The power of ambiguity and of ambiguity minimization in communication. By Dana Edwards on Steemit. June 1, 2018.
Formal communication benefits from ambiguity minimization.
So what exactly do I mean by formal communication? Well when we think of how human beings communicate with machines it is in a formal language. This formal language requires minimized ambiguity for security analysis (how can we analyze code if we cannot effectively interpret it?). The other problem is that the machines require for example that if... then... else and similar conditional statements are well defined and unambiguous.
Is it possible to show that a grammar is unambiguous?
To show a grammar is unambiguous you have to argue that for each string in the language there is only one derivation tree. This is how it would be done theoretically speaking.
In computer science, an ambiguous grammar is a context-free grammar for which there exists a string that can have more than one leftmost derivation or parse tree, while an unambiguous grammar is a context-free grammar for which every valid string has a unique leftmost derivation or parse tree. Many languages admit both ambiguous and unambiguous grammars, while some languages admit only ambiguous grammars.
Specifically we know that deterministic context free grammars must be unambiguous. So we know unambiguous grammars exist. It appears the strategy is ambiguity minimization with regard to formal languages (such as computer programming languages).
For computer programming languages, the reference grammar is often ambiguous, due to issues such as the dangling else problem. If present, these ambiguities are generally resolved by adding precedence rules or other context-sensitive parsing rules, so the overall phrase grammar is unambiguous. The set of all parse trees for an ambiguous sentence is called a parse forest.
The parse forest is an important concept to note. All possible parse trees for an ambiguous sentence is called a "parse forest". This concept is key to understanding the strategy of ambiguity minimization. So we can in practice minimize ambiguity and we know for certain that deterministic context free grammars admit an unambiguous grammar but what does that mean? What are the benefits of unambiguous language in general?
A benefit of ambiguity minimization
Simple English is a form of controlled English designed to minimize ambiguity in English. This is important because by using simple English to codify the rules or write the laws it puts it in a language where there is less of a computational expense (in brain power) to process and interpret the statements.
In one of my older blogposts @omitaylor commented and in one of her future posts she asked about the topic of love. In specific her post was titled: "What Does LOVE Mean To YOU"
Her post highlights the fact that there are different love languages and that we don't all speak the same love language. Ambiguity here is actually not a good thing but the simple fact is when someone speaks about love how do we know they are talking about the same thing? As a result we often seek an agreed upon or formally defined "love concept" where we all agree it's love. This is not trivial to find and as a result a topic like love is not easy to discuss in any serious manner. Unambiguous communication or to be more precise (minimized ambiguity) would allow Alice to discuss with Bob the topic of love in a way where they both know exactly what the other is referring to in terms of behavioral expectations, emotions/feelings, etc.
If Alice agrees to love Bob then Bob has no way to determine what Alice means unless he and she agree on a mutually defined concept of love. This highlights how agreement requires very good communication and how minimizing ambiguity can be beneficial at least in this example.
Ambiguity minimization makes sense when you are following a principle of computational kindness. That is if Alice would like to reduce the computational burden on Bob then she can reduce or minimize the ambiguity of her sentence. This is because in order for Bob to interpret an ambiguous sentence Bob must in essence sort all possible interpretations of that sentence from most likely interpretation to least likely interpretation, and before he can even sort he must first search in order to find all possible or at least plausible interpretations.
This is very computationally expensive for Bob but very cheap for Alice. Alice knows exactly what she means but Bob has no clue what Alice REALLY means.
A benefit of ambiguity
There are other examples where increasing ambiguity could be beneficial, such as perhaps when the communication is less than formal, or to share a stream of consciousness without turning it into a formal communication. Humor for example rides on ambiguity and a good joke may have multiple layers. Art also leverages ambiguity because it's perhaps meant to be interpreted 20 different ways all to produce a certain desired affect.
