Decoded Artificial Intelligence | A game-changing technology | Great Learning

Artificial Intelligence Decoded

One of the major technologies disrupting industries and business processes is Artificial Intelligence (AI).  It has become so pervasive in everyday life that you come across AI applications in almost every aspect of your life. Complex real-world solutions like tumor detection, to internet search and industrial robots; AI redefines the way things work. Design intelligent agents use machine learning and logic to solve problems. The world of gaming has undergone a massive transformation with the use of AI to enhance the human players’ gaming experience. Messaging apps have also witnessed wide AI implementation with image recognition, filtering, and other functionalities.

AI has thus emerged as a game-changing technology in as diverse areas as healthcare, medicine, agriculture, education, banking and finance, e-commerce, industrial processes, logistics, customer engagement, social media and various internet activities. As AI reshapes our world, we need to understand what the term means and how the technology adapts works in different environments.

So what is Artificial Intelligence (AI)?

Artificial intelligence is defined as a branch of computer science dedicated to making intelligent machines and programs. The father of Artificial Intelligence, John McCarthy, established artificial intelligence as The science and engineering of making intelligent machines, especially intelligent computer programs.

In other words, AI is the theory and practice of applying science, logic, and engineering to machines and computer programs in a way that they exhibit the characteristics associated with intelligence in human behavior – perception, language processing, reasoning, planning, problem-solving, learning and adaptation.

Knowledge representation and reasoning are closely linked components of AI used to create artificial models based on the information. While knowledge representation analyses the various types of knowledge used in everyday life; reasoning is the process that enables us to make judgment, decisions, and prediction.

To get the essence of AI, you need to have a basic understanding of knowledge and how the various knowledge types are used to map relationships and establish rules. Declarative knowledge is facts about objects. Structural knowledge refers to relationships between objects and concepts. Meta knowledge is the understanding of knowledge. Procedural knowledge covers the rules, methods, and procedures. Heuristic knowledge represents the rule of thumb. These knowledge types are ultimately “represented” as symbols, images, numbers, graphs, and networks (relationships); and make up the umbrella word knowledge representation.

Reasoning is the mechanism of applying “reason” or logic on the “knowledge” once it is “represented.” In other words, reasoning is the process of deriving logical conclusions from given knowledge.

Perception applies the “sensing” element of humans to the machine systems where data is acquired by sensors. So the systems can acquire, interpret, select, and organize sensory information in a meaningful way, as in aircraft sensors.

Learning refers to the process where knowledge or skill is gained through study, practice, experience, or by being taught.

Problem Solving is the application of perception to decide upon the best path or alternative result, from a given set of possible alternatives to reach the desired goal.

Linguistic Intelligence refers to the ability to use, understand, speak, and write a verbal or written language for two-way communication.

Planning concerns identification of goals, formulation of strategies or action sequences for implementing a task. In AI this typically translates into choosing and executing a set of actions as taken by intelligent agents and unmanned vehicles.

Motion and manipulation is the human ability to move based on knowledge, reasoning, perception or other decision. AI uses this for object manipulation in autonomous robots.

The ultimate research goal of AI is to create intelligent systems that display intelligent behavior and think and reason like humans.

How does Artificial Intelligence work?

AI can be manifested in physical machines like robots and self-driving cars, or virtual machines like programs. In both, the machine displays intelligent behavior – recognizes objects or voices, senses changes, applies reason, learns from data, makes decisions, or plans – just as any intelligent human being would do.

How do you measure the intelligence level of the machines?

By using the Turing test.

Just as psychometric and aptitude tests measure the cognitive or logical thinking and reasoning capacity of a human; the Turing Test measures the intelligence of the machine.

The Turing Test is a must-know terminology for every wannabe AI engineer or professional. The term was coined by Alan Turing in 1950, who is also credited with pioneering machine learning in the 1940s. The test was devised as a rudimentary method of finding “whether or not a computer is capable of thinking like a human being.”

How does AI work in different scenarios?

