People think Artificial intelligence (AI) as a sort of brain which is artificial and recollect images from science fiction movies about robots. Such images contain a very little to do with how AI is literally used in today’s world. It is true that AI has a lot of similarities to the functioning of the human brain, AI does not need to pretend to be biological.
Before we have a deep dive into AI, I would like to make you familiar with some general concepts about interacting with an AI algorithm. An AI algorithm is also referred as a model. There are various AI algorithms, or models. A few of them are Bayesian networks, Neural Networks, Support Vector Machines, and Hidden Markov Models.
How the human brain relate to AI:
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 the human brain and how it reacts to 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.
Modeling Problems
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:
- Data classification
- Regression analysis
- Time Series
- Clustering
Sometimes you will model one problem using several of these approaches. We will examine each of these, beginning with data classification.
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
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
Time Series:
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
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.
