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Top 5 Real Time Applications of Artificial Intelligence
 September 25, 2019
 Posted by: admin
 Category: Uncategorized
“Computers are able to see, hear and learn. Welcome to the future.” ~Dave Waters
The biggest advancement that software technology has achieved in the last decade has to be the concept of artificial intelligence. Today, we encounter the perks of artificial intelligence at almost every step in our daytoday life and also, in professional scenario.
As per research, $28.5 billion has been already allocated to artificial intelligence worldwide during the first quarter of 2019. The monetary and human investment in artificial intelligence proves that it forms a core part of future technologies. Without artificial intelligence, the ability of systems to learn from data becomes null and that affects the support of most industries in today’s world.
WHAT EXACTLY IS ARTIFICIAL INTELLIGENCE?
In simple words, artificial intelligence is that part of the technology which trains machines to act and react like humans. Some of its major and most common examples:
 Speech recognition
 Fraud Detection
 Dynamic ticket price optimization
 Sales & Business forecasting
 Online customer support chat bots
It works by using machine learning to train systems. Machine learning is the brain that makes artificial intelligence possible.
I AM CONFUSED, IS MACHINE LEARNING DIFFERENT FROM ARTIFICIAL INTELLIGENCE?
You could say that. By the way, is CPU different from computer?
If you understand this reference, you’ve almost understood the point.
 Machine learning is a subset of artificial intelligence which uses statistical methods and tool to study available databases in order to build predictive, selfprogrammed computer system.
 Amazon knows what you would like to buy, say thanks to machine learning. Your phone knows your face, great, it is machine learning again.
 Machine learning is a discipline based on the interpretative feature of a collection of data. It traces patterns and behaviors to construct a possible future action or reaction.
All of this sounds great and promising, but don’t you want to know how machine learning actually functions?
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN REAL LIFE
Our daily life is sprinkled with various examples of AI AND machine learning.
From Siri telling you your favorite food to Uber detecting who should be your copassenger, everything becomes simple because of AI and machine learning.
In order to find a solution to these problems or to simplify a process for you, machine learning uses algorithms. You would be surprised to know that some of them are pretty simple and can easily be learnt by a fresher.
To explain our point further, let us explain the magic behind every reallife example of Artificial intelligence based on machine learning algorithms that can easily be learnt by a beginner or fresher.
TOP 5 REAL TIME AI APPLICATIONS:

Facebook Knows the Emotions of Your Status: Facebook is essentially a system. Yet, it gives you the right suggestion for emoticons relating to your mood based on your status. Like everything else on Internet, Facebook, too, uses ML algorithms for classifying the updates and Statuses.Both of these AI applications run on the following algorithm which I one of the first thing you learn as a beginner in Artificial Intelligence and Machine learning. –
Naïve Bayes Algorithm: This is a combination of two words which when put together can explain what this algorithm stands for. Naïve denotes that every element in this algorithm is considered to be independent and unrelated to each other. Also, this is based on bayes theorem which uses conditional probability to arrive at a particular result. As per this theorem, something will happen given that something else has already occurred.
Mathematically, it is represented by the mentioned formula, wherein
P(A) represents prior probability of A occurring independently.
P(B) represents prior probability of B occurring independently.
P(AB) is the posterior probability that A occurs given B.