AI integrates seamlessly with the analytics program, for deep data-driven insights. It takes into account the convergence of people, process and technology, and their relationships. In marketing, AI is used to automate real-time offers, engage in chatbot conversations with the customer and sift through huge data to improve the accuracy of personalized offers. In banking, one of the most powerful AI function is in the fraud detection system where linkages can be established from many hidden and complex layers for discovering suspicious transactions. In the medical field, AI techniques in deep learning, image classification, and object recognition can detect cancer from MRIs and X-rays with the accuracy of a highly trained medical practitioner. By leveraging automation, conversational platforms and bots, machines are mining large amounts of sensor and other data to improve technologies at home, like security intelligence. Deep learning is combined with analytics engine in a high-performance computing environment for making instant market predictions in high-frequency algorithm trading.

These are some examples of how AI can work in multiple roles and different industry applications.

Why is Artificial Intelligence important?

Everyone around you is talking about AI and how it has invaded every aspect of our lives and beyond. This is true. Decades of extensive research has made the applications of AI technologies diverse and more useful.

Here are some reasons that will satisfy you why AI is important, and why you should think of a career in AI engineering:

  • – AI automates iterative learning through data.It performs frequent, high-volume, tasks with precision.
  • AI embodies intelligence in existing products.
  • AI adapts to progressive learning algorithms to allow the data do the programming. Through training and more data insights, it provides the best answers.
  • – AI can analyze hidden layers of data, for intelligent analysis in real-time.
  • – AI provides high accuracy through deep neural networks, getting more and more accurate with use.
  • AI gets the most out of the data as its algorithms are self-learning, making the data itself an intellectual property for competitive advantage.

The Artificial Intelligence technology cluster

Over time, specialized research has extended the functionalities of AI across business processes and domains. New fields have evolved to examine ways how to apply rules and logic to make a program or machine “highly intelligent.” There are various offshoots of AI finding prolific use as listed below.

Knowledge Engineering is a key offshoot of AI that creates knowledge-based systems. It is based on large amounts of knowledge, rules and reasoning methods to provide best answers to real-world problems. Advanced research has developed an expert system that can be designed to imitate the reasoning processes of a practitioner or expert in the domain under consideration.

Machine learning gives machines the ability to “learn” using algorithms to discover patterns. By automating the analytics engine using neural networks, statistics, operations research and physics; machine learning technology discovers hidden insights in the data without being programmed. Instead, it learns from iterations to look for insights and make decisions and predictions, side-stepping the need to be programmed.

A neural network is a type of machine learning that uses interrelated units to process and relay information between each unit. The process is aimed to discover connections and meaning from unknown data.

Deep learning is a subset of machine learning and the most advanced of AI technologies. Deep learning uses advanced techniques to make machines mimic human intelligence most closely. It makes use of massive neural networks, high computing power and enhanced training to learn complex patterns in huge amounts of data. Pattern Recognition and Image Processing are examples.

Cognitive computing is a subfield of AI that aims at a natural, human-like interaction with machines. The ultimate goal is for a machine to replicate human processes through images and speech and intelligent response.

Computer vision deals with pattern recognition using deep learning to identify given elements in a picture or video. With machines being able to understand, visualize and process images, the ability to capture images in real time is used to interpret the setting. This finds use in imagery and video analysis, for crime and military purpose.

Natural language processing (NLP) is the talent of computers to understand natural languages in their native forms, for further analysis and processing. It supports conversational interface in natural language creating plenty of user data for further analysis.

Soft Computing is a field that is used to build intelligent and wise machines. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty or partial truth, just as a human; which makes it the perfect tool to solve real-life problems.

Additionally, several software programs and techniques support the AI technology cluster for more diverse and robust applications.

Graphical processing units (GPU) is core to AI as it provides the computing power required for iterative processing, as in training of neural networks.

The Internet of Things (IoT) is an indispensable element of the connected world. This generates voluminous data from connected devices and sensors. AI implementation in the IoT network allows using the data for insights and action.

