Assistant Professor at TCE, Gadag at almaBetter
Machine learning is one of the technology that has become more and more popular with time and machine learning is the subset of the Artificial Intelligence which comes to your knowledge when you are connected to IT industry. Most of the companies like Netflix, Google and smaller companies uses Machine learning algoithms to predict the insights from the data.
Although terms like artificial intelligence, machine learning and deep learning are used interchangeably but, they are not the same thing. Machine learning is the subset of artificial intelligence and deep learning is a subset of machine learning. Here, the key takeaway is:
Alan Turing’s vision towards machine learning is being explained in one of his seminal paper such as “ Machine learning is an application of artificial intelligence where a computer/machine learns from the past experiences (input data) and make future predictions. The performance of such a system should be at least human level.”
A Computer Scientist Arthur Samuel who had experience for very long years in artificial intelligence, has described machine learning as “the study that gives computer the ability to learn without being explicitly programmed.”
Tom M. Mitchell’s technical definition is explianed as “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”
Machine learning in todays world is being treated as the emerging technology where all the problem in any of the field can be treated with solution of this technology. In my view machine learning is the concept where we are making the machines to learn from the previous data it may be labelled or not labelled with the foundation of mathematics and statistics.
Types of Learning used by Machine
Supervised learning: This type of learning includes input data with labels which makes machines to identify them with the features which have been already predefined. In this type of learning by machines are supervised.
Unsupervised learning: This type of learning not includes input data with any labels which makes machines hard to identify them. It is hard to tell what is good learning and what is not. Here machines are not supervised
Reinforcement learning: This type of learning are directed of using task oriented algorithms, where learning will be achieved through series of steps.
Machines often learn from sample data that has both an example input and an example output. For example, one data-sample pair may be input data about an list of animals which are labelled and the associated output is the corresponding labelled identified animals. Given enough of these input-output samples, the machine learns how to construct a model that is consistent with the samples it trained on. From there, the model can be applied to new data that it has never seen before — in this case, which animal is given as input. After learning from sample data, the model applies what it has learned to the real world.
This class of machine learning is called “supervised learning,” since the desired predicted outcome is given, and the model is “supervised” to learn the associated model parameters. Humans know the right answer, and they supervise the model as it learns how to find it. Since humans must label all of the data, supervised learning is a time-intensive process.
Supervised Learning can be further classified into two types:
Regression learning allows us to predict continuous outcome variables based on the value of one or more predictor variables. In simple words, it estimates the relationship between the input and output variables. The goal of regression model is to build a mathematical equation that defines y as a function of the x variables.
Classification methods have a similar goal to regression models. Thus classification is a predictive modeling technique where a label is predicted for a given input. The difference between regression and classification is that the dependent attributes are numerical for regression while they are categorical for classification. In classification the output variable is categorical, that means it can be values like yes-no, true-false, spam-not spam, etc.
In unsupervised learning, the machine learns from data for which the outcomes are not known. It’s given input samples, but no output samples. Unsupervised learning is less common in practical business settings, but it is attractive: you don’t need labeled data and can avoid the human effort and cost of doing so. Unsupervised learning is potentially applicable in many more areas, since it’s not narrowly restricted to applications with labeled data.
As we know that supervised learning requires input and output examples. Reinforcement learning is like unsupervised learning in the sense that outputs are usually not given. The central concept of reinforcement learning is based around an “agent” (a computer or robot) that is interacting with an “environment” (here defined as everything that is not the agent). The agent performs actions on the environment (for instance, a robot takes a step forward). Then, the environments will then provide some sort of feedback to the agent, usually in a form called the “reward.” By “reward”, we don’t mean we give the machine a jolt of electrons. We literally just add to the program’s reward counter. The agent’s goal is to maximize the number in that counter. Critically, however, no one is telling the agent how to maximize the reward or explaining why it gets a reward. That’s what the agent figures out for itself by taking actions and observing its environment.
In many forms of reinforcement learning, the agent does not know what the objective is because it does not have examples of success. All it knows is whether it receives the reward or not. Reinforcement learning has echoes of human psychology — the brain experiences something good and a dopamine rush makes a person want more of it. A bad experience, like touching a hot stove, causes pain that discourages the person from repeating the behavior. However, despite the parallels to human psychology, humanizing it too much is a mistake.
1. Virtual Personal Assistants
Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. All you need to do is activate them and ask “What is my schedule for today?”, “What are the flights from Germany to London”, or similar questions. For answering, your personal assistant looks out for the information, recalls your related queries, or send a command to other resources (like phone apps) to collect info. You can even instruct assistants for certain tasks like “Set an alarm for 6 AM next morning”, “Remind me to visit Visa Office the day after tomorrow”.
Machine learning is an important part of these personal assistants as they collect and refine the information on the basis of your previous involvement with them. Later, this set of data is utilized to render results that are tailored to your preferences.
Virtual Assistants are integrated to a variety of platforms. For example:
2. Predictions while Commuting
Traffic Predictions: We all have been using GPS navigation services. While we do that, our current locations and velocities are being saved at a central server for managing traffic. This data is then used to build a map of the current traffic. While this helps in preventing the traffic and does congestion analysis, the underlying problem is that there are fewer cars that are equipped with GPS. The machine learning models in such scenarios helps to estimate the regions where congestion can be found on the basis of daily experiences.
Online Transportation Networks: When booking a cab, the app estimates the price of the ride. When sharing these services, how do they minimize the detours? The answer is machine learning. Jeff Schneider, the engineering lead at Uber ATC reveals in an interview that they use ML to define price surge hours by predicting the rider demand. In the entire cycle of the services, ML is playing a major role.
3. Videos Surveillance
Imagine a single person monitoring multiple video cameras! Certainly, a difficult job to do and boring as well. This is why the idea of training computers to do this job makes sense.
The video surveillance system nowadays is powered by AI that makes it possible to detect crime before they happen. They track unusual behaviour of people like standing motionless for a long time, stumbling, or napping on benches etc. The system can thus give an alert to human attendants, which can ultimately help to avoid mishaps. And when such activities are reported and counted to be true, they help to improve the surveillance services. This happens with machine learning models doing their job at the backend.
4. Social Media Services
From personalizing your news feed to better ads targeting, social media platforms are utilizing machine learning for their own and user benefits. Here are a few examples that you must be noticing, using, and loving in your social media accounts, without realizing that these wonderful features are nothing but the applications of ML.
People You May Know: Machine learning works on a simple concept: understanding with experiences. Facebook continuously notices the friends that you connect with, the profiles that you visit very often, your interests, workplace, or a group that you share with someone etc. On the basis of continuous learning, a list of Facebook users are suggested that you can become friends with.
Face Recognition: You upload a picture of yourself with a friend and Facebook instantly recognizes that friend. Facebook checks the poses and projections in the picture, notice the unique features, and then match them with the people in your friend list. The entire process at the backend is complicated and takes care of the precision factor but seems to be a simple application of ML at the front end…
Similar Pins: Machine learning is the core element of Computer Vision, which is a technique to extract useful information from images and videos. Pinterest uses computer vision to identify the objects (or pins) in the images and recommend similar pins accordingly.