What is Artificial Intelligence?
Mobile phones, accommodations, home appliances, vehicles, and even parking spots, every whatsit doohickey around us is getting “smarter”. And we are not complaining! Rather, we are happy and thankful to Artificial Intelligence (aka AI) for turning our ordinary and mundane world into extraordinary and magical! Although Artificial Intelligence has entered the syllabus of 1st graders worldwide in some form or the other, a swarm of people is still living under the rock and the terms such as Artificial Intelligence, Machine Learning, and Coding are geekspeak for them. If you are one of them, then you’re on the right page. Ahead, we will not only familiarize you with AI but also some related terms.
Understand Artificial Intelligence in a way you’ll never forget!
Artificial Intelligence (AI) is a comprehensive branch of computer science, which came into existence to design systems that can act intelligently and independently, just like we humans. You may take ATM machines as an example to understand the concept. Just a few keystrokes are enough for them to understand how much money you want to deposit or withdraw and serve you by following your instructions way more efficiently and immaculately than any bank representative can. Because they have been programmed to behave like human cashiers, using AI in a superhero way.
Simply put, AI helps make machines emulate human intelligence processes which include learning, reasoning, and self-correction. Certain AI applications incorporate machine vision, speech recognition, and expert systems. Now that you know what artificial intelligence is, you might be interested in knowing what are the elements that make machines work smartly. Read on to know about the branches of AI.
Subfields or Branches of Artificial Intelligence
Machine Learning, Neural Networks, Evolutionary Computation, Machine Vision, Robotics, Expert Systems, Speech Processing, Natural Language Processing, and AI Planning are termed primary sub-fields of AI. Let’s get acquainted with these terms.
Machine learning (ML): enables software applications to become more exact at anticipating results without being explicitly programmed to do so. The algorithms used in Machine Learning utilize historical data as input to predict new output values. Speech & Image Recognition is one of the most popular examples of machine learning in the real world. Other examples of ML include Computer Speech Recognition or Automatic Speech Recognition (to text), Traffic Alerts using Google Map, Chatbot (Online Customer Support), Google Translation, Prediction, Extraction, Statistical Arbitrage, and Auto-Friend Tagging Suggestion. Furthermore, Machine learning is of three types: supervised, unsupervised, and reinforcement learning.
Neural Networks: help computers make intelligent decisions with limited human assistance. It is nothing but a method in artificial intelligence that trains computers to process data in a way that is inspired by the human brain. A type of deep learning process of machine learning, Neural Networks use interconnected nodes (or neurons) in a layered structure that resembles the human brain. For instance, your smartphone’s camera identifies faces by using neural networks. Text suggestions you see while writing texts or emails are because of neural networks. Not just that, behind the skills of driverless cars to sense other vehicles, traffic signs, pedestrians, and various road hazards, and act accordingly is the magic of neural networks.
Evolutionary Computation (EC): is closely connected to computational intelligence and involves a slew of combinatorial optimization problems and continuous optimization. It is employed in problem-solving systems that use computational models with evolutionary processes as the mainstay design elements. Inspired by the evolutionary concept in biology, it deals with methods and concepts that are continually and selectively evolving and optimizing. Solving optimization problems, designing robots, creating decision trees, tuning data mining algorithms, training neural networks, and tuning hyperparameters employ Evolutionary Computation.
Machine Vision: employs the latest AI technologies to enable industrial equipment to see and study tasks in smart manufacturing, quality control, and worker safety. This subset of AI automates complex or mundane visual assessment tasks and precisely guides operating equipment during product assembly with the help of sensors (cameras), processing hardware and software algorithms. There are majorly four types of Machine Vision Systems — 1D Vision Systems, 2D Vision Systems, Area Scans vs. Line Scans, and 3D Vision Systems. Applications of Machine Vision include Positioning, Identification, Verification, Measurement, and Flaw Detection.
Robotics: empower robots with a computer vision to navigate, sense, and calculate their reaction accordingly by utilizing Electrical Engineering, Mechanical Engineering, and Computer Science, which it is composed of. Machine learning further helps robots learn to function from humans and become intelligent machines that are able to take actions and make choices. There are mainly six types of robots — autonomous mobile robots (AMRs), automated guided vehicles (AGVs), articulated robots, humanoids, cobots, and hybrids. In addition to a smorgasbord of robotic toys and kits, the Robot dog Aibo, the Roomba vacuum, and AI-powered robot assistants are some successful creations of the Robotics branch of AI.
Expert System: is a computer program that is designed to use artificial intelligence (AI) technologies to simulate the decision-making skills and conduct of a human or an organization that are experts in their field. Intended to complement, not replace, human experts, Expert System is usually viewed at the most elevated level of an entity’s knowledge and mastery. PXDES, DENDRAL and MYCIN are some common real-world examples of Expert Systems in AI.
Speech Processing: is a software technology that deals with developing computer systems that recognize spoken words. Powered by advanced solutions such as Natural Language Processing (NLP) and Machine Learning (ML), this discipline of Computer Science processes natural human speaking and lets the AI-enabled gadget behave accordingly. Siri, Apple’s voice-recognition service on a mobile device, is perhaps the most recognised example of speech recognition technology.
Natural Language Processing (NLP): also sometimes called human language processing, NLP equips a computer program to comprehend the natural language of humans, whether spoken or written. It is NLP that helps computers understand the meaning, sentiment, and intent which further facilitates them to learn on the job, storing information and context to strengthen their future responses. Standing the test of time for more than 50 years, NLP has myriad real-world applications in diverse fields such as medical research, search engines, and business intelligence. Some well-known examples of NLP include Language Translation, Search Engine Results, Smart Assistants, Customer Service Automation, Email Filters, and Survey Analytics.
AI Planning: is the task of locating a procedural course of action for a declaratively depicted system to attain its goals while optimizing overall performance measures. This long-standing sub-area of Artificial Intelligence is all about decision-making actions executed by robots or computer programs to accomplish a specific goal. In simple terms, AI Planning is about determining the tasks to be executed by the AI system and the system’s functioning under domain-independent conditions. For example, humans do everything with a definite goal in mind and all their actions are oriented towards achieving those goals, similarly, planning helps find the best route to reach the objective of the AI project. Backward State Space Planning (BSSP) and Forward State Space Planning (FSSP) are two basic types of Planning in AI.
To Sum Up
AI is everywhere as far as the eye can see. Driverless trains are already running for your to-fro journeys. Flying cars are also on their way to reaching you soon. AI is working like a charm in medical science as well. It’s AI that made space trips and space exploration possible. NASA scientists are also using AI to find life in the solar system. Perhaps AI is the key to unlocking the chest of answers to our fundamental questions about life beyond this planet blue. From Siri to Alexa, smartphones to fully-automatic washing machines, driverless trains to talking elevators, and drones to flying restaurants, AI-enabled machines have become part and parcel of our life, taking it notches above. We are so obsessed with artificial intelligence that we can’t imagine our life without AI. Here’s to the most astounding invention of humanity!
With this blog, we have just touched the tip of the iceberg. To explore more about Artificial Intelligence, stay tuned with us.