Difference between revisions of "Meta University"
| Line 2: | Line 2: | ||
=Context= | =Context= | ||
As [[Moore's Law]] opened up the flood gate of data processing powers to the public, the world also needs an intellectual framework that can cope with this unprecedented public data availability. To prepare people everywhere to participate in this scientific revolution, a science that deals with data at large, there needs to be a core curriculum that can guide them to enter this data-intensive era, by accessing public data and guarding their own data assets. MU is an effort to re-organize knowledge production process based on [[Data Science]]. It defines a curriculum to help anyone who wish to benefit from | As [[Moore's Law]] opened up the flood gate of data processing powers to the public, the world also needs an intellectual framework that can cope with this unprecedented public data availability. To prepare people everywhere to participate in this scientific revolution, a science that deals with data at large, there needs to be a core curriculum that can guide them to enter this data-intensive era, by accessing public data and guarding their own data assets. MU is an effort to re-organize knowledge production process based on [[Data Science]]. It defines a curriculum to help anyone who wish to benefit from [[intuitive data presentation]] on pervasive computing devices, and [[highly-available]] data content, a universally impactful vision that was first outlined by [[Gordon Moore]]<ref>{{:Paper/Cramming more components onto integrated circuits}}</ref>. | ||
=Goal Statement= | =Goal Statement= | ||
Revision as of 08:34, 5 February 2022
Meta University(MU), is an abstract specification of idealized universities[1]. It is an implementation-neutral document that welcomes any attempts of concrete implementations of this specification. This specification is intended to be universally applicable to all learning organizations.
Context
As Moore's Law opened up the flood gate of data processing powers to the public, the world also needs an intellectual framework that can cope with this unprecedented public data availability. To prepare people everywhere to participate in this scientific revolution, a science that deals with data at large, there needs to be a core curriculum that can guide them to enter this data-intensive era, by accessing public data and guarding their own data assets. MU is an effort to re-organize knowledge production process based on Data Science. It defines a curriculum to help anyone who wish to benefit from intuitive data presentation on pervasive computing devices, and highly-available data content, a universally impactful vision that was first outlined by Gordon Moore[2].
Goal Statement
The goal of MU is to provide the following tri-fold knowledge organization framework:
- Establish a sound Cognitive Foundation on Data: Data Structures and Algorithms to process the data.
- Support effective Decisions with Computational Thinking: Making sense of data with the help of mental models and computing tools.
- Explore knowledge in all Frontiers: Evangelize knowledge and data verification/validation techniques to anyone who need it.
All the above three goals should be unified with an extensible knowledge representation, retainment, and revisioning mechanism without loss of universality.
This requirement mandates the generalized knowledge container to have an extremely simple structure, as simple as a causally related data representation, called function. In other words, the goal of MU is to organize all content knowledge using an abstract model that represent knowledge as a generic function that could be composed of other functions recursively.
Implementation Strategy
To make this happen, we decide to leverage the Web Tech Stack as an operational platform and leverage the resources and tools in this open ended platform to govern the evolutionary trends of MU. MU has three explicit functions:
- MU is an data container for Learning Activities, it captures physical activities in concrete data elements for the organization.
- The Data Content of MU is governed by the participants of these learning activities, implemented as smart contracts authorized by the said participants.
- MU invites verification and validation, through namespace management, time stamping and data linkages, MU associates physical meaning to data by encoding content knowledge based on the observable patterns of the physical universe.
The goal statement mentioned above also indicates that MU will provide a curriculum that enables minimal footprint to the core content knowledge, while covering the broadest possible content areas. In other words, MU's existence can be thought of as the core/kernel of an operating system of knowledge acquisition in this data-intensive era, and it will help organize learning activities in a structure that best adhere to this fast changing world. Specifically, MU will offer a curriculum structure that follows the above assumptions, and try to link all knowledge content using a unifying data management strategy.
Individuals and their Societies
Knowing that all content knowledge cannot exist in isolation. MU is intended to be a social learning tool that allows individuals to maintain one's private content, while maximize its ease of exchange with other interacting parties. Therefore, MU will explicitly spell out project management tools and practices for both individuals and team members, and offer data governing policies for all participants. The logic of how data evolve, is the foundation of computing science, and that is where all projects on MU-supported projects will leverage its project management/governance features. This requires users to be highly aware of data security and data privacy related issues.
