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 ).''
Size matters. Some people object that it does not matter, but has meaning. But meaning always matters, so it is the same.
The bigger problems one solves, the bigger the gains. Big problems require big solutions. We live in a big universe and our very survival is to deal with bigger and bigger problems, which require bigger and bigger solutions to cope.
But nevertheless to build big is hard so we naturally prefer to create small things which can grow. Small from point of view both of understandable and affordable to build. So best fit are small solutions, cheap and easy to make which scale out or unfold or unleash into big means to address big problems. Scaling is everything.
Scaling. Scalable! Scalability !!
The root-word 'scale' possesses marvelous riches of meaning in English language  with lots of poetics inside.:
 snake skin epidermals - wisdom, memory, protection, rejuvenation, regeneration, eternity...
hen to pan (ἓν τὸ πᾶν), "the all is one"
 warrior armour - security, defense, power, strength.
 weighting scales - device to measure mass, unit, measure, account.
all very Blockchainy wording without any shadow of doubt.
The scalability issues could be grokked  with the following anecdote:
Bunch of workers on a construction site and a huge log. The onsite manager commands a few of them to lift and move it. They try and object ''Too heavy!''. The manager adds more and more workers, until they shout back again: ''Too short!''.
A few real examples, the first two - bad and the last three excellent:
[a] I won't name this 'crypto' just will say it is named after a mythical element of the universe, according to the prescientific gnostic  imaginations. It's core 'value proposition is to shovel meaningful computation into a thread of computation which very value proposition is to be as random, meaningless and unidirectional (hard to do, easy to prove) as possibly possible - the blockchain. The theoretically most expensive form of computation. Visualize: cars and airplanes made of gold and diamonds burning most expensive perfumes. Or mass production of electricity by raising trillions of cats and hiring trillions of people to pet them with grid of pure gold wires to discharge and collect the electrostatics. If they have chosen the original Satoshi blockchain  for their 'experiments' - where the futility of such attempt would become instantly clear and would die out outright due to impending unbearable cost - will of course be more fair way to do, and would've spared dozens of billions of dollars to the Mankind, but logically they preferred a 'controlled' blockchain of their own. In a sense that the guys with vested interest into it have the power to hand-drive, stop, restart and vivisect it. The only use of this 'blockchain supercomputer' is ... tokenomics by Layering. Why it was at all necessary for a blockchain advertised as so good as to do all the general computation, to be made so hairy and bushy with layered tokens??
[b] Another trio of chaps, won't mention names again, were really at awe with Satoshi's creation, so much that they not just liked, but wanted it and decided to have it. For themselves. All of it. And rebelled and forked out and provided 'scaling' errrmm ... uhhh... solution. By increasing the blocksize. Something which Satoshi meditated on, extensively discussed with his disciples and not occasionally decided to put breaks on.  Very recently the crypto news headlines said that the blocksize increase solution providers are eyeing ... Layering. Which they furiously were advocating that blocksize increase makes unnecessary. Cause it is the solution, isn't it? Or maybe it just was. And is not anymore? Well, I'd say that all the aka 'alts'  - to provide a rejuvenated clone of Bitcoin tweeked here and there to provide momentary ease of difficulty and transaction fees - suffer from one and a same problem - traveling back in time does not tell you the future.
[c] Lets jump half a century back in time. It is 1960es. The very making of internet. Computers are already here and scaled up in numbers so their networking to become a problem/juice worth the solution/squeeze. The birth of TCP/IP  and the report of the very makers of it. Of the solution for the network scaling. Enjoy the ancient wisdom:
Initially, the TCP managed both datagram transmissions and routing, but as the protocol grew, other researchers recommended a division of functionality into protocol layers. Advocates included Johnatan Postel of the University of Southern California's Information Sciences Institute, who edited the Request for Comments (RFCs), the technical and strategic document series that has both documented and catalyzed Internet development. Postel stated, "We are screwing up in our design of Internet protocols by violating the principle of layering." Encapsulation of different mechanisms was intended to create an environment where the upper layers could access only what was needed from the lower layers. A monolithic design would be inflexible and lead to scalability issues. The Transmission Control Program was split into two distinct protocols, the Transmission Control Protocol and the Internet Protocol.
The layering made the Internet as we know it. By the simple trick of just one node needed to permit another. Unstoppable inclusivity!
[d] The Mastercoin / Omni Layer :
«A common analogy that is used to describe the relation of the Omni Layer to bitcoin is that of HTTP to TCP/IP: HTTP, like the Omni Layer, is the application layer to the more fundamental transport and internet layer of TCP/IP, like bitcoin».
[e] The Lightning network (LN) :
The Lightning Network is a "second layer" payment protocol that operates on top of a blockchain (most commonly Bitcoin).
Satoshi spoke on 'payment' channels in his masterpiece. Foreseeing the way to scale.
An estimate of the power of LN layering .:
''The bitcoin devs accept that eventually larger block sizes will be needed. The current transaction rate isn't going to cut it if people all over the world actually start using bitcoin daily. They estimate that eventually, if everyone in the world uses bitcoin and makes 2 transactions a day, but uses the lightning network, a 133mb blocksize will be needed. Without the lightning network, something like a 200gb (GIGABYTE) size PER BLOCK would be needed to accommodate that much usage.''
Layering upscales it with orders of magnitude of higher efficiency.
