Neural Networks and Their Failures and Successes

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It’s no secret at this point that there are some really smart AIs in today’s world. From everything to self-driving cars, to something so simple it only takes 9 lines of code. Many AI systems today use something called a Neural Network, which tries to mimic the human brains cognitive abilities. A human brain consists of 100 billion cells called neurons, which are connected by synapses. When sufficient synaptic input reaches a neuron, that neuron will also trigger in a process called thinking. This is what Neural Networks aim to be, though 9 lines is only about 1 neuron. The main goal of Neural Networks and AI is to try and reach the same level of cognition and learning as a human does, where it becomes difficult to distinguish one from the other. For every success made in one area, there are many failures that arise, meaning that there are many examples of the problems with trying to teach Neural Networks how to actually solve problems the correct way.

Many Neural Networks are designed to learn different tasks and give consistent results back. This is done through a Training Process where, put simply, inputs are given and constantly adjusted until the correct output is given. Through this process, Neural Networks can learn to walk or play games or to even cheat a system. Neural Networks try to be like the human mind but, much like the human mind, they can learn the wrong things and accomplish tasks in a very different manner. This problem can result in very interesting problem solving. One great example is an experiment held in a system called PolyWorld. PolyWorld is an ecological simulator of a simple flat world, possibly divided up by a few impassable barriers, and inhabited by a variety of organisms and freely growing food (Yaeger). During one of the trials of this, an input mistake was made and, while food gave energy, creating a child did not cost any energy. This led some of the organisms in the simulation to come to the conclusion that a mostly sedentary lifestyle was the best option, as long as they reproduced and, in very much A Modest Proposal fashion, consumed their offspring to make more energy. This solved the problem of having to search for food, and allowed the organisms to not have to expend much energy to live.

This means that while we can train a Neural Network to create its own solutions to given problems, in this case of survival, we are not able to teach them a form of morality and that eating ones children, while practical, is not ethical, nor is it an actual solution to living. Because these kinds of systems essentially teach themselves new solutions after some training, they can adapt to new circumstances and find new solutions as they go, and can lead to some amazing success stories. In one instance, Facebook designed its own AI to learn how to make and carryout deals,

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