In layman's term, MU will organize content knowledge in the units of languages, starting with the foundation of all languages, the meta language, or the logic of data in general. Then, MU will introduce domain-specific languages, including linkages to natural languages as ways to demonstrate the power of information compression in languages. Therefore, MU participants can continuously leverage a single repository of languages, to organize their own knowledge content in privately controlled data storage containers.
A Universal Container
The first principle of MU is to assume that certain universal principle exists. The principle that we will introduce to all MU students is the notion that all data can be represented using the same type, a type called: "Lattice" or bounded "Partially ordered set"(POSet). This universal data type can be mapped onto human's daily experience in terms of space, time, and energy. The reason that we must introduce this common notion of data structure is to offer a universal data framework that will be applicable to situations across all spatial and temporal contexts. To guarantee this universality, some logical assumptions must be made, and we assume that all spacetime complex follows the logical boundaries of POSet.
Foundational References
MU differs from other universities by starting its curriculum in a sequence that is almost opposite of what most popular curriculum are designed. This is suggested by Gregory Chaitin, that many accomplished mathematicians are taught the most advanced mathematics from a young age and in a sequence that delights their interest to pursue mathematical content. When properly administered, this strategy may apply to learn and teach content knowledge of many kind. For example, we will provide a universal data structure[3], a.k.a. lattice[4][5], to approximate the boundary of our logical reasoning scopes.
Prior work
A Google Document on Meta University that lead to this document is available[6].
MetaUniversity
Programs in MU will be organized in languages, as in managing vocabulary, syntactical rules, and pragmatic uses. This classification will enable a unifying framework and data analytics tools for learning outcome assessments as well as enabling the compositional opportunities of knowledge content.
The Notion of Unviersality
Universality is a operationalized definition according to logicians.
A Generalized Process for Knowledge Acquisition
A university is an agency that embodies collective Intelligence. The process model for such intelligence can be modeled after ideas originated in Carl Jung and Thomas Kuhn's theories.
Containers for all Knowledge
Using Wikidata as an example.
- Leverage Existing Data and Learning Assets on the Internet
- Capture visitation data for all these assets
- Provide annotation and create new content
- Publish it using PKC
Conceptual Space and Repeatability
As I talking with 郭逸舟， he mentioned Gutenberg can make more impact was due to the size of alphabet in European languages are smaller than Chinese. So that he was able to leverage his work to create Renaisance.
Functional Roles in Society
The initial vocabulary of Roles in MU will be based on two camps of knowledge, Holacracy's roles and Ansible's roles.
Data as the Medium
Core Curriculum:Reliable Data as the Medium
Data identity is given measurable degrees of trust worthiness based on timestamps and namespace references.
According to Prof. Gautam Dasgupta, this curriculum will be organized in various methods of counting:
- Arithemtic:Counting in Numbers (See Gasing Counting)
- Geometry:Counting in Space
- Music:Counting in Time
- Astronomy and Geography:Counting in Spacetime
Skill Mastery
A univeresity is also a place where skills and knowledge of persons get refined to a point of mastery. To ensure this experience of incremental improvements and shaping of good habits, while avoiding addiction, we need a general framework to observe the process of entering mastery. The program will be initiated with past experience accumulated in the previous programs:
Domain Specialties: Disciplinary Specific Data
Smart Contracts to operationalize accountability
All students in Meta University must use Smart Contracts to organize their shared tasks.
Measuring Metrics: All learning activities will be accounted for
Learning activities will be registered using common databases. Detail actions for each participants will be tracked by Matomo.
Working with Concurrent Data Events at Scale
Teach all students the basic notion of sets, orderings, and compositions in precise languages. Moreover, allow them to present the data and information content in tools and platforms that would matter to their daily lives.
- Content-wise Linked Open Data
- Processing Capacity Scale Up and Scale Out
An Exchange Platform
There are some existing literature[7] that already covers content related to this proposition. Financial and labor market places will be set up to enable exchanges. Ideally, some form of financial rewards should be directly written into smart contracts for people who are competent to conduct their work professionally.
Organizational Sustainability
Participation model and Reaching Consensus
Branching and Exiting
Since all types of organizations must evolve and change over time, mention Thomas Kuhn and his book[8].
Merging and Joining
All organizations evolves in a similar way, mention Kuhn's Cycle and his book[8].
Data is the Asset
To help students assess the values of knowledge content, value of data content is dynamically evaluated in covertable currencies. MU will provide an exchange market place for students to verify and validate the transaction of data content, while measuring these transactional activities to assess the social value of individual data assets.
