Artificial Intelligence :
There is a lot of buzz around these days, about the artificial intelligence, and the term AI seems to be thrown around a lot, but what is it exactly ? 🧐
to clear things up, I will just explaining the things in brief here.
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| Artificial Intelligence |
First of all, lets look at the definition
To avoid confusion we have to go back to the earliest and hence purest
definition of AI was first coined by Jay McCartney in 1955 at Dartmouth Conference.
Of course those plenty of research work done on AI by others such 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 how to make machines use language, from abstractions and concepts, solve kind of problems now reserved for humans, and improve themselves"
AI is a machine with the ability to solve problems that are usually done by 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 7 areas of AI.
Today there are surely more but here are the original 7
- Simulating higher functions of the human brain.
- Programming a computer to use general language.
- Arranging hypothetical neurons in a manner so that they can form concepts
- 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.
So in the definition, you can see the word "Intelligence"
now, what is intelligence ?
Well! according to Jack Copeland, Who has written several books on AI, Some of the most important factors of intelligence are
- Generalization learning: that is learning that enables the learner to be able to perform better in situations not previously encountered
- Reasoning: to reason to draw conclusions appropriate to the situation in hand
- Problem solving: given such and such data find x
- perception: analyzing an environment features and relationships between objects.
- Language Understanding : Understanding a language by following syntax and other rules similar to a human
Okay, So now we have an understanding of AI and Intelligence,
now lets bring it together a bit and solidify the concepts in your mind of what AI is here's few example of AI
Machine Learning, Computer Vision, Natural language processing, Robotics, Pattern recognition and knowledge management
there are also different kind of Artificial Intelligence in terms of approach.
For example : Strong AI & Weak AI
Strong AI: It is a system formed by simulating the human brain by building systems that thinks and the process, give us an insight into how the brain works ( we are nowhere near the stage yet 😅)
Weak AI: It is a system that behaves like a human but doesn't give us a insight into how the brain works ,
IBM's Deep Blue a chess playing AI was an example of it.
It processed millions of moves before it made any actual moves on the chessboard,
Weak AI achieves only the result of a human not the actual process
It doesn't stop here either..
there is actually 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 is an example of it
( Like humans it reads a lot of information recognizes patterns and builds up evidence to say, " hey I am x percent confident that this is the right solution to the question that you have asked me from the information that I have read ")
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 acts as 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 software to improve its performance over the time, as it obtains more data about usage pattern.
Strong AI: It is a system formed by simulating the human brain by building systems that thinks and the process, give us an insight into how the brain works ( we are nowhere near the stage yet 😅)
Weak AI: It is a system that behaves like a human but doesn't give us a insight into how the brain works ,
IBM's Deep Blue a chess playing AI was an example of it.
It processed millions of moves before it made any actual moves on the chessboard,
Weak AI achieves only the result of a human not the actual process
It doesn't stop here either..
there is actually 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 is an example of it
( Like humans it reads a lot of information recognizes patterns and builds up evidence to say, " hey I am x percent confident that this is the right solution to the question that you have asked me from the information that I have read ")
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 acts as 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 software to improve its performance over the time, as it obtains more data about usage pattern.
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