Vision Behind Knowledge Engine
Knowledge And Information:
There is difference between information that computer store and process and knowledge that human process and think. Information is bound within framework of formats like image formats (JPEG, BMP, GIF, ..), video formats (MP4, 3GP, AVI, ..) and so on.. whereas we (humans) do not need any fixed format to process knowledge, facts are flowing freely in our mind without any boundaries or restrictions of format. We can structure the same information as vision (image+video), audio or text. Thought process is continuously digesting the information in our mind, trying to co-relate information, restructure it and evolve into better formats of knowledge. In the language of computer science, we can say, thought is an unified format that is at the top of all other data structures. The question that remains unanswered to all of us is what guides our thinking process.
Wiki Says About Machine Learning and Computational Creativity:
Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions. Computational creativity is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts. The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends:
1. To construct a program or computer capable of human-level creativity.
2. To better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans.
3. To design programs that can enhance human creativity without necessarily being creative themselves.
My Perspective About Machine Creativity:
A computer by itself can never be creative, because it always goes by a fixed formula. Creativity has no fixed formula. One is required to think out of the box to be really creative. Machine will be required to try random patterns of code for a problem and learn from them to get the best solution. In the language of machine learning, hypothesis space needs to be expanded to create new patterns of data from observed patterns.
Vision behind Knowledge Engine:
Knowledge Engine aims to create knowledge representations that are self-adaptive, flexible (like human knowledge) and can evolve and digest the new information as knowledge, with interaction to multiple users over the web interface.
This knowledge will act as an expert system that can be used for online learning and interactive problem solving in engineering, finance, medical and research domain.
Philosophy behind Knowledge Engine:
Machines have capability to store and process large amount of data. We have capability to understand our world, model our problems into computer and get better solutions from knowledge of science, engineering or social domains.
Role of Knowledge Engine:
The role of computer in problem solving has changed from 'calculator' to 'data processing equipment' in last few decades (1950's to 2010). Knowledge engine is an attempt to get a step ahead in this model where computers not only understand data getting processed, but the boundaries between data and code itself is vanished. Data will generate new code and drive new thoughts into computer (data driven complexity) or computer will generate new code by using creativity on learned code (code driven complexity).
Challenge of Knowledge Engine:
To define some basic rules that govern the knowledge digestion and creation process to drive new thoughts and collaborate with humans with minimum friction of human computer interface.
In the next blog, we will see the state of the art tools and technologies for knowledge engine in different domains.
Knowledge And Information:
There is difference between information that computer store and process and knowledge that human process and think. Information is bound within framework of formats like image formats (JPEG, BMP, GIF, ..), video formats (MP4, 3GP, AVI, ..) and so on.. whereas we (humans) do not need any fixed format to process knowledge, facts are flowing freely in our mind without any boundaries or restrictions of format. We can structure the same information as vision (image+video), audio or text. Thought process is continuously digesting the information in our mind, trying to co-relate information, restructure it and evolve into better formats of knowledge. In the language of computer science, we can say, thought is an unified format that is at the top of all other data structures. The question that remains unanswered to all of us is what guides our thinking process.
Wiki Says About Machine Learning and Computational Creativity:
Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions. Computational creativity is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts. The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends:
1. To construct a program or computer capable of human-level creativity.
2. To better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans.
3. To design programs that can enhance human creativity without necessarily being creative themselves.
My Perspective About Machine Creativity:
A computer by itself can never be creative, because it always goes by a fixed formula. Creativity has no fixed formula. One is required to think out of the box to be really creative. Machine will be required to try random patterns of code for a problem and learn from them to get the best solution. In the language of machine learning, hypothesis space needs to be expanded to create new patterns of data from observed patterns.
Vision behind Knowledge Engine:
Knowledge Engine aims to create knowledge representations that are self-adaptive, flexible (like human knowledge) and can evolve and digest the new information as knowledge, with interaction to multiple users over the web interface.
This knowledge will act as an expert system that can be used for online learning and interactive problem solving in engineering, finance, medical and research domain.
Philosophy behind Knowledge Engine:
Machines have capability to store and process large amount of data. We have capability to understand our world, model our problems into computer and get better solutions from knowledge of science, engineering or social domains.
Role of Knowledge Engine:
The role of computer in problem solving has changed from 'calculator' to 'data processing equipment' in last few decades (1950's to 2010). Knowledge engine is an attempt to get a step ahead in this model where computers not only understand data getting processed, but the boundaries between data and code itself is vanished. Data will generate new code and drive new thoughts into computer (data driven complexity) or computer will generate new code by using creativity on learned code (code driven complexity).
Challenge of Knowledge Engine:
To define some basic rules that govern the knowledge digestion and creation process to drive new thoughts and collaborate with humans with minimum friction of human computer interface.
In the next blog, we will see the state of the art tools and technologies for knowledge engine in different domains.
No comments:
Post a Comment