How MNCs are getting benefited from AI/ML?

Here’s is a basic definition of machine learning –
“Machine Learning is a technique of parsing data, learn from that data and then apply what they have learned to make an informed decision”

Now a days many of big companies use machine learning to give there users a better experience, some of the examples are, Amazon using machine learning to give better product choice recommendations to there costumers based on their preferences, Netflix uses machine learning to give better suggestions to their users of the Tv series or movie or shows that they would like to watch.

Abstract

Machine learning (ML) is a technology that gives the systems the ability to learn on its own through real-world interactions and generalizing from examples without being explicitly programmed as in the case of rule-based programming. Machine Learning can play a key role in a wide range of critical applications. In machine learning, Linear Regression (LR) is a basic technique by which a linear trend can be obtained. But Support Vector Machines (SVMs) have advanced features such as high accuracy and predictability. In this paper we survey the pros and cons of using both these techniques to predict values and compare both algorithms.

Before talking about machine learning lets talk about another concept that is called data mining. Data mining is a technique of examining a large pre-existing database and extracting new information from that database, it’s easy to understand, right, machine learning does the same, in fact, machine learning is a type of data mining technique.

INTRODUCTION
One of the most important tasks in ML is to predict, with high accuracy and speed, the trend and the results for any given dataset. Before the era of Artificial Intelligence (AI) and ML, predictions were done manually by a statistician who would plot graphs and use mathematical methods and models to observe trends. One of these methods was to fit a straight line of the form y = mx + c to a graph such that the line passes through the maximum number of data points of the given dataset. Mathematically speaking, on plotting the values of the dataset on a graph, fit a straight line through the points such that the square of the distance between each point and the line is minimum. This line, called the hypothesis is used to predict the y value for any given x. This prediction technique is called Linear Regression and the formula used is called the Least Squares method. This technique is widely known to statisticians and has also been used as one of the basic concepts of ML.

Deep Learning:
Deep learning is actually a subset of machine learning. It technically is machine learning and functions in the same way but it has different capabilities.

The main difference between deep and machine learning is, machine learning models become better progressively but the model still needs some guidance. If a machine learning model returns an inaccurate prediction then the programmer needs to fix that problem explicitly but in the case of deep learning, the model does it by himself. Automatic car driving system is a good example of deep learning.

Let’s take an example to understand both machine learning and deep learning –
Suppose we have a flashlight and we teach a machine learning model that whenever someone says “dark” the flashlight should be on, now the machine learning model will analyse different phrases said by people and it will search for the word “dark” and as the word comes the flashlight will be on but what if someone said “I am not able to see anything the light is very dim”, here the user wants the flashlight to be on but the sentence does not the consist the word “dark” so the flashlight will not be on. That’s where deep learning is different from machine learning. If it were a deep learning model it would on the flashlight, a deep learning model is able to learn from its own method of computing.

Artificial intelligence:
Now if we talk about AI, it is completely a different thing from Machine learning and deep learning, actually deep learning and machine learning both are the subsets of AI. There is no fixed definition for AI, you will find a different definition everywhere, but here is a definition that will give you idea of what exactly AI is.
“AI is a ability of computer program to function like a human brain ”

AI means to actually replicate a human brain, the way a human brain thinks, works and functions. The truth is we are not able to establish a proper AI till now but we are very close to establish it, one of the examples of AI is Sophia, the most advanced AI model present today. The reason we are not able to establish proper AI till now is, we don’t know the many aspects of the human brain till now like why do we dream ? etc.

How ML works?

• Gathering past data in any form suitable for processing.The better the quality of data, the more suitable it will be for modeling
• Data Processing — Sometimes, the data collected is in the raw form and it needs to be pre-processed.
  Example: Some tuples may have missing values for certain attributes, an, in this case, it has to be filled with suitable values in order to perform machine learning or any form of data mining.
  Missing values for numerical attributes such as the price of the house may be replaced with the mean value of the attribute whereas missing values for categorical attributes may be replaced with the attribute with the highest mode. This invariably depends on the types of filters we use. If data is in the form of text or images then converting it to numerical form will be required, be it a list or array or matrix. Simply, Data is to be made relevant and consistent. It is to be converted into a format understandable by the machine
• Divide the input data into training,cross-validation and test sets. The ratio between the respective sets must be 6:2:2
• Building models with suitable algorithms and techniques on the training set.
• Testing our conceptualized model with data which was not fed to the model at the time of training and evaluating its performance using metrics such as F1 score, precision and recall.

Machine Learning & Artificial Intelligence in our Daily Life

Commuting:

In general, a single trip takes more than average time to complete, multiple modes of transportation are used for a trip including traffic timing to reach the destination. Reducing commute time is not simple yet, here below you find how machine learning aid in reducing commute time

1.Google’s AI-Powered Predictions

Using anonymized location data from smartphones, Google Maps (Maps) can analyze the speed of movement of traffic at any given time. And, with its acquisition of crowdsourced traffic app Waze in 2013, Maps can more easily incorporate user-reported traffic incidents like construction and accidents. Access to vast amounts of data being fed to its proprietary algorithms means Maps can reduce commutes by suggesting the fastest routes to and from work.

2. Riding Apps: From how to fix the price of the ride, and how to minimize the waiting time to how do riding cars fix up one’s trip with other passengers to lessen diversion. Yes, the solution is machine learning. ML assists the company to estimate the price of a ride, computing optimal pickup location and ensuring the shortest route of the trip, also for fraud detection.

3. Commercial flights to use Autopilot: With the help of AI technology, Autopilots are taken care of Flights now. In a report of The NewYork Times, pilots reported doing manual flying of seven minutes, mainly during takeoff and landing, and the rest fly is done by autopilots.

Social Networking

1. Facebook: While uploading a photo on Facebook, it automatically reflects faces and suggests friends tag. Facebook uses AI and ML to identify faces. It uses the ANN algorithmthat imitates the human brain and power facial recognition software. Facebook uses AI to personalize newsfeed and makes sure to reflect posts that entertain one. It shows ads of a particular business that are relevant to one’s’ interest.

2. Pinterest: It employs computer vision to automatically recognize objects in the images or “pin” and then recommend similar pins. Other applications cover pam prevention, search, and discovery, email marketing, ad performance, etc with the help of machine learning.

3. Snapchat: It offers facial filters (known as Lenses) that filter and track facial activity, permits users to tag animated images or digital masks that shift when their faces move.

4. Instagram: With the help of ML algorithms, sentiments behind the emojis can be identified. Instagram can make and auto-recommend emojis and emojis hashtags. There is massive use of emoji across all demographics that are used to describe and explore by Instagram at a massive scale through emoji-to-text-translation.

Medical Diagnosis and Healthcare

Machine Learning incorporates a soup of techniques and tools to deal with the diagnostic and prognostic issues in the diverse medical realms. ML algorithms are highly used for the analysis of medical data for detecting regularities in data, handling inappropriate data, explaining data generated by medical units, also for effective monitoring of patients. (You must wonder to know how Internet-connect devices incorporate to monitor patients remotely)

Machine learning also helps in estimating disease breakthroughs, driving medical information for outcomes research, planning and assisting therapy, and entire patient management.

Conclusion

It remains not worth amazing how machine learning and artificial intelligence have changed our life by making it easier, we have screened various applications here the machine learning is used back in the arena to impact our daily lives, it also allows us to take business decisions, optimize operations and augment productivity for industries to stand out in the market.