Hey, who out there is coding AI?My basic assumption is that AI programming today is, at best, a chess program. What I mean is that everything is based off of pre-existing data and a decision tree that handles each situation. In a chess program that means that there is most likely a database of some sort that chooses a move based upon its likely hood of leading to a check mate. Maybe the chess program is capable of assessing which move it should make based upon the chess rating of the player it is up against, but that still is developed by previous experience (data logging) with that user or preprogrammed difficulty levels and the result is nothing more than a secondary decision tree that helps narrow down the decision the chess program makes.The essence of AI is a decision based upon known data.Pretend that we have a project with the mission of flying from Seattle to Mount Rushmore. Even if I take my basic assumption about AI and apply it to what is taking place inside a UAV, decisions are based on the constant flow of data from the sensors and GPS. If the UAV’s AI is designed to find lift then all the AI is doing is recognizing that condition, which is preprogrammed, and executing a sub routine that takes advantage of that situation and modifying its flight plan accordingly, as long as it ends up at Mount Rushmore. From the perspective of the computer, even the sensor input is pre-existing as a kind of analog database that it compares to conditions that it can utilize. What the AI should do in a particular situation is predefined based on recognizing the conditions in the environment. If the AI encounters an unknown it will have to resort to a program that is designed to handle what it has not experienced before, which is going to require data logging and Boolean logic “learning” to still achieve the goal of getting to Mount Rushmore.Suppose the condition the AI has never encountered before is a hurricane. It may be that the AI collected more data, data that it had no idea existed before, but to do that, the AI had to be pointed in the direction of collecting the data in the first place. Whatever response the AI then makes with this new data is going to come from a program that tells it to collect a certain kind of data about itself as it strives to achieve the goal of getting to Mount Rushmore. But the essence of AI is still a decision tree; it is just that the decision tree is being filtered through the predefined expectation of what gets it to Mount Rushmore.Basically:If this is getting me to Mount Rushmore ThenKeep doing it = trueEnd IfWe still have a decision tree that works on predefined data. The only difference is that we have a program that tells the AI how to build the decision tree based on the positive choices that achieve the goal.The decision tree being built would really be based on a template and that is why AI is essentially a decision based upon known data, even data that it has not seen before, because even the unknown is going to be made to conform to something that is known.Here is a philosophy of where I would like to take AI in the future.I think the future of AI will be made using chaos theory and fractals. For instance, snowflakes are practically infinite in their appearance but they are still snowflakes. AI intelligence based on a pure decision tree may be able to handle this fact, but the process of recognizing each individual snowflake will take more time than it’s worth to figure out. Wind moves in predicable patterns and the predictability is not much different than the fact that a snowflake is still a snowflake even though each, individually, is radically different.Of course, this notion of using chaos theory is nothing new. In a world that is increasingly using biometrics, the idea of using the math that matches the fractal for something like facial recognition is becoming increasingly more common. This recognition then would allow a robot, perhaps, to go into a subroutine that governs its protocol around a human being.In the world of UAVs, and for that matter robotics in general, the ability to use fractals could create the ability to forecast conditions before they happen. For instance, instead of having a situation where the decision tree is being created on the linear input of the sensors, the AI would anticipate its own unknowns.A good example of what I mean would be eddies of the wind. In order to survive a hurricane the AI is able to anticipate where they are and plan a flight path that tries to avoid the most powerful swirls. In a one knot breeze, the AI can choose to ignore them as unimportant and stop forecasting these unknowable events. Somewhere in-between these two wind factors the AI might choose to start forecasting new data for itself.Another way this might be useful is recognizing a geographical condition, such a mountain, and projecting how the wind may act in that situation. A factor it might consider then is the abandonment of a current lift scenario in favor of taking advantage of the lift being provided near that mountain (or hill for that matter).There is no escaping a decision tree. They are a necessary function, but they can become more of the conscious part of the AI while the abstract of fractals can become more of a subconscious that is able to bring the abstract into focus.In fact, in this subconscious, the AI can run simulations of what events might take place if it chooses a certain path and have a preplanned behavior for venturing off into the unknown.
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