Brief of Knowledge Engine (KE)
Knowledge Engine is for people to work with their consciousness in this digital age, for learning, team cooperation, project management, time management and so on, although currently its major focus is on learning. Or you can try it on this self-learning platform.
Knowledge Engine is built on top of Significant Experiences, which we deem as the basic units of human knowledge. Significant Experiences (SEs) are those experiences that can help you achieve a better understanding on things that you are intersted. SEs are associated with each other by tags, and tree-like frames are used to represent the “containing” relationship among knowledge, which we deem as the most basic relationship of knowledge.
SEs are basic units of human knowledge just like cells are the basic units of human body. Although cells of different tissues can be very different in various ways, their basic componnents stay the same. So as the basic units, the SEs can be composed together in various ways to build up higher level of structured knowledge, just like structured tissues and organs of human body, and these SEs and structured knowledge can thus paticipate in various activities related to knowledge, such as learning, teaching, project management, writing a blog or a book, making a ppt and so on.
SEs should be caputured promptly from various contexts, and the context should be captured automatically as well. So when adding a SE, Knowledge Engine should support adding of various contexts as well.
Tags are used as the most basic relation among SEs. Using of tags allow very dynamic and flexible organizing of SEs, and thus can support powerful things like group contribution. However, in addtion to these dynamic and free bottom up approaches, upper level mechanisms can be added to support having a better top down control on the tags used. For example, for personal SEs, a top down tag tree, mechasims that support quick merge or removal of tags, a group admin’s managing of group tags (possibly setting tags on different levels). AI can be used to recommend tags to be used for SEs.
Frames as the reprentation of the basic “containing” relationship among knowledge, it should allow embedding of frames inside a frame so a frame can be expanded continuously.
Frame can be built up manually or dynamically (by putting a folder inside a frame).
- Significant Experiences
Significant Experiences(SEs) are regarded by us as the basic units of knowledge, like cells in biological creatures. Significant Experiences are like Ray Kurzweil’s “Qualia”(Consciousness Experiences). SEs have self-vote of importance and usefulness vote by others, which are like the weights in biological neural networks.
The basic relation btw knowledge is “containing”. Frame is the embodiment of such “containing” relation, it constructs life centers at the higher level and is capable to engage in large parallel computation and pattern recognition.
Tag establish connections btw knowledge (like synapses rewiring)
Tag Tree is to extract out abstract knowledge structure or patterns (we think it is better to use Life Centers instead of patterns) through SEs.
For a period of time, you can focus on working in a certain WorkingSet, which is a set of tags in KE.
When working with a lot of SEs, cache helps to store some SE ids temporarily.
Dynamic folders that retrieve SEs by pre-configured search terms.
Structured Knowledge on top of SEs
- Learning Areas
Learning Area is a large domain of learning, such as Software Programming. It automatically collects your SEs and structured knowledge in an area for knowledge display and discovery.
- Learning Groups
Learning Groups are groups of learners who are interested in the same topic of learning. Some users in the group can have some teaching roles.
- Learning Salons
Learning activities organized based on knowledge structures from learning groups.
- Learning Plans
Regular plans (weekly and monthly) and reviews, together with scheduled reminders, forming good learning habits.
Workflows to work with lists of SEs to accomplish learning and thinking processes. Workflows can be shared to spread good learning methods.
Core functions and beliefs
- Quick capture of SEs in various contexts
Quickly note down SEs at the moment without interrupting current work in hands. Various contexts can be automatically appended to the SEs.
- Quick and flexible display of SEs and knowledge
Knowledge at your fingertips.
- Cultivation of self-reflection skills
Self-reflection makes consciousness grow, from drops of SEs to understanding of the whole.
- Learning or thinking accomplished through simple editing
Learning or thinking can be done in layers and steps, so it can be transformed into many short steps of simple editing operations, making it a highly efficient process with minimum thinking. One task a time, zoom in.
- Workflows helps you learn and think efficiently
Learning and thinking workflows can be configured and shared.
- Learning Plans cultivate good learning habits
Weekly plans, monthly plans, yearly plans. Reviews and stats.
- Visualization of knowledge and brain
- Smooth transitioning btw learning and teaching. Teach effectively
Rendering of one’s knowledge and instructing in a finer grain.
- Generating knowledge of conventional forms smoothly
wiki, pdf, ppt/slide, blog, book, and so on
- Powering and transforming various types of knowledge related activities
Knowledge is flowing in all kinds of activities, and all have “Learning Embedded”.
- Knowledge needs to be applied, and knowledge is everywhere
Knowledge formed from KE can be shared to various social networking sites with one click, with url to reference back and interact.
Ideas behind and Design Concerns/Challenges
- Engine in the knowledge era
As the engine in the industrial age powers automobiles, ships, airplanes, and razors, the engine in the knowledge era powers learning, teaching, blog writing, paper publication, book writing, project management and so on.
- Instill wisdom into the machine from the human crowd
By taking drops of wisdom from billions of human beings, the machine can have intelligence, like PageRank provides search service by absorbing the wisdom of the crowd.
- Fragmented Learning
Fragmented Learning is the truth of real life learning. Learning should never be separated from life and from play. KE makes it easier for everyone to learn while play.
- Self-Directed Learning or Selflearning
There is no learning other than Self-Directed Learning or Selflearning.
- Understanding of Consciousness
Our understanding of consciousness，how this understanding applies to the design of KE，and similarity with Ray Kurzweil’s PRTM. Briefly，consciousness is hierarchical lists on top of SEs.
- About tags
The flexibility of tags and how to manage it. Tag Tree represents abstract knowledge structure, and it can be applied to the KE itself, thus fulfilling a self-reference system.
Current knowledge produced (in progress)
- Knowledge Frame
Life Framework Theory, here is its introductory essay. you can also browse other knowledge structures
- Tag Tree
Education，Software Programming, History
- Learning Area
Learning，Education，Real Life Learning，Consciousness，Software Programming，Zen，Soccer
- Application of abstract knowledge
Abstract patterns (life centers) can be extracted from SEs, and these patterns can again be used in various learning areas. Such as Life Framework applied to Learning, applied to Education, applied to Consciousness, applied to Soccer. Abstract knowledge can even be applied to the KE itself. For example, you can configure the system root knowledge. Such root knowledge can be applied to your newly generated learning area. KE itself has a lot of internal structure built based on its self-produced knowledge. KE is a self-reference system。