Ambiguity allows more meaning to be packed into fewer words. This in a sense is a sort of compression scheme. So if a sentence has multiple possible meanings the levels or meanings are still finite. It's a fixed amount of meanings and so theoretically speaking a search can be conducted. In fact this is what a human being does when interpreting natural language where a sentence can have multiple meanings (they do a search for all possible interpretations of that sentence). The problem with this is that it is computationally expensive as a process at least for the human being to try to figure out all possible interpretations of a sentence.
Lawyers when they do their work are working with a specific knowledge base of common legal sentences and common interpretations known in their profession but the rest of us might see a sentence in lawyer-speak and not really know what it means because we will not know the common interpretations. This is a big problem of course because to form agreements between two parties both parties need to have a common understanding (a kind of knowledge symmetric understandability) allowing them both to interpret roughly the same sentence to mean the same thing.
“A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects.”
― Robert A. Heinlein 
No, it is not a vow everybody to be everything. It is a reflection of the fundamental human fungibility . The average human can be taught to take any human role. The exceptions of true organic geniuses (those who are hard to be replaced) and morons (those who are incapable to replace), only confirm this general rule of shear numbers . This is what makes the mankind so scalable .
''Know'' is synonymous with ''can''. Literally. Knowledge = technology. Even etymologically . Knowledge is praxis . Only. There ain't such thing as impractical knowledge. If it is not a skill, it is not knowledge. I mentioned once  that we're all AIs. Ref.: feral children .
We are not what we eat , but we are what we've learnt. You are what you know/can. And you can what you have learnt. Learning is from the taking side. Teaching is on the giving side. Of one and a same process. We do not have a word to denote the modulus  of learning/teaching, it seems. But it will come.
We are taught by the others, the society. We are the cherry ontop of a layer cake of culture onto nature . We are learning by ... living. We acquire skills in plethora of contexts from family, street, school, job, media ... Learning  is not a monopoly of man, countless systems are also learners. Maybe one of the basic definitions of life and intelligence is the ability to learn . Giant topic, yeah. We won't graze into it here now on what is learning, but on how we learn.
Due to our neurological bottlenecks we spontaneously form hierarchies . This hinders our scalabilty  by forcing humanity to be more or less a fractal of 5. We are close to a number of breakthroughs which to mitigate these innate limitations of ours into a number of ways    . But the general case is not subject of this article - herein we focus on HOW we are taught. How we acquire knowledge, and how this knowledge of ours gets recognized and utilized by society. And the hierarchic emergent structuring is of course in full force upon us in teaching as well as into everything social else.
So comes education , such comes exam , knowledge certification , certified skills application , knowledge creation verification , job fitness testing , CVs and employer recommendations ... etc., etc. With all the bugs and the so little features of this 'map is not the territory' , situation.
It is all centralized and hierarchic - exactly as the global fractal of double-entry accountancy ledgers which we call fiat financial system is. In fact it is so interwoven with fiat finance than it is almost inextricable from it . And as much inefficient and imprecise.
In all these years of talking and thinking on Tauchain  - I noticed - and this suspicion of mine incrementally turns into shear conviction - that Tau, the upscaler of humanity, inevitably also is the ultimate teaching machine. If education is facilitating of learning, Tau is the maximizer of learning. By its very construction, it comes out so.
People talk and listen whenever and whatever they want. Tau has unlimited capacity to listen and attend and remember, and answer. Only limited by the hardware capacity allocated. Tau extracts meaning. Purifies the stream, distills it down to the essence. Detects repetitions, contradictions and all other, ubiquitous nowadays conversation bugs. Remembers changes of opinions of the individual user. And points them out. Sounds like the best tool to know oneself. And the others to know you if you let them.
Your Tau account or profile is what you know. You say what you say and also ask. Say statements and questions. Tau pools you together with the others who state the same and, more importantly, who ask the same type of questions. Knowing what you know, and asking about what you don't know but want to know, maps not only your knowledge state but also maps your knowledge dynamics. Records and drives how your knowledge changes. You even have access to what you forget, and can recollect it. True real time knowledge state reporting. For first time in human history.
If consciousness  is - aside from the clinical state of being merely awake - the post-factum integration of senso-motoric experience , the Accountant of mind, the speaker of the narrative which is you, then Tau is your consciousness booster. That is - stronger than thought.
