Tauchain is not easy to understand but here are some concepts to know to track Ohad's progress. By Dana Edwards. Posted on Steemit. February 28, 2018.
Someone asked over social media why anyone would want to hold onto AGRS tokens merely due to faith in Ohad Asor. This is a good question to ask and a good time to ask it. I'll explain first why I'm holding and then explain how it is important to understand what you invest in.
I hold AGRS because I've communicated with Ohad Asor and he is one of the rare people who is better at research and development than I am. In fact, he is the best I've met so far in the crypto community at that specifically. R&D is the most critical component in software design and we see over and over again if they get that wrong projects are doomed. One of the strengths of Steemit, EOS, etc, is that Dan Larimer is very good at conducting both R&D and at writing the code.
Tauchain currently and unfortunately requires a Phd level understanding of computer science to get very excited about. This is not so good for people who do not really understand their investments but it means that you have the option to ask people who do have Phd level knowledge to either explain Tauchain to you or some aspect of these concepts which I'll list below.
The concepts to understand in order to see what Ohad is attempting on Github.
These concepts may be abstract but they all work together to allow for you the user to input both a document and a grammar into TML and receive a useful output. You have the power of partial evaluation with Futamura Projection. Without going into too much detail here, what it will allow is for the user to provide the input document defining the language and a source code. The first document is the "interpreter" to parse the source code.
So if we look at Github we can see Ohad has completed a rough yet functional implementation of the TML with partial fixed point logic and partial evaluation. The Earley parser takes the source code which is always a string, and parses it. This quote from Wikipedia explains it better:
In computer science, the Earley parser is an algorithm for parsing strings that belong to a given context-free language, though (depending on the variant) it may suffer problems with certain nullable grammars. The algorithm, named after its inventor, Jay Earley, is a chart parser that uses dynamic programming; it is mainly used for parsing in computational linguistics. It was first introduced in his dissertation in 1968 (and later appeared in abbreviated, more legible form in a journal).
In developing compilers we use the grammar to define the syntax of a programming language. TML will require a grammar defining the syntax of the language you want to use and also the source code which are the commands. The AST is an abstract representation of the source code and the logic is applied are part of the syntax analysis phase of compiling. If I did not explain this sufficiently, I apologize as this is again something complicated and requires a lot of prerequisite knowledge.
The point being that if you really want to understand why some of us are so excited about what Ohad Asor is attempting to do then the only way to truly grasp what is at stake is to do the necessary learning. Learn as much as you can about your investment. Study the concepts you have to study in order to keep up with what happens on Github. You don't have to trust the experts if you're willing to gather the knowledge to become an expert yourself. This means reading at minimum all the Wikipedia entries and at maximum it could mean spending hundreds of hours watching Youtube videos, reading academic journals, like some of us have been doing.
If you do not want to invest the time and energy to truly understand Tauchain then maybe it is better that you do not buy a token you cannot understand. Simply wait for it to reach a stage to where you can see what it can do for yourself before making a decision. Do not consider this post or these comments as investment advice but merely a suggestion to use caution with regard to how you spend your money if you do not understand what you are getting into.
Using Controlled English as a Knowledge Representation language. By Dana Edwards. Posted on Steemit. April 4, 2017.
Previously I mentioned "controlled English" when discussing the concept of knowledge representation. This post will go into some detail about what controlled English is. In specific I will discuss Kuhn's doctoral dissertation and Attempto Controlled English (ACE).
Computational linguistics is an interdisciplinary field concerned with the statistical or rule-based modeling of natural language from a computational perspective.
There are many different controlled natural languages
First I would like to discuss the fact that controlled English is not the only controlled nature language and Attempto Controlled English is only one particular controlled English. For example 1 there is RuleSpeak which is a controlled natural language for business rules. Another example2 is Quelo Controlled English which is a controlled English for querying, where you would say statements such as: "I am looking for something, it should be located in a city, the city should produce a new car, the new car should be equipped with a diesel engine". In addition to these examples we also have Google which uses Voice Actions where you can speak into your android phone and say something like: "Create a calendar event: Dinner in San Francisco, Saturday at 7:00PM". All of these are examples of controlled natural languages and reveal just how powerful this could be for users and developers.
What is Attempto Controlled English (ACE)?
Attempto Controlled English also known as ACE is a specific controlled natural language. It is likely that at some point in the early stages of development this controlled natural language will be implemented on Tauchain. ACE is like English but relies on following certain rules with a restricted vocabulary.
Rule subject + verb + complements + adjuncts
All simple ACE sentences have the above structure of subject + verb + complements + adjuncts. An example would be the following sentences below:
A customer waits.
To construct sentences without a verb you can rely on:
there is + noun phrase
There is a customer.
And you can add detail with:
A trusted customer inserts two valid cards.
And you can use variables:
How does Attempto Controlled English help with Knowledge Representation?
In specific because anyone who speaks English can quickly learn Attempto Controlled English it will mean anyone will be able to contribute to the process of knowledge representation. Contributing to a knowledge base becomes very easy when you can simply describe in plain English (with restrictions) exactly the knowledge you want to represent. A semantic Wiki can be built out of this process rather easily.
How does Attempto Controlled English relate to Tauchain?
Tauchain requires input from the users to determine a formal specification. Attempto Controlled English is simple enough that anyone can describe a formal specification. For example sentences like:
Every customer inserts a card.
As you see above, we are dealing with types. Human is a type. Human is divided at a minimum between male and female subtypes.
And ACEWiki gives an example of what a formal specification could look like in Tauchain. The example being country, where the knowledge in this case is the concept of a country. Then we describe a country by filling in the Wiki collaboratively, where we know first of all that every country is an area, but then collaboratively we fill in the list of current persons who govern a country. Through this method we add to the knowledge base using the knowledge representation language ACE, and in the case of Tauchain we would be adding to potentially a formal specification which eventually is synthesized (program synthesis) by the Tauchain automatic programmer.
To learn more about Attempto Controlled English Wiki watch the video lecture
Kuhn, T. (2009). Controlled English for knowledge representation (Doctoral dissertation, University of Zurich).
Kuhn, T. (2014). A survey and classification of controlled natural languages. Computational Linguistics, 40(1), 121-170.
Kuhn, T. (2009). How controlled English can improve semantic wikis. arXiv preprint arXiv:0907.1245.
Ranta, A., Enache, R., & Détrez, G. (2010, September). Controlled language for everyday use: the molto phrasebook. In International Workshop on Controlled Natural Language (pp. 115-136). Springer Berlin Heidelberg.
Ross, Ronald G. 2013. Tabulation of lists in RuleSpeak—using “the following” clause. Business Rules Journal, 14(4):1–16.
White, C., & Schwitter, R. (2009, December). An update on PENG light. In Proceedings of ALTA (Vol. 7, pp. 80-88).
Web 2: http://attempto.ifi.uzh.ch/site/resources/
Fuente / Source: Original post written by Dana Edwards. Published on Steemit: Using Controlled English as a Knowledge Representation language. April 4, 2017.
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Suggested readings to better understand the Tau ecosystem, Tau Meta Language, Tau-Chain and Agoras, and collaborate in the development of the project.
Lecturas sugeridas para entender mejor el ecosistema Tau, Tau Meta Lenguaje, Tau-Chain y Agoras, y colaborar en el desarrollo del proyecto.