Strong AI implies consciousness and self-awareness. This has been the holy grail of AI since the 1970s. Neural networks are function emulators where input produces the desired output. Neural networks use classified or labeled training data and feedback to self correct (back propagation) until their functional output is acceptable. Deep learning and layered networks are leveraging models that were already trained to produce a more complex network. There are several different types of neural networks like convolutional, feed forward, etc. By using a multi model and filtering approaches, models can be combined so that more and more complex tasks can be accomplished. For example, driving involves several models working in concert like one that determines a road type, a few more for feature extraction, etc. Many statistical models such as clustering and regression are called “machine learning” and AI, even though they weren’t when I first learned them. Many of the original AI systems were rules based and were called “expert systems.” However, how these techniques produce outputs is dramatically different than a brain. Mimicking human behavior and capabilities is very different from possessing them like any creature with a brain.