The ultimate teaching, the ultimate fair testing or exam, the ultimate real-time comprehensive diploma, or certificate, super-peer reviewed paper(s) of you as academic carrer.., the ultimate job interview AND the ultimate ... job of being working as yourself and anything useful you create to be instantly scarcifiable and monetizable - your Tau account is! And all the rest of accessible socoety - being your own workforce. And you to them. In the billions. In a move. In real time.
Including control over the pathways of increase of your skills towards the most productive personally for you learning directions, because it aids you to analyze the you-Tau history and to apply knowledge maximizer techniques and to participate profitably into creation of newer better ones. Maximizer of self. And maximizer of society making it to consist of max-selfs. Ever improving. Merger of education with work occupation. Work-as-you-live.
The literal Knowledge Economy, as described by @trafalgar in his article  from few months ago. Where search, creation, reflection, certification, recognition, commercialization, accumulation, modification, improvement ... everything of knowledge - is all in one.
And it is not only Humans and Tau lonely job. I foresee the other Machines to join the party . Yes, I mean machines capable to have interests and to ask and seek answers of palatable questions.
This - the education amplification - to come down the technology way - has been, of course, anticipated by many. Few arbitrary examples:
- A distant rough-sketch hint for the inevitable tuition power of Tau is Neil Stephenson's  ''The Diamond age''  , with the depicted: '' Or, A Young Lady's Illustrated Primer '' , as an interactive networked teaching device.
- or if I'm right about the inevitable conquest of the natural languages territory  - UX  like in the 'Her' (2013) film .
- Thomas Frey  of the futurist DaVinci Institute  in his book ''Epiphany Z''  paid special attention of this.: down the way of micro- and nano-education, an effective merger of the processes of education, diplomas issuing, job application, exam and actual execution of job obligations. Tom does not know about Tau. But I'll tell him.
With a big smile of irony and self-irony of course... these examples. Just to pick from here and there proofs of the giant anticipation of what's to come. And taken with a few big grains of salt. Cause the reality will be immensely more powerful.
Tutor , tuition , my emphasis via using exactly this wording, comes to denote the economic side of learning/teaching. It is about the cost of learning - the association of tuition with fees, about the placement of the acquired skills, about the business organization of those, about the protection of ownership and security of transaction of knowledge ... Let me introduce here a neologism  which to reflect the business side of it:
Scrooge Factor 
- Simply denoting the money-making power of a technology use by a business. The 'money suction power' of a business entity or organization of any kind coming from the application of a technology, if you want. Technology as socialized knowledge. Scaled up over multiple humans. Over a society. Of course the Scrooge Factor can pump in different directions. The Scrooge Factor of the traditional hierarchic education, governance and everything ... is apparently very often negative - hierarchies decapitalize, dissipate, waste. Orders of magnitude more wasteful than any PoW , but on this - some other time.
So aside from all the niceties of the abstractions of the full supply and value chains of a Knowledge economy, lets round up some numbers:
- We know that a true functional semantic search engine alone is worth $10t. Yeah. Tens of Trills. Trillions. As per the assessments of Davos WEF attendees of as far as I remember 2015 or 2016...
- Also, Bill Gates stated back in 2004  that ''If you invent a breakthrough in artificial intelligence, so machines can learn,'' Mr. Gates responded, ''that is worth 10 Microsofts.''
- Tom Frey  also argued  that by 2030 the biggest corporation in the world will be an online school. Given the present day size and growth rate  of, say, Amazon  this 'online school' should be in the range of good deal of trillions of marcap if it is to be bigger than the biggest corporations. But we do not need such indirect analogies over analogies to access the scale. The shear size of the global education industry is the most eloquent indicator . Note that Tom talks about 'corporation' i.e. for clumsy and inefficient hierarchic human collective. Not for a system which does this orders of magnitude more efficiently and powerfully due to being intrinsically P2P, i.e. geodesic . Even the best futurologists can be forgiven for missing to predict Tau. :)
And this mind-boggling hail of trillions, does not even account for the Hanson Engine  factor.
