Artificial Intelligence Explained - Micra Cloud Services

There’s a lot of buzz around Artificial Intelligence (AI) at the moment and the term AI seems to be thrown around a lot but, what is it exactly? To clear things up, 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 coined, the official idea and definition of AI were first coined by Jay McCartney in 1955 at the Dart mouth 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.  Okay 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.”


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In essence, AI is a machine with the ability to solve problems, which 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 in solving these problems. To elaborate further, the 1955 proposal defines seven areas of AI.


The Original Seven Aspects of A.I. (1955):


  • Today there surely more but here are the original seven:
  • Simulating higher functions of the human brain
  • Programming a computer to use general language
  • Arranging hypothetical neurons in a manner enabling them to form concepts
  • For a way to determine and measure problem complexity
  • Self-improvement
  • Abstraction defined as the quality of dealing with ideas rather than events
  • Randomness and creativity


After 60 years, I think realistically 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 this year.

Now, let’s move on to know why Artificial and the word Intelligence have brought together.


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Okay, so in the definition, you heard the word intelligence. What is the intelligence? Well according to Jack Copeland who has written several books on AI, some of the most important factors of intelligence are generalization learning.


Generalization Learning: It is learning that enables the learner to be able to perform better in situations not previously encountered.

Reasoning: To reason is to draw conclusions appropriate to the situation in hand

Problem-solving: given such and such data find X

Perception: analyzing asks 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

Okay 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 are a few examples of AI.


  • Machine learning
  • Computer vision
  • Natural language processing
  • Robotics
  • Pattern recognition
  • Knowledge management




There are also different types of artificial intelligence in terms of approach, for example, the strong AI and weak AI. Strong AI is simulating the human brain by building systems, which 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 works.

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, there’s actually a new kind of middle ground between strong and weak AI, and this is where a system is inspired by human reasoning but doesn’t have to stick to it.

IBM’s Watson is an example, like 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 from the information that I’ve read. If you want to know more about IBM Watson you can click on the annotation now or the link in the description below”.

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, which act as artificial neurons connecting information. Going a little bit deeper, neural networks are actually a subset of machine learning.




Machine learning refers to algorithms that enable software to improve its performance over time as it obtains more data. This is programming by input-output examples rather than just coding.

So, this makes more sense. Let’s use an example. A programmer would have no idea how to program a computer to recognize a dog but, he can create a program with a form of intelligence, which 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 give the program, an image of a new dog that it’s never seen before, would be able to tell that it’s a dog with relative ease.


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Okay so, before we finish just one last concept. Most artificial intelligence algorithms are expert systems. 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, which ordinarily inquire human expertise.” Basically, it’s the practical application of a knowledge database.

We’ve arguably only just got the first proven non-expert system, this year, Deep mind’s Alpha go.  Alpha go is not an expert system meaning that its algorithms could be used and applied to other things Demis Hassabis was the co-creator of D mind highlighted this in a Google Blog.

“We are thrilled to have mastered go and thus achieved one of the grand challenges of AI. However, the most significant aspect of all of this for us is that alpha go 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 at Go.”

He goes on “Because the methods we’ve used a general-purpose, our hope is 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.” And, in other words, “The algorithms they alpha go used to win go, could serve as a basis to be applied to very complex problems.



All right, so to bring this all together and summarize all that we’ve learned, let’s recap.  So, what is AI? Commonly AI or Artificial Intelligence is a machine or a computer program, which learns how to do tasks that require forms of intelligence and are usually done by humans and, the other thing to take away, intelligence comes in many forms and has many different aspects at this time we just have many different types of AIs that are good, a particular subsets of Intelligence. I hope that clears things up what AI actually is and other things as well.

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