Artificial Intelligence & Machine Learning – The Concept,Opportunities and The Future

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Content Created by Divya Das M, Digital Marketing Intern, One Team Solutions

Overview

Over the years, technology has revolutionized the world. With all of these revolutions, technology has also made our lives easier, faster, better, and more fun. Here, one of the booming technologies of Information Technology is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines.

Artificial Intelligence and Machine Learning have become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably.

Artificial Intelligence

Artificial intelligence is a technology that enables a machine to simulate human behavior. Artificial intelligence is an area of innovation in science trying to make machines intelligent. John McCarthy is one of the “founding fathers” of artificial intelligence, together with Alan Turing, Marvin Minsky, Allen Newell, and Herbert A. Simon.

The “AI” term was coined by John McCarthy in 1956 at The Dartmouth Conference in Hanover, New Hampshire. He defined artificial intelligence as “Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs”.

The central principles of AI include reasoning, knowledge, planning, learning, communication, perception, and the ability to move and manipulate objects. Most of the new technologies come with Artificial intelligence.

Algorithms often play a very important part in the structure of artificial intelligence, where simple algorithms are used in simple applications, while more complex ones help frame strong artificial intelligence.

A few of the well-known pillars of AI include machine learning, knowledge/logic-based systems, machine vision, robotics, and natural language processing, etc. The goal of AI is to make a smart computer system like humans to solve complex problems.

Why Artificial Intelligence?

Before Start Learning about Artificial Intelligence, we should acknowledge the significance of AI and why should we learn it

• Day to Day Application
• Digital Assistance
• Opens a World of Opportunities.
• Delivering Health Care

Types of Artificial Intelligence

Artificial Intelligence can be classified into various types. There are mainly two types of main categorization which are based on capabilities and based on functionally.

• Type1  : Based on Capabilities
  1. Weak AI or Narrow AI : Narrow AI is a term used to describe artificial intelligence systems that are specified to handle a singular or limited task. For example, Narrow AI may be used for spam email filtering, music recommendation services, and even autonomous vehicles
  2. Strong AI: Strong Artificial Intelligence (AI) is a theoretical form of machine intelligence that is equal to human intelligence. Key characteristics of Strong AI include the ability to reason, solve puzzles, make judgments, plan, learn, and communicate. It should also have consciousness, objective thoughts, self-awareness, sentience, and sapience.
• Type2: Based on Functionalities

1. Reactive Machines:  This is one of the basic forms of AI. It doesn’t have past memory and cannot use past information to information for future actions. Example:- IBM chess program that beat Garry Kasparov in the 1990s.
2. Limited Memory:  Limited memory AI is mostly used in self-driving cars. They will detect the movement of vehicles around them constantly. The static data such as lane marks, traffic lights, and any curves in the road will be added to the AI machine. This helps autonomous cars to avoid getting hit by a nearby vehicle.
3. Theory of Mind:  This type of AI should be able to understand people’s emotions, beliefs, thoughts, expectations, and be able to interact socially Even though a lot of improvements are there in this field this kind of AI is not complete yet.
4. Self-awareness: An AI that has it’s own conscious, super-intelligent, self-awareness, and sentient. Self-aware AI involves machines that have human-level consciousness. This form of AI is not currently in existence but would be considered the most advanced form of artificial intelligence known to man.

Scope of Artificial Intelligence

Artificial Intelligence is going to change almost every aspect of daily life

“AI is going to change the world more than anything in the history of mankind. More than electricity.”— AI oracle and venture capitalist Dr. Kai-Fu Lee”

Over the next 10 years, artificial intelligence will make more progress than in the fifty before it, combined. Let’s glance through a list of possible and probable futuristic applications surrounding AI, which will sure make human lives much easier on Earth.

• Autonomous Transportation
• Education powered by AI
• Employment in the era of AI
• Healthcare re-imagined
• Home and Service Robots
• Weaponry in the world of AI
• AI for space exploration.

Machine learning

“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” — Tom Mitchell, Carnegie Mellon University

Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term “Machine Learning” in 1959 while at IBM.

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead.

Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly. Simply it is defined as the learning from examples And experience. Machine Learning is the dominant mode of AI.

Machine learning describes a set of techniques that are  commonly  used to  solve a  variety  of real-world problems with the help of computer systems which can learn  to  solve a  problem instead  of being  explicitly programmed

Types of Machine Learning  (ML)  Algorithms

1. Supervised Learning algorithms 
   1. Supervised learning uses the classification of algorithms and regression techniques to develop predictive models. The algorithms include linear regression, logistic regression, and neural networks as well, apart from the decision tree, Support Vector Machine (SVM), random forest, naive Bayes, and k-nearest neighbor.
   2. Here the human experts act as the teacher where we feed the computer with training data containing the input/predictors and we show it the correct answers (output) and from the data, the computer should be able to learn the patterns.
   3. Examples of Unsupervised Learning: Nearest Neighbor, Naive Bayes
2. Unsupervised Learning algorithms 
   1. Unsupervised machine learning helps you to finds all kinds of unknown patterns in data. Clustering and Association are two types of unsupervised learning.
   2. In Supervised learning, Algorithms are trained using labeled data while in Unsupervised Learning Algorithms are used against data that is not labeled.
   3. Examples of Unsupervised Learning: Apriori algorithm, K-means.
3. Semi-supervised machine learning algorithms
   1. Semi-supervised machine learning algorithm falls somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data.
   2. The systems that use this method are able to considerably improve learning accuracy.
   3. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
4. Reinforcement Learning algorithms 
   There are three approaches to implement a Reinforcement Learning algorithm.
   1. Value-Based
      In a value-based Reinforcement Learning method, you should try to maximize a value function V(s). In this method, the agent is expecting a long-term return of the current states under policy π.
   2. Policy-based
      In a policy-based RL method, you try to come up with such a policy that the action performed in every state helps you to gain maximum reward in the future.
   3. Model-Based
      In this Reinforcement Learning method, you need to create a virtual model for each environment. The agent learns to perform in that specific environment.
      Example of Reinforcement Learning: Markov Decision Process

Applications of Machine learning

Machine learning algorithms are used in a wide variety of applications. Herein, we share most trending real-world applications of Machine Learning that we use every day

• 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. Virtual Assistants are integrated into a variety of platforms.
  • Smart Speakers: Amazon Echo and Google Home
  • Smartphones: Samsung Bixby on Samsung S8
  • Mobile Apps: Google Allo
• Social Media Services
  It is used to identify objects, persons, places, digital images, etc. From personalizing your news fed to better ad targeting, social media platforms are utilizing machine learning for their own and user benefits.
  • 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 you 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.
• Medical Diagnosis
  • ML provides methods, techniques, and tools that can help in solving diagnostic and prognostic problems in a variety of medical domains.
  • The successful implementation of ML methods can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care.
• Predictions while Commuting
  • Traffic Predictions: We all have been using GPS navigation services.
    It predicts the traffic conditions such as whether traffic is cleared, slow-moving, or heavily congested with the help of two ways:
    • Real-Time location of the vehicle form Google Map app and sensors
    • The average time has taken on past days at the same time.

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Conclusion

Machine learning and AI complement each other, and the next breakthrough lies not only in pushing each of them but also in combining them.

In future, you’ve been hearing a lot of buzz around Artificial Intelligence and Machine Learning

Artificial Intelligence is the overarching category for Machine Learning. We conclude that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent.

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