Blog 22: What is Artificial Intelligence?


Artificial intelligence (AI), also known as machine intelligence, is a branch of computer science that focuses on inventing, building, and supervising technology that can learn to independently make decisions and carry out actions just like a human being.

AI is also defined as,

  • An Intelligent Entity Created By humans
  • Capable of Performing Tasks intelligently without being explicitly instructed.
  • Capable of thinking and acting rationally and humanely.

AI cannot be considered as a single technology, but rather as an umbrella that includes any type of software or hardware component that supports machine learning, computer vision, natural language understanding (NLU), and natural language processing (NLP). Artificial intelligence is progressing significantly from SIRI to self-driving cars. It can be seen everywhere. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous weapons.

AI is currently divided into two major categories.

  • NARROW AI is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car).
  • GENERAL AI is not yet achieved but engineers and scientists are working to achieve a General AI level. This AI is being designed to outperform humans at whatever its specific task is, like playing chess or solving equations.

Today’s AI uses conventional CMOS hardware and the same basic algorithmic functions that drive traditional software. Future generations of AI are expected to inspire new types of brain-inspired circuits and architectures that can make data-driven decisions faster and more accurately than a human being can.

What is Artificial Intelligence?

If you ask about artificial intelligence an AI researcher would say that it’s a set of algorithms that can produce results without having to be explicitly instructed to do so. The intelligence demonstrated by machines is known as Artificial Intelligence.

Artificial Intelligence has grown to be very popular in today’s world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow, they will have a great impact on our quality of life. It’s natural that everyone today wants to connect with AI technology somehow, may it be as an end-user or pursuing a career in artificial intelligence.

How did the terminology AI come into existence?

The terminology AI came into existence not less than a decade later after helping the Allied forces win World War II by breaking the Nazi encryption machine Enigma, mathematician Alan Turing changed history a second time with a simple question:

“Can machines think?”

Turing’s 1950 paper “Computing Machinery and Intelligence” and its subsequent Turing Test established the fundamental goal and vision of AI.   

Since then scientists and engineers are trying to answer Turing’s question and trying to replicate or simulate human intelligence in machines. The expansive goal of AI has given rise to many questions and debates. So much so that no singular definition of the field is universally accepted.

How does Artificial Intelligence (AI) Work?

Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using its computational powers to surpass what we are capable of. To understand How Artificial Intelligence actually works, one needs to deep dive into the various sub-domains of Artificial Intelligence and understand how those domains could be applied to the various fields of the industry.

AI is often simply referring as one component of AI, such as machine learning. AI rather requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R, and Java, are popular.

In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.

AI programming focuses on three cognitive skills: learning, reasoning, and self-correction.

  • Learning processes. This aspect of AI programming focuses on acquiring data and creating rules for how to turn the data into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
  • Reasoning processes. This aspect of AI programming focuses on choosing the right algorithm to reach the desired outcome.
  • Self-correction processes. This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.

Some components of AI are:

  • Machine Learning: ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns and analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data saves human time for businesses and helps them make better decisions.
  • Deep Learning: Deep Learning is an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome.
  • Neural Networks: Neural Networks work on similar principles to Human Neural cells. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain does.
  • Natural Language Processing: NLP is the science of reading, understanding, and interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly.
  • Computer Vision: Computer vision algorithms try to understand an image by breaking down the image and studying different parts of the object. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations.
  • Cognitive Computing: Cognitive Computing algorithms try to mimic a human brain by analyzing text/speech/images/objects in a manner that a human does and tries to give the desired output.

What are the 4 types of artificial intelligence?

AI can be categorized into four types as explained by Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, in his article written in 2016. The categories are as follows:

  • Type 1: Reactive machines. These AI systems have no memory and are task-specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
  • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
  • Type 3: Theory of mind. Theory of mind is a psychological term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
  • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.


  • Good at detail-oriented jobs;
  • Reduced time for data-heavy tasks;
  • Delivers consistent results; and
  • AI-powered virtual agents are always available.
  • Reduction in human error
  • Available 24×7
  • Helps in repetitive work
  • Digital assistance 
  • Faster decisions
  • Rational Decision Maker
  • Medical applications
  • Improves Security
  • Efficient Communication


  • Expensive;
  • Requires deep technical expertise;
  • A limited supply of qualified workers to build AI tools;
  • Only one knows what it’s been shown; and
  • Lack of ability to generalize from one task to another.

Applications of Artificial Intelligence:

The applications for artificial intelligence are endless. The technology can be applied to many different sectors and industries. AI is being tested and used in the healthcare industry for dosing drugs and conducting different treatments tailored to specific patients, and for aiding in surgical procedures in the operating room.

Other examples of machines with artificial intelligence include computers that play chess and self-driving cars. Each of these machines must weigh the consequences of any action they take, as each action will impact the end result. In chess, the end result is winning the game. For self-driving cars, the computer system must account for all external data and compute it to act in a way that prevents a collision.

Artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity in banking and finance such as unusual debit card usage and large account deposits—all of which help a bank’s fraud department. Applications for AI are also being used to help streamline and make trading easier. This is done by making the supply, demand, and pricing of securities easier to estimate.

Examples of AI:

  • Facebook Watch
  • Facebook Friends Recommendations
  • Siri, Alexa, and other smart assistants
  • Self-driving cars
  • Robo-advisors
  • Conversational bots
  • Email spam filters
  • Netflix’s recommendations
  • Proactive healthcare management
  • Disease mapping
  • Automated financial investing
  • Virtual travel booking agent
  • Social media monitoring

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