There’s a lot of buzz around that what is artificial intelligence at the moment, and in the term, AI seems to be thrown around a lot but what is it exactly. To clear things up around this topic, first of all.

Let’s look at the definition to avoid confusion we have to go back to the earliest and hence purest definition of AI from the time when it was first defined. the official idea and definition of AI were defined by Jay McCartney in 1955 at the Dartmouth conference. Of course, those plenty of research work done on AI by others such as Alan Turing before this, but what they were working on was an undefined field before 1955. So here’s what McCarthy proposed every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate. It an attempt will be made to find out how to make machines use language form abstractions and concepts solve kinds of problems now reserved for humans and improve themselves.
translation, in essence, AI is a machine with the ability to solve problems that are usually done by us humans with our natural intelligence
A computer would demonstrate a form of intelligence when it learns how to improve itself at solving these problems. To elaborate further the 1955 proposal defines seven areas of AI today they’re surely more but here are the original seven.

  1. Simulating higher functions of the human brain.
  2. Programming a computer to use general language.
  3. Arranging hypothetical neurons in a manner enabling them to form concepts.
  4. An away to determine and measure problem complexity.
  5. Self-improvement.
  6. abstraction defined as the quality of dealing with ideas rather than events.
  7. Randomness and creativity

After 60 years, I think we’ve completed the language measure problem complexity and self-improvement to at least some degree however randomness and creativity are just starting to be explored. We’ve seen a couple of web episode scripts short films and even a feature-length movie co-written or entirely written by AI.

Every aspect of learning or any other feature of intelligence can, in principle, be so precisely described that a machine can be made to simulate it. So in the definition, you see the word “intelligence”- what is the intelligence?

well according to Jack Copeland who has written several books on AI some of the essential factors of intelligence are:

Generalization Learning

learning that enables the learner to be able to perform better in situations not previously encountered.


To reason is to conclude appropriate to the situation in hand.


Given such and such data find X.


Analyzing ask and environment and analyzing features and relationships between objects self-driving cars are an example.

Language understanding

Understanding language by following syntax and other rules similar to a human.

So now we have an understanding of AI and intelligence to bring it together a bit and solidify the concept in your mind of what AI is here’s a few examples of AI:
machine learning, computer vision, natural language processing, robotics, pattern recognition, and knowledge management.

There are also different types of artificial intelligence in terms of approach, for example, strong AI and weak AI. strong AI is simulating the human brain by building systems that think and in the process give us an insight into how the brain works we’re nowhere near the stage yet weak AI is a system that behaves like a human but doesn’t give us an insight into how the brain work IBM’s deep blue a chess-playing AI was an example it processed millions of moves before it made any actual moves on the chessboard.

It doesn’t stop there though they’re a new kind of middle ground between strong and weak AI. This is where a system is inspired by human reasoning but doesn’t have to stick to it IBM’s Watson. There’s an example humans it reads a lot of information recognizes patterns and builds up evidence to say hey I’m X percent confident that this is the right solution to the question that you have asked me.

Google’s deep learning is similar as it mimics the structure of the human brain by using neural networks but doesn’t follow its function exactly the system uses nodes that act like artificial neurons connecting information going a little bit deeper neural networks are actually a subset of machine learning so what’s machine learning then machine learning refers to algorithms that enable the software to improve its performance over time as it obtains more data. This is programming by input-output examples rather than just coding.

So that this makes more sense, let’s use an example a programmer would have no idea how to program a computer to recognize a dog. Still, he can create a program with a form of intelligence that can learn to do so if he gives the program enough image data in the form of dogs and let it process and learn when you provide the program with an image of a new dog that it’s never seen before it would be able to tell that it’s a dog with relative ease.

Before we finish what is artificial intelligence, just one last concept most artificial intelligence algorithms are expert systems.

Expert System - what is artificial intelligence

so what’s an expert system the often-cited definition of an expert system is as follows an expert system is a system that employs human knowledge in a computer to solve problems that ordinarily inquire human expertise it’s the practical application of a knowledge database we’ve arguably only just got the first proven nonexpert system in 2016 Deep mind’s AlphaGo. AlphaGo is not an expert system meaning that its algorithms could be used and applied to other things.

Must Read: Future Of Animation With AI

Demis Hassabis, he was the co-creator of DeepMind, highlighted this in a Google Blog.

“We are thrilled to have mastered Go and thus achieved one of the grand challenges of however the most significant aspect of all of this for us is that AlphaGo isn’t just an expert system built on handcrafted rules instead it uses general machine learning techniques to figure out for itself how to win to go.” He said “because the methods we’ve used a general-purpose we hope that one day they could be extended to help us address some of society’s toughest and most pressing problems from climate modeling to complex disease analysis
in other words, the algorithms they AlphaGo used to win could serve as a basis to be applied to very complex problems.”

Now we had enough knowledge about what is artificial intelligence. Please comment and let us know what do you think.


Please enter your comment!
Please enter your name here