Public:Parts of Training Data - Revision history

Mayank Gupta: Mayank Gupta moved page Parts of Training Data to Public:Parts of Training Data

2023-10-09T16:04:04Z

Mayank Gupta moved page Parts of Training Data to Public:Parts of Training Data

← Older revision

Revision as of 21:34, 9 October 2023

Tushar Goswami: /* Anatomy of Knowledge */

2023-10-09T13:36:54Z

Anatomy of Knowledge

← Older revision		Revision as of 19:06, 9 October 2023
Line 56:		Line 56:

	= Anatomy of Knowledge =		= Anatomy of Knowledge =
	Knowledge can be sub-divided into multiple parts.		<!-- Sadkirat to recommend infographic describing Propositions, and Logic -->Knowledge can be sub-divided into multiple parts.

	== Propositions ==		== Propositions ==
Line 94:		Line 94:

	= Applying DIKW in your AI project =		= Applying DIKW in your AI project =
	Whatever AI software you are building, conduct a Data Sprint and prototype the Training Data that your AI algorithms would use. Depending on how ambitious your AI project is sometimes you prototype only Propositions and no Logic. Other times you only do Low Level Logic, but not anything beyond that. Below are examples of some DIKW we ~~prototyped~~ in some AI projects		Whatever AI software you are building, conduct a Data Sprint and prototype the Training Data that your AI algorithms would use. Depending on how ambitious your AI project is sometimes you prototype only Propositions and no Logic. Other times you only do Low Level Logic, but not anything beyond that. Below are examples of some DIKW we prototype in some AI projects

	[https://docs.google.com/document/d/1DILPcDIPsqhAIcRqIUNK6J30aPydReaiArDysBRDJHk/edit?usp=sharing Personal Assistant for Learning, Time Managing, Chatting & Social Networking]		[https://docs.google.com/document/d/1DILPcDIPsqhAIcRqIUNK6J30aPydReaiArDysBRDJHk/edit?usp=sharing Personal Assistant for Learning, Time Managing, Chatting & Social Networking]

	= ~~Explainability~~ =		= Explain-ability =
	In typical AI projects around the world Data and Information layer are Transparent (often referred as white box), but Knowledge and Wisdom tend to be Opaque (often referred as black box). At Smarter.Codes the training data in Knowledge and Wisdom also tends to be transparent		In typical AI projects around the world Data and Information layer are Transparent (often referred as white box), but Knowledge and Wisdom tend to be Opaque (often referred as black box). At Smarter.Codes the training data in Knowledge and Wisdom also tends to be transparent

Tushar Goswami: /* IF you like it Ease */

2023-10-09T13:34:38Z

IF you like it Ease

← Older revision		Revision as of 19:04, 9 October 2023
Line 87:		Line 87:
	== Style of Response ==		== Style of Response ==

	==== IF you like it ~~Ease~~ ====		==== IF you like it Easy ====
	THEN choose the route of tolls		THEN choose the route of express way that contains tolls

	==== IF you want to optimize for frugality ====		==== IF you want to optimize for frugality ====

Tushar Goswami: /* Low Level Logic */

2023-10-08T02:25:05Z

Low Level Logic

← Older revision		Revision as of 07:55, 8 October 2023
Line 63:		Line 63:
	* RGB (255,0,0) is red		* RGB (255,0,0) is red
	* There are various [[:en:Shades_of_red\|shades of red]]		* There are various [[:en:Shades_of_red\|shades of red]]
	*

	== Logic ==		== Logic ==

	=== Low Level Logic ===		=== Low Level Logic ===

			==== Stop on Red signal ====

			* IF a traffic light IS red
			* AND you ARE driving on the road which HAS this traffic light
			* THEN Stop

	=== Strategical Logic ===		=== Strategical Logic ===

			==== IF it's a Sunny moment ====
			THEN Stop on Red Signal

			==== IF its a moment of Emergency ====
			(like a matter life and death - say you were driving an ambulance today and you have a patient to save) then

			* Drive on the Ambulance lane if available
			* Prefer NOT to Stop on Red Signal

	== Style of Response ==		== Style of Response ==

			==== IF you like it Ease ====
			THEN choose the route of tolls

			==== IF you want to optimize for frugality ====
			THEN choose the route without tolls, especially if additional fuel expense would be lesser than toll expense

	= Applying DIKW in your AI project =		= Applying DIKW in your AI project =

Tushar Goswami: /* Propositions */

2023-10-08T01:28:11Z

Propositions

← Older revision		Revision as of 06:58, 8 October 2023
Line 56:		Line 56:

	= Anatomy of Knowledge =		= Anatomy of Knowledge =
			Knowledge can be sub-divided into multiple parts.

