Sunday, 3 February 2019

Part 2: Artificial cognitive systems, Machine Learning

In Part 1, we discussed the logical approach of the artificial cognitive systems. We mentioned that it is based on rules and knowledge. These rules and knowledge are pre-programmed in the system. This makes the system static and doesn't learn.
Another approach is the data-driven system, which is called machine learning. In this approach, the system gets a data set with real examples of the input and the output and the system tries to find the correlation in the data based on statistical calculations. A famous example, is a data set of house prices. In this example, each house is identified with features such as house area, number of rooms, etc. and the system tries to predict the best price for the houses. The system finds the correlation between the house features and its prices without explicitly giving it a rule or knowledge. Such a system that can adjust itself by itself based on examples, is considered a learning system.
Deep learning is a recent branch of machine learning. Deep learning is a multi-layer neural network that finds the correlation between the input and the output in a gradient descent mechanism. Deep learning has been widely used in more complex data sets such as text, images, audio and video. It went beyond classification and prediction. It has been used for interactive systems such as autonomous cars. In the autonomous car system, the driver collects sensory data such as cameras along with the action control such as the steering. After a long driving, a big driving data is collected. The data is then fed to the network to train it to predict the best action given a sensory data. After that, by deploying the network into the car with autonomous mode, the network is able to drive by itself is a similar road.
An excellent example is a tutorial by Siraj Raval about self-driving cars. Try it and enjoy!

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