Public:Parts of Training Data: Difference between revisions
(Created page with "thumb|Parts of a successful AI Software A successful AI software is made up of two parts. Half of it is AI algorithms, and rest half is Training Data. This training data is fine-tuned such that the AI algorithm.. * ..can feed upon the supplied training data and make AI software act smart. * ..can add more and more training data on its own, and make itself even more smarter In this article we will zoom inside the Training D...") |
No edit summary |
||
| Line 8: | Line 8: | ||
= DIKW = | = DIKW = | ||
[[File:Interpretation of DIKW at Smarter.Codes.png|thumb| | [[File:Interpretation of DIKW at Smarter.Codes.png|thumb|DIKW at Smarter.Codes]] | ||
At Smarter,Codes we divide the the Training Data into 4 parts : Data, Information, Knowledge, Wisdom. Inspired from [https://en.m.wikipedia.org/wiki/DIKW_pyramid DIKW pyramid]. | At Smarter,Codes we divide the the Training Data into 4 parts : Data, Information, Knowledge, Wisdom. Inspired from [https://en.m.wikipedia.org/wiki/DIKW_pyramid DIKW pyramid]. | ||
== Data == | == Data == | ||
Data is raw input | Data is raw input. In the example of an Autonomous Driving car, data could be | ||
* Color code : RGB (255, 0, 0). Sourced from Camera | * Color code : RGB (255, 0, 0). Sourced from Camera | ||
* Geographical Coordinates : 41°24'12.2"N 2°10'26.5"E. Sourced from GPS module | * Geographical Coordinates : 41°24'12.2"N 2°10'26.5"E. Sourced from GPS module | ||
* The direction our Camera 1 is looking at is 15°. Sourced from Compass | * The direction our Camera 1 is looking at is 15°. Sourced from Compass | ||
The more creative sources you put to bring the data in, the more useful the AI software becomes. Think of input source as sensory organ of your AI software. The more sensory organs you have, the better. | |||
== Information == | == Information == | ||
This raw data once understood is | This raw data once understood is called Information. | ||
In an Autonomous Driving car example, Information could be | |||
* Color is Red. This Color Red is of Traffic Light. | * Color is Red. This Color Red is of Traffic Light. | ||
| Line 25: | Line 28: | ||
* We are seeing the traffic light facing north. | * We are seeing the traffic light facing north. | ||
* This particular traffic light is signaling our car. | * This particular traffic light is signaling our car. | ||
It stores the interpretation of stimuli. It often contains facts about the current environment. | |||
== Knowledge == | == Knowledge == | ||
Information is deduced from Data by referring to pre-existing Knowledge. In an Autonomous Driving car example, Knowledge can be | Information is deduced from Data by referring to pre-existing Knowledge. | ||
In an Autonomous Driving car example, Knowledge can be | |||
* RGB (255, 0, 0) is given the name Red. Traffic lights tend of have Red color signal. | * RGB (255, 0, 0) is given the name Red. Traffic lights tend of have Red color signal. | ||
| Line 35: | Line 41: | ||
* You are supposed to stop at traffic lights while the signal is red. | * You are supposed to stop at traffic lights while the signal is red. | ||
Because it is pre-existing knowledge, it is easy to believe that Knowledge is the Training. But the more Data comes, and more Information is made, it makes Knowledge better. Hence the steps before Knowledge : Information and Data also part of Training. | It guides how to interpret stimuli. And how to respond back to stimuli. Because it is pre-existing knowledge, it is easy to believe that Knowledge is the Training. But the more Data comes, and more Information is made, it makes Knowledge better. Hence the steps before Knowledge : Information and Data also part of Training. | ||
== Wisdom == | == Wisdom == | ||
Wisdom is output of Knowledge applied on Information. | Wisdom is output of Knowledge applied on Information. | ||
In an Autonomous Driving car example, Wisdom can be | |||
* I must stop my car while the traffic signal is red. | |||
* Because | |||
** I am facing a traffic light on Acme Street cross road. | |||
** And you must stop your car when the signal is red. | |||
It is the decisions that system has taken based on the Information that came in. It also [[:en:Explainable_artificial_intelligence|explains]] why the decision was taken. | |||
Revision as of 06:31, 8 October 2023
A successful AI software is made up of two parts. Half of it is AI algorithms, and rest half is Training Data. This training data is fine-tuned such that the AI algorithm..
- ..can feed upon the supplied training data and make AI software act smart.
- ..can add more and more training data on its own, and make itself even more smarter
In this article we will zoom inside the Training Data to see how at Smarter.Codes we arrange training data
DIKW
At Smarter,Codes we divide the the Training Data into 4 parts : Data, Information, Knowledge, Wisdom. Inspired from DIKW pyramid.
Data
Data is raw input. In the example of an Autonomous Driving car, data could be
- Color code : RGB (255, 0, 0). Sourced from Camera
- Geographical Coordinates : 41°24'12.2"N 2°10'26.5"E. Sourced from GPS module
- The direction our Camera 1 is looking at is 15°. Sourced from Compass
The more creative sources you put to bring the data in, the more useful the AI software becomes. Think of input source as sensory organ of your AI software. The more sensory organs you have, the better.
Information
This raw data once understood is called Information.
In an Autonomous Driving car example, Information could be
- Color is Red. This Color Red is of Traffic Light.
- This Geo coordinate is of Acme Street's cross road.
- We are seeing the traffic light facing north.
- This particular traffic light is signaling our car.
It stores the interpretation of stimuli. It often contains facts about the current environment.
Knowledge
Information is deduced from Data by referring to pre-existing Knowledge.
In an Autonomous Driving car example, Knowledge can be
- RGB (255, 0, 0) is given the name Red. Traffic lights tend of have Red color signal.
- Also there are many types of red. It is okay if a shade of red is assumed as red if seen on a traffic light.
- Geo coordinate 41°24'12.2"N 2°10'26.5"E (with 5% variance) means we are Acme Street's cross road.
- Given that we are looking in the direction of 0° to 30° this means we are facing to North.
- You are supposed to stop at traffic lights while the signal is red.
It guides how to interpret stimuli. And how to respond back to stimuli. Because it is pre-existing knowledge, it is easy to believe that Knowledge is the Training. But the more Data comes, and more Information is made, it makes Knowledge better. Hence the steps before Knowledge : Information and Data also part of Training.
Wisdom
Wisdom is output of Knowledge applied on Information.
In an Autonomous Driving car example, Wisdom can be
- I must stop my car while the traffic signal is red.
- Because
- I am facing a traffic light on Acme Street cross road.
- And you must stop your car when the signal is red.
It is the decisions that system has taken based on the Information that came in. It also explains why the decision was taken.