't matter in very high dimensional landscapes. This is true if the convergence speed doesn't matter and also if all dimensions have equal weights. However, we know that is not the case, convergence speed always matters in a competitive world like ours as all Life forms are in a tough race for survival. Also, not all dimensions have the same impactful weight. Many dimensions can be ignored or they are already pruned or not accessible, therefore, the actual number of the plausible dimensions is much less than the available dimensions. Why Is Life Full of Struggle? Success Simulation Using Liberal & Conservative Agents Reaching Maxima Other related videos: Artificial Life: Simulation Hypothesis: Other related Algorithms: Neat algorithm, Hill Climbing, Particle swarm optimization...,etc. Other Optimization techniques that uses Calculus: Gradient Descent, Adam, Recursive Least Squares…, etc. Other terms: Local Minimum & Global Minimum (when objective is minimizing error) Deep Learning techniques are good to avoid being stuck at local maximum as they use many layers and lots of data. Other learning methods: Hebbian Learning, Winner takes it all (WTA), Topics: Neural Networks Machine Learning Mathematics Science brain neuroscience liberal conservative evolution politics left progressive right conservative artificial intelligence philosophy game engine unity godot Website: 00:00 Reaching Your Goal In a Dark World 01:54 Hill Climbing Algorithm 03:32 The Local Maximum Problem 06:27 How Brain Learns (Hebb's rule) 09:13 Liberals vs. Conservatives 11:54 Evolution 16:25 Threat Simulation (Survival) 18:25 Existential Threats and Internal Struggle 20:22 Final Simulation 26:44 Sub-Global Maxima 30:00 Instincts (To increase signal) 32:06 Love (To share traits) 35:24 Memory (To expand vision) 35:53 Intelligence (To interpolate and extrapolate i.e generalize) #evolution #game #liberal #conservative the matrix Music and Sound Tracks: Monkeys Spinning Monkeys by Kevin MacLeod Link: : Local Forecast - Slower by Kevin MacLeod Link: Unseen Horrors by Kevin MacLeod Link: License: “Signal To Noise (CC-BY) by Scott Buckley“ is under a Creative Commons ( cc-by ) license Music promoted by BreakingCopyright: “Cjbeards - Fire and Thunder“ is under a Creative Commons license (CC BY 3.0) Music promoted by BreakingCopyright: Eternity - Whitesand (Martynas Lau) Year: 2017 Link: Warriyo Mortal Feat Laura Brehm Link: This track is not included but it's nice to listen to it after watching the video Where are we going by ksherwoodops & Malukah