	== Propositions ==		== Propositions ==
			Propositions are facts. Like

			* RGB (255,0,0) is red
			* There are various [[:en:Shades_of_red\|shades of red]]
			*

	== Logic ==		== Logic ==
Line 71:		Line 77:

	[https://docs.google.com/document/d/1DILPcDIPsqhAIcRqIUNK6J30aPydReaiArDysBRDJHk/edit?usp=sharing Personal Assistant for Learning, Time Managing, Chatting & Social Networking]		[https://docs.google.com/document/d/1DILPcDIPsqhAIcRqIUNK6J30aPydReaiArDysBRDJHk/edit?usp=sharing Personal Assistant for Learning, Time Managing, Chatting & Social Networking]

			= Explainability =
			In typical AI projects around the world Data and Information layer are Transparent (often referred as white box), but Knowledge and Wisdom tend to be Opaque (often referred as black box). At Smarter.Codes the training data in Knowledge and Wisdom also tends to be transparent

Tushar Goswami at 01:18, 8 October 2023

2023-10-08T01:18:08Z

← Older revision		Revision as of 06:48, 8 October 2023
Line 65:		Line 65:
	=== Strategical Logic ===		=== Strategical Logic ===

	=== ~~Response Style Logic~~ ===		== Style of Response ==

			= Applying DIKW in your AI project =
			Whatever AI software you are building, conduct a Data Sprint and prototype the Training Data that your AI algorithms would use. Depending on how ambitious your AI project is sometimes you prototype only Propositions and no Logic. Other times you only do Low Level Logic, but not anything beyond that. Below are examples of some DIKW we prototyped in some AI projects

			[https://docs.google.com/document/d/1DILPcDIPsqhAIcRqIUNK6J30aPydReaiArDysBRDJHk/edit?usp=sharing Personal Assistant for Learning, Time Managing, Chatting & Social Networking]

Tushar Goswami at 01:06, 8 October 2023

2023-10-08T01:06:09Z

← Older revision		Revision as of 06:36, 8 October 2023
Line 54:		Line 54:

	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.		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.

			= Anatomy of Knowledge =

			== Propositions ==

			== Logic ==

			=== Low Level Logic ===

			=== Strategical Logic ===

			=== Response Style Logic ===

Tushar Goswami at 01:01, 8 October 2023

2023-10-08T01:01:52Z

← Older revision		Revision as of 06:31, 8 October 2023
Line 8:		Line 8:

	= DIKW =		= DIKW =
	[[File:Interpretation of DIKW at Smarter.Codes.png\|thumb\|~~Interpretation of~~ DIKW at Smarter.Codes]]		[[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~~. The more creative sources you put to bring the data in, the more useful the AI software becomes. Thing of input source as sensory organ of your AI System~~. In the example of an Autonomous Driving car, data could be		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 ~~interpreted~~ called Information. In an Autonomous Driving car example, Information could be		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.

	~~Because it is pre-existing~~, ~~it may~~ be ~~believed that Knowledge~~ is the ~~actual Training Data. But Knowledge~~ is ~~"built" as more data is transformed into Information, and more Information is transformed into Knowledge~~. ~~Basically with experience the Knowledge gets better~~		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.

	~~== Wisdom ==~~		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.

Tushar Goswami: Created page with "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..."

2023-10-08T00:50:59Z

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..."

New page

[[File:Parts of a successful AI Software.png|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 Data to see how at Smarter.Codes we arrange training data

= DIKW =
[[File:Interpretation of DIKW at Smarter.Codes.png|thumb|Interpretation of 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].

== Data ==
Data is raw input. The more creative sources you put to bring the data in, the more useful the AI software becomes. Thing of input source as sensory organ of your AI System. 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

== Information ==
This raw data once understood is interpreted 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.

== 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 [[:en:Shades_of_red|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.

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.

Because it is pre-existing, it may be believed that Knowledge is the actual Training Data. But Knowledge is "built" as more data is transformed into Information, and more Information is transformed into Knowledge. Basically with experience the Knowledge gets better

== Wisdom ==