Tau the Tutor ex Machina is just another unintended useful consequence outta the overall design.
It is nearly impossible to track and contemplate exactly what all these 'side-effects' would be and how they will synergetically boost each other.
With my articles I intend to only touch some lines of the immense phase space  of the possibilia, with neither any ambition to think it is possible to cover it all, nor this to represent any form of advice.
Future is incompressible. Compression is comprehension. Comprehensible only by living.
Failure to go to the geodesic way of learning, will turn these beautiful but trilling words into prophecy:
"The most merciful thing in the world, I think, is the inability of the human mind to correlate all its contents. We live on a placid island of ignorance in the midst of black seas of infinity, and it was not meant that we should voyage far. The sciences, each straining in its own direction, have hitherto harmed us little; but some day the piecing together of dissociated knowledge will open up such terrifying vistas of reality, and of our frightful position therein, that we shall either go mad from the revelation or flee from the deadly light into the peace and safety of a new dark age." H.P.Lovecraft  (1926 ).''
What is the Knowledge Acquisition Bottleneck problem? By Dana Edwards. Posted on Steemit. March 29, 2017.
Now that we know what knowledge representation is, and what knowledge bases are, and how the knowledge base is relied upon in a knowledge based system of artificial intelligence (KR+KB+Inference engine), we can move on to discussing one of the open problems.
The Knowledge Acquisition Bottleneck problem.
Many people already know about the familiar Byzantines generals problem in computer science. We also know how the Nakamoto consensus in Bitcoin provided a novel example of a solution. The Knowledge Acquisition Bottleneck problem is one of the problems plaguing AI and is what limits or seems to be a limit on the strength of artificial intelligence. One of the main problems in artificial intelligence is that knowledge formation typically requires domain experts who can contribute to the knowledge base. The Cyc project attempted to solve the problem of scaling up the knowledge base but is suffering from the bottleneck. The bottleneck can be summarized below [taken from Wagner, 2006]:
The paper from which this summary was pulled "Breaking the Knowledge Acquisition Bottleneck Through Conversational Knowledge Management" also offers a solution called collaborative conversational knowledge management. This is the same solution which Tauchain will attempt to utilize in a more sophisticated way. Tauchain will allow for collaborative theory formation. In the paper this quote explains a key concept:
We see this concept in how Wikipedia works to manage knowledge. We know Wikipedia is indeed not without flaws but it does manage knowledge. In their conclusion we see this quote:
Tauchain by design will be collaborative and allow for collaborative theory formation. This would mean anyone will be able to contribute to the knowledge base with relative ease. In addition, it will have knowledge management properties built in, and if the knowledge acquisition bottleneck problem can be solved then it will have a huge impact. For one, the problems which prevent knowledge based AI from scaling could be resolved if this bottleneck is removed.
DARPA has attempted to solve the Knowledge Acquisition Bottleneck problem utilizing high performance knowledge bases (HPKBs)and Rapid Knowledge Formation yet failed. Cyc has attempted to solve the same problem and has failed. The semantic web has yet to take off because this problem stands in the way. Will Tauchain succeed where these other attempts have failed? I think it is a strong possibility which is why I'm excited about the implications should Tauchain successfully be built.
Lenat, D. B., Prakash, M., & Shepherd, M. (1985). CYC: Using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks. AI magazine, 6(4), 65.
Wagner, C. (2006). Breaking the Knowledge Acquisition Bottleneck Through Conversational Knowledge Management. Information Resources Management Journal, 19(1), 70-83.
Web 1. https://www.quora.com/What-is-knowledge-acquisition-bottleneck
Web 2. http://www.igi-global.com/dictionary/knowledge-acquisition-bottleneck/49991
Web 3: http://www.tauchain.org
Web 4: https://steemit.com/tauchain/@dana-edwards/how-to-become-a-stakeholder-in-agoras-and-indirectly-tauchain
Fuente / Source: Original post written by Dana Edwards. Published on Steemit: What is the Knowledge Acquisition Bottleneck problem?
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