Machine Learning experiments

       Learning is an optional feature in an intelligent system. An intelligent system that knows everything need not learn. But the amount of knowledge in this universe is exponentially more than atoms. So no systems can have a marked answer to a marked question for every knowledge. Learning is the ability of a system to change its configuration. It can change its algorithm.      There are other metrics like effeciency of learning, its precision and accuracy. But that is for later on.    Why is learning important in a system? It allows the system to pick up knowledge from its immediate surroundings, ensuring that it knows what is necessary. Let's speak in biology terms:      Evolution of learning:  There are multiple species in an ecosystem . One species feasts on the other, one species competes with the others for the same resources. Now, it is a constant arm's race between species thanks to evolution . Over the evolutio...

With an infinite budget and 1970s rocket technology, we could achieve some extremely ambitious space projects, even without modern advancements. Here’s what would be possible: 1. Mars Colonization (Apollo-Style) Using Saturn V or equivalent, we could launch multiple heavy-lift rockets to send habitats, supplies, and people to Mars. Without modern efficiency improvements, the mission would require tons of fuel and staging , but brute force can compensate for technological limitations. Nuclear thermal rockets (already theorized in the '70s) could reduce travel time. 2. Moon Base (Extensive, Permanent) A massive lunar base is very doable by continuously launching Saturn V-sized rockets with modules, rovers, and supplies. The Apollo program already demonstrated crewed lunar landings—scaling that up is just a matter of repetition and logistics . Nuclear power and regenerative life support (already conceptualized in the '70s) could sustain a base long-term. 3. Space Stations Bigg...