Score Keeping: Manage incentives
Offering currencies to quantify data asset transactions can be an incentive mechanism for learning. It is operationally feasible to record all data transactions between users in MU. In the universe of data, any data exchange could cause a cascading effect throughout the community. Therefore, score keeping should be managed by sustainable governing mechanisms. Data from the past, present, and future should all be given credits and therefore, create a perpetual incentive system for all. Instrumentation such as PKC will keep these transactional data records as a form of evidence for accumulative efforts or contributions. In educational practices, they are often called degree certificate/badges or gamification. Put it simply, it is one of many scoring, or assessment mechanism in traditional teaching. The most important aspect is to establish a formalized metric to continuously assess learning accomplishments using highly-available and consistent data.
Namespaces: Managing Data Dictionary over Time
To ensure all data content are tracked within MU, a set of highly-available, automated data dictionaries will be utilized to track the above mentioned transactional activities in terms of frequencies and amount of data exchange. This transactional activity log will provide a mechanism to measure the focus of interests of the utilizing parties.
Time:the symmetry-breaker
Visualizing Projects in Timeline
| Start datetime | End datetime | |
|---|---|---|
| A new form | 6 December 2021 10:04:56 | 7 December 2021 10:04:56 |
| Another contextualized event | 29 November 2021 10:05:39 | 8 December 2021 10:05:39 |
| Event:Conference/2021,07,15 | 15 July 2021 07:50:08 | 18 July 2021 07:50:08 |
| Event:Meeting/2021,07,15 | 15 July 2021 08:00:00 | 15 July 2021 09:00:00 |
| Event:Project/Clean up dev.xlp.pub | 16 July 2021 09:00:13 | 18 July 2021 08:00:13 |
| Event:Project/EnterpriseForTheFuture/Stage 1 | 1 September 2021 00:00:01 | 30 November 2021 23:59:59 |
| Event:Project/EnterpriseForTheFuture/Stage 2 | 1 January 2022 00:00:01 | 31 August 2022 23:59:59 |
| Event:Project/HDX/2021,07,15 | 15 July 2021 12:30:00 | 15 July 2021 14:00:00 |
| Meeting with Prof. Surya | 30 January 2022 06:47:08 | 8 April 2022 06:47:08 |
| PKC Workflow/Stage 1 | 16 July 2021 08:00:00 | 16 July 2021 09:00:00 |
Become Productive and Offer Services to Society
All the above mentioned knowledge should enhance the participants' capability to create better products and services in their respective society. MU intend to have all participants to be productive in offering data, information, knowledge, and actual services/products while they are learning at MU. The data-intensive environment articulated earlier will enable people to contribute to a data-linked supply chain of services and products. More importantly, it should reduce the unnecessary barriers and middle persons in the economic process. MU will enhance students' and the overall MU community's productivity by offering a number of foundational guidance:
- Governance Template in terms of Constitution
- Economic Template in terms of Exchange Marketplaces and Currencies
- Technology Template in terms of DevOps and Linked Open Data APIs
Conclusion
Toward the Science of Self-Governance
References
- ↑ Newman, John (1986). The Idea of a University (University of Notre Dame Press edition 1982 ed.). local page: University of Notre Dame Press. ISBN 0-268-01150-8.
- ↑ Gordon, Moore E. (Apr 19, 1965). Cramming more components onto integrated circuits (PDF). local page: Electronics Magazine.
- ↑ Scott, Dana (January 1, 1970). "Outline of a Mathematical Theory of Computation". local page: Oxford University Computing Laboratory Programming Research Group.
- ↑ Cousot, Patrick; Cousot, Radhia (1977). Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints (PDF). 4th POPL. local page: ACM Press. p. 238-252.
- ↑ Cousot, Patrick (Sep 2021). Principles of Abstract Interpretation. local page: ACM Press.
- ↑ If you have editorial access to the Meta University Google document, click here
- ↑ Crawley, Edward; Hegarty, John; Edström, Kristina; Sanchez, Juan Cristobal Garcia (2020). Universities as Engines of Economic Development. local page: Springer. ISBN 978-3-030-47549-9.
- ↑ 8.0 8.1 Kuhn, Thomas (2012). The Structure of Scientific Revolutions (50th Anniversary ed.). local page: University of Chicago Press. ISBN 978-0-226-45811-3.