Advanced search algorithms are key to AI implementation, with the intelligent processing of data. The capability of algorithm design is limitless, and its advantages of understanding complex systems to identify unique scenarios are considered indispensable. AI sifts through massive of data using fast, iterative and intelligent algorithms, which allow the program to self-learn from patterns or features in the data.

In summary, AI leverages the power of computing algorithms to model exactly how humans think, act, and agents should think or act

APIs, or application processing interfaces, are packages of code critical to AI functionality in products and software. They can add more value to AI capabilities with descriptions, and call outs.

Use cases of Artificial Intelligence

  • – Post Office – automatic sorting of mail for address recognition, knowledge-driven image interpretation for handwritten envelopes
  • – Credit card companies – automated fraud detection, scoring
  • – Utilities – automated voice recognition for inquiries
  • – Healthcare – personalized medicine recommendation, highly accurate x-ray and scan readings for diagnosis of critical and genetic illnesses
  • – Retail – personalized recommendations
  • – Automotive – self-driving cars
  • – Manufacturing – analyze factory IOT data to forecast load and demand; use sensor data for predictive maintenance especially of high-value assets like aircrafts
  • – Banking – track customer activity for red-flagging unusual or suspicious transactions; use of chatbots like HDFC “Eva” for handling customer queries; robotic AI software for automating various banking functions; automated loan application scoring; automated signature verification system
  • – Finance – analyze historical and real-time market data to predict trends in a fast-paced trading environment; identification of rogue trading practices and other misconduct; automation of underwriting and risk management
  • – Insurance – pattern recognition in user data to identify business opportunities and help reduce fraud; use client data from IoT network for developing best-fit insurance products
  • – E-commerce – identify high-value targets/customers to create leads, autonomous forklifts and robot warehouse workers are already retrieving boxes for shipment.
  • – Human Resource – applications can be filtered and contextual reasoning added for smart recruitments of the best-fit candidate for a job
  • – Home appliances – learn from user behavior and trends to perform smartly without human intervention
  • – Smartphones – personal assistants to provide answers
  • – Customer service – use chatbots for powerful customer engagement
  • – Business processes – with use of cognitive capture, records are stored digitally and processed for intuitive intelligence in case of management, for instance, contracts and FIRs.
  • – Video Games – generate responsive, intelligent behavior in non-player characters, similar to human intelligence
  • – Sports – use image capture for optimizing filed position and strategy
  • – Military – analyze military drone footage
  • – Internet – intelligent, pre-emptive and intuitive searches

The Future of Artificial Intelligence

The future of AI is marked with a race against time, as man strives to make machines more intelligent than humans! What was a fascinating aspect of science fiction has now become the most powerful technology disruptive everyday processes in industries and businesses, and human touchpoints? With continuous breakthroughs in AI research, across domains and use cases, AI is being implemented by one company after another, at a breakneck speed.

The Career Scope

Thus, AI is based on several disciplines that contribute to intelligent systems – mathematics, biology, logic/philosophy, psychology, linguistic, computer science, and engineering. You need to have a certain level of expertise in math, probability, statistics, algebra, calculus, logic, and algorithms.

Besides, these are areas, where you need to boost your AI learning, with courses or certifications in

  • Bayesian networking or graphical modeling; neural nets.
  • Physics, engineering, and robotics.
  • Computer science, programming languages, and coding.
  • Cognitive science theory.

Some of the popular job roles in AI in demand are

  • Software analysts and developers.
  • Computer scientists and engineers.
  • Algorithm specialists and trainers.
  • Mechanical engineers.
  • Manufacturing and electrical engineers.
  • Research scientists
  • Engineering consultants.
  • Military and aviation imagery analysts or engineers working with flight simulators, drones and armaments.
  • Robot personality designer and trainer
  • Autonomous vehicle designer

So retrofit yourself for a career in artificial intelligence. Learn artificial intelligence and make yourself ready for an AI-driven future.

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