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Introduction to Computational Intelligence

Hadoop, Data Science, Statistics & others

So, it is complex & not feasible to convert these problems into computer understandable binary format (0 and 1). In such cases, we have computational intelligence with which it is possible to formulate these problems into computer understandable format. Computational Intelligence uses some techniques that try to imitate the human’s way of questioning and reasoning.

What is Computational Intelligence?

Computational intelligence is a way to imitate the reasoning process of human intelligence and accomplish this; some CI approaches are used in combination. To achieve human intelligence is complex, but the way it reasons or questions the problem at hand can be replicated, and the same is done using CI approaches.

The approaches which are used in CI are as follows:

Fuzzy Logic

Neural Networks

Evolutionary Computation

Learning Theory

Probabilistic Methods

1. Fuzzy Logic

As the name suggests fuzzy or unclear, there are a number of instances in our day to day life where the situation is not clear, whether to say yes or no, may be its yes/no or no/yes (e.g., asking girlfriend if she wants to buy an expensive dress she may say no with a weird face, man is supposed to read between the lines it is yes). Fuzzy logic decides final output based on the levels of possibilities; for example, Yes sure/ Yes may be/ Not sure / may be no / No. Basically, fuzzy logic is the conditions of ifs and else, which is easy to understand but is not very accurate. It is used for control systems, intelligent appliances, voice recognition etc.

2. Neural Networks

The human intelligence system works on biological neurons; for CI, we have an artificial neural network. Artificial neural network (ANN) attempts to replicate human neural network, where each node of ANN is referred to biological neuron. A biological neuron takes input through input cells called dendrites; in neuron, the information is processed with logic and reasoning (Synapses); the output will be given by cells called axons. The same is referred to as the input layer, activation function, and output layer in an artificial neural network.

Input is given with some weight which further is evaluated and adjusted with feedback, and the activation function decides output based on input parameters. In CI, it has wide applications in the field of classification problems, regression problems, association problems and pattern recognition problems.

3. Evolutionary Computation

Over a period of time, we have created a number of evolution theories and the same is applied to create evolutionary algorithms for e.g., “The survival of the fittest” is the theory and fitness function is the part of the genetic algorithm. In computation intelligence, evolutionary computation is used for optimization problems (the goal is to optimise the present state) and progressive problems (the goal is to predict the future state).

4. Learning Theory

Learning theory basically means the philosophy of learning, how a learner absorbs information, process it and then retain it for further decision-making problems. In learning theory goal is to study/understand different learning techniques through which learning can occur. Cognitive learning is also part of learning theory.

5. Probabilistic Methods

The probabilistic methods are non-constructive and non-deterministic methods that are used for providing the existence of an object. In simple words, if there is a collection of objects and are assigned with certain properties. Now, if all the objects in this collection does not have one specific property and then one chooses a random object from this collection, for this object, the probability to have that property is zero for sure similarly, if we show that probability is less than one, which will prove that there exist one or more objects that do not possess that specific property.

Use of Computational Intelligence

As we have seen, computation intelligence has the ability to consider real-life complexity and then make probabilistic decisions, which makes it very useful in cases of scheduling industry procedures, Disease diagnostics, Video games visualization, Translation systems (like Alexa, Siri which can understand human command and follow along precisely), intelligent robots, infobots autonomous vehicles etc. Computation intelligence is a growing field and has some interesting approaches like evolutionary computation and learning theory, making it competent enough for a wide variety of real-life problems.

Benefits of Using Computational Intelligence

Example of Cell Phone:

The primary purpose was to convey information from one place to another; it started with sending ravens (birds with messages), then we got telegraphs and then fax. The revolutionary discovery was telephones. With time our requirements grew; we wanted more comfort. So, the size was optimized, the way of installation was optimized, and then we have portable phones, which are nothing but our mobiles now. These portable phones were only able to make calls, then we wanted to have a text feature and a way to add all the contacts and then utilities like calendar, watch, alarm, games etc. But we wanted something more our goal shifted further. Now we have smartphones, which are still getting new features a day in and day out.

To Cop-up with such kind of goal, we need to have an approach that can keep up with evolution; this is where Computation Intelligence benefits over other approaches.

Conclusion

Computational Intelligence, which sometimes is also referred to as soft computing, is very useful in addressing real-life problems. CI uses algorithms/approaches like fuzzy logic, neural network, evolutionary theory, learning theory and probabilistic theory, which makes it a good fit for real-life complex problems. CI has application over a wide variety of real-life problems.