become a sort of “Hello World” problem for new neural networks. The XOR problem will be revisited in Chapter 3. While the XOR problem was eventually surmounted, another test, the Turing Test, remains unsolved to this day.
The Turing Test
Finally, the computer program must be capable for forming a response, and perhaps questioning the human that it is interacting with. This is no small feat. This goes well beyond the capabilities of current neural networks.
One of the most complex parts of solving the Turing Test is working with the database of human knowledge. This has given way to a new test called the “Limited Turing Test”. The “Limited Turing Test” works similarly to the actual Turing Test. A human is allowed to conduct a conversation with a computer program. The difference is that the human must restrict the conversation to one narrow subject area. This limits the size of the human experience database.
Speech and handwriting recognition are two common uses for today’s neural networks.
Chapter 7 contains an example that illustrates handwriting recognition using a neural network. Neural networks tend to work well for both speech and handwriting recognition because neural networks can be trained to the individual user.
A Fixed Wing Neural Network
Some researchers suggest that perhaps the neural network itself is a fallacy. Perhaps other methods of modeling human intelligence must be explored. The ultimate goal of AI is the produce a thinking machine. Does this not mean that such a machine would have to be constructed exactly like a human brain? That to solve the AI puzzle we should seek to imitate nature. Imitating nature has not always led mankind to the most optimal solution. Consider the airplane.