P(BA) is the likelihood probability of B occurring, given A.  Your Favorite TV Anchor is not Using Baseless Assumption for Exit Polls: Whether you enjoy politics or not, you cannot escape the mayhem before an election result. We know that you think that the exit polls are imaginary, but, let us tell you – they’re not. They’re based on sophisticated AI application and use ML algorithms.Both of the above cases are a result of logistic regression. Here’s what it is and we bet you can learn it easily –
Logistic Regression: Logistic regression is predictive model algorithm which is used in the case of binary variables, i.e., when it can only take up two values 0 and 1. The purpose is establish a mathematical equation which helps in predicting the probability of event 1. After the equation is determined, it is used to predict Y when only X is known. Logistic regression derives its name from the transformation function which is an Sshaped curve called logistic function. It is denoted by the formula h(x) = 1/ (1 + e^x)
In logistic regression, the output is defined as probabilities of default class instead of a direct result. Thus, the output is defined in a range 01. The output(Y) is determined by log transforming the XValue with the help of logistic function h(x) = 1/ (1 + e^ x). After that, a threshold is used to turn this probability into a binary classification
 A Machine Can recognize Your Handwriting: We have always thought handwriting to be something personal, more like a personal font. However, in 2019, machines can detect your handwriting accurately. This is a result of AI applications based on machine learning algorithms. It is done by using the following algorithm
Support Vector Machine: SVM is both a classification and regression algorithm . It is a type of supervised machine learning algorithm. In a N dimensional place, SVM creates a (N1) dimensional hyperplane to segregate all these points in two separate groups. Basically, if there’re 2 linearly inseparable points on a paper, SVM helps in finding a straight line which divides these point into two points.
To put it simply, if you were a 3 year child and were asked to separate cherries and apples, you would have difficulty because of how similar they looked. With SVM, one can dive deep into seemingly indistinguishable data for advanced classification that helps in arriving at more sophisticated and accurate results.
If we continue talking more about various algorithms, their use and AI application, we will end up writing a book. Well, there’re already books, so, we don’t need that anymore.
What we really need is good trainers. Thus, if you are looking for good trainers in Artificial Intelligence and Machine learning, look no further than Gyansetu.
 Your Email Knows What’s Junk: 9 out of 10 times, your mail always knows what should go to the spam folder and what should be priority. Did you know that it is because of an AI application? Artificial intelligence uses machine learning to identify and classify emails based on the content used and segregate them accordingly.
 Your Marketing Lead Uses this For Predictions: If you’re a part of a marketing team, you must be well aware of the exercise of framing a marketing campaign. But, do you know you topmost marketing firms use simple AI application to predict the success of a marketing campaign
THE AI ALGORITHM AND MACHINE LEARNING CONNECT
If you’ve ever given an interview for a coder profile in a reputed firm, you would know that one of the most important question is always regarding algorithms.
 Algorithms have been a constant solver in the field of software and programming. Machine learning, too, greatly depends on algorithm to pave way for problem solving.
 In simple words, algorithms are defined as stepbystep process of finding a solution by performing sequenced actions. Computer programs are basically elaborate algorithms and machine learning is no different.
 Machine learning is computers teaching themselves how to behave based on past data.
“Machine” “learns” by experience and not by being explicitly programmed
This phenomenon takes place by developing a relationship between certain input factors and output results. The process can be described as follows:
 Input data is identified.
 The system analyses pattern and behavior from the given dataset.
 The system, then, utilizes learnings form the given datasets to predict future outcome or potential behavior for a similar data.
If we go deeply into these, machine learning is completely dependent on algorithms and its working is divided into the following algorithms:
 SUPERVISED ALGORITHMS: These type of algorithms use labeled examples of past data to predict behavior of similar datasets. The purpose of this is to bring an intended output with varying input datasets of similar kind and type.
 UNSUPERVISED ALGORITHMS: It is exactly the opposite of supervised algorithms wherein hidden structures and probable outcomes are figured out by exploring completely unlabeled data. It is not coming to a correct conclusion but about exploring to find out inferences.
 SEMISUPERVISED ALGORITHMS: A wonderful cusp of both supervised and unsupervised algorithms which helps in improving learning accuracy.
 REINFORCEMENT ALGORITHM: With this method, a machine can determine the ideal behavior expected in a specific situation to maximize it utility with trial and error method.
Machine learning is a game of finding pattern in the enormous data we have produced so far with the help of algorithms. If you want to learn more about some of the most important machine learning algorithms in detail, please check our blog.
CONCLUSION:
Artificial Intelligence is truly based on the power of algorithms. To truly learn and enjoy the powerful technology, one must be in sync with its algorithms.
The good news is learning machine learning algorithms is not as difficult as it sounds like. All you need is a good support system who cam make learning fun for you and help you appreciate the beauty of artificial intelligence and machine learning.
So, even if you’re a fresher and are skeptical to begin, we are here for you. Drop us a hello and we will get back to you.