The purpose of AI is to allow a computer to work similar to a human brain. However, this doesn’t mean that AI seeks to emulate every aspect of the human brain. The degree to which an AI algorithm relates the actual functioning of the human brain is known as biological plausibility. What you want to do is more important than how. At the highest level, there are similarities between a human brain and most AI algorithms.
About human brain and how it reacts to the real life situations:
While we know relatively little about the internal operation of the brain, we do know a fair amount about the external operation of the brain. The brain is essentially a black box connected by nerves. These nerves carry signals between the brain and the body. A certain set of inputs causes a certain output. For example, feeling your finger about to touch a hot stove will result in other nerves sending commands to your muscles to pull your finger back.
It is also very important to note that the brain has an internal state. Consider if you suddenly heard a horn. How you react is determined not just by the stimuli of the horn, but where you are when you hear the horn. Hearing a horn in the middle of a movie evokes a very different response than hearing a horn when you are crossing a busy street. The knowledge of where you are present, creates a certain internal state that causes your brain to react differently to different contexts.
The order in which stimuli are received is also important. A common game is to close your eyes and attempt to use only touch to determine what an object is. When you grab the object, you do not instantly receive enough information to determine what it is. Rather, you must grab the object and run your fingers over it. As your fingers run over the object, you receive information that forms an image of what the object is.
You can essentially think of the human brain as a black box with a series of inputs and outputs. Our nerves provide our entire perception of the world. The nerves are the inputs to the brain. There are actually a finite number of inputs to a typical human brain.
Similarly, our only means to interact with the world are the outputs from our nerves to our muscles. The output from the human brain is a function of the inputs and internal state of the brain. In response to any input, the human brain will alter its internal state and produce output. The significance of the order of the inputs is handled by the internal state of the brain.
Computer based neural networks are not like the human brain in that they are not general-purpose computation devices. Neural networks, as they currently exist, carry out very small, specific tasks. An AI algorithm experiences its reality by providing output based on the algorithm’s internal state and the input it is currently receiving. The “reality” that the algorithm is attached to may change as the researcher experiments with the algorithm.
This model of inputs, outputs, and internal state holds true for most AI algorithms, regardless of whether you are creating AI for a robot or a stock picker. Of course, some algorithms are more complex than others.
Knowing how to model a real-world problem to a machine-learning algorithm is critical. Different problems will lend themselves to different algorithms. At the highest level, you will model your problem in one of four different ways:
Sometimes you will model one problem using several of these approaches. We will examine each of these, beginning with data classification.
Classification attempts to determine the class in which the input data falls into. Classification is usually a supervised training operation, which occurs when the user provides data and expected results to the machine-learning algorithm. In data classification, the expected result is identification of the data class.
Supervised training always deals with known data. Between the training period, machine-learning algorithms are estimated accordingly to know how well they classify data. The belief is that the algorithm, trained once, will have the ability to classify unknown data as well.
Regression analysis is a structure of predictive modeling technique which examines the relationship between an independent variable and a dependent variable. This technique is used for time series modeling, forecasting and finding the natural relationship between the variables. For instance, relationship between rash driving and the count of road accidents by a driver is calculated through regression. Regression analysis is a prominent tool for analyzing and modeling data.
Why do we use Regression Analysis?
Considering an example, you want to determine growth in sales of an enterprise based on present economic conditions. You have the data of a enterprise for past few months, which indicates growth in sales is approximately two and half times the growth in the economy. Using this insight, we can imagine the sales of the enterprise based on past and current information.
There are many advantages of using regression analysis. They are expressed below:
It indicates the important relationships between an independent variable and a dependent variable.
It shows the strength of impact of many independent variables on a variable which is dependent
A time series is defined as a sequentially indexed rendering of your historical data which is used to solve segmentation and classification problems, in addition to predicting future values of numerical properties. For instance, considering the real time air quality index of madrid. This is the best method often used in sales forecasting, predicting stock prices, production and inventory analysis, website traffic, or weather forecasting, etc.
Clustering is very similar to classification in that the computer is required to group data. Clustering algorithms take input data and place it into clusters. The programmer usually specifies the number of clusters to be created before training the algorithm. The computer places similar items together using the input data. Because you do not specify what cluster you expect a given item to fall into, clustering is useful when you have no expected output. Because there is no expected output, clustering is considered unsupervised training.