If Bitcoin is the 'first layer' and Omni and Lightning are 'second layer', I see which one is the 'Zeroth Layer' and also foresee  the inevitability of the merger or 'Amalgamation' of all second layers over all blockchains, so the user will be able to transact everything into anything to anybody, without to know or care which chain is in use ... I have special nicknames for these and will go back to these topics in series of future posts.
Enough of examples I reckon.
The Postel's sacred Principle of Layering comes from the implementation levels paradigm.
or Abstraction layering :
''separations of concerns to facilitate interoperability and platform independence''
With other words - delegate the task to that layer of the system which does the particular job best. We can generalize this into The Scaling Commandment. Only one enough:
''Thou shalt not jam it all into a single layer!''
The Layer Cake architecture is literally ubiquitous across the Universe.: biology, semantics, informatics ...
It seems that it is if not the only, at least THE way to scale.
Maybe, someday, we the Humanity, upscaled by Tauchain will discover more powerful than Layering ways to Scale, but it is all we have for now.
Scaling is a problem. Scaling must be scalable, too.
Metascale from here to Eternity.
The value of Knowledge Representation and the Decentralized Knowledge Base for Artificial Intelligence (expert systems). By Dana Edwards. Posted on Steemit. March 27, 2017.
This article contains an explanation of two core concepts for creating decentralized artificial intelligence and also discusses some projects which are attempting to bring these concepts into practical reality. The first of these concepts is called knowledge representation. The second of these concepts is called a knowledge base. Human beings contribute to a knowledge base using a knowledge representation language. Reasoning over this knowledge base is possible and artificial intelligence utilizing this knowledge base is also possible.
Knowledge representation defined by it's roles.
To define knowledge representation we must list the five roles of knowledge representation which can reveal what it does.
1. Knowledge representation is a surrogate
2. Knowledge representation is a set of ontological commitments
3. Knowledge representation is a fragmentary theory of intelligent reasoning
4. Knowledge representation is a medium for efficient computation
Part 1: Knowledge Representation is a Surrogate
By surrogate we means it is substituting or acting in place of something. So if knowledge representation is a surrogate then it must be representing some original. There is of course an issue that the surrogate must be a completely accurate representation but if we want a completely accurate representation of an object then it can only come from the object itself. In this case all other representations are inaccurate as they inevitably contain simplifying assumptions and possibly artifacts. To put this into a context, if you make a copy of an audio recording, for every copy you make it going to contain slightly more artifacts. This similarly also happens when dealing with information sent through a wire, where if not properly amplified there eventually will be artifects that come from copying a transmission.
"Two important consequences follow from the inevitability of imperfect surrogates. One consequence is that in describing the natural world, we must inevitably lie, by omission at least. At a minimum we must omit some of the effectively limitless complexity of the natural world; our descriptions may in addition introduce artifacts not present in the world.
Part 2: Knowledge Representation is a Set of Ontological Commitments.
"If, as we have argued, all representations are imperfect approximations to reality, each approximation attending to some things and ignoring others, then in selecting any representation we are in the very same act unavoidably making a set of decisions about how and what to see in the world. That is, selecting a representation means making a set of ontological commitments. (2) The commitments are in effect a strong pair of glasses that determine what we can see, bringing some part of the world into sharp focus, at the expense of blurring other parts."
In this case because our commitments are made then our representation is selected by making a set of ontological commitments. An ontological commitment is a framework for how we will view the world, such as viewing the world through logic. If we choose to view the world through logic, through rule-based systems then all of our knowledge about the world is also within that framework. We choose our representation technology and commit to a particular view of the world.
Part 3: Knowledge Representation is a Fragmentary Theory of Intelligent Reasoning.
Mathmaetical logic seems to provide a basis for some of intelligent reasoning but it is also recognized to be derived from the five fields which include of course mathematical logic, but also psychology, biology, statistics, and economics. If we go with mathematical logic then we have deductive and inductive reasoning approaches. Deductive reasoning according to some is the basis behind. If we want to explore an example of reasoning we can take the Socrates example,
Statement A: True? Y/N?
"All men are mortal"
Statement B: True? Y/N?
"Socrates is a man"
Satement C: True? Y/N?
"Socrates is a mortal"
If A is true, and B is also true, then C must be true. This is an example of basic logical reasoning which can easily be resolved using symbol manipulation and knowledge representation. The symbol at play in this example would be implication.
Part 4: Knowledge Representation is a Medium for Efficient Computation.
If we think of computational efficiency, and think of all forms of computation whether mechanical or natural in the sense of the sort of computation done by a biological entity, then we may think of knowledge representation as a medium for that computation efficiency. Currently we think of money as a medium of exchange, and if we think of the human brain as a type of computer which does human computation, then we may think of knowledge representation.
While the issue of efficient use of representations has been addressed by representation designers, in the larger sense the field appears to have been historically ambivalent in its reaction. Early recognition of the notion of heuristic adequacy  demonstrates that early on researchers appreciated the significance of the computational properties of a representation, but the tone of much subsequent work in logic (e.g., ) suggested that epistemology (knowledge content) alone mattered, and defined computational efficiency out of the agenda. Epistemology does of course matter, and it may be useful to study it without the potentially distracting concerns about speed. But eventually we must compute with our representations, hence efficiency must be part of the agenda. The pendulum later swung sharply over, to what we might call the computational imperative view. Some work in this vein (e.g., ) offered representation languages whose design was strongly driven by the desire to provide not only efficiency, but guaranteed efficiency. The result appears to be a language of significant speed but restricted expressive power .
While I will admit the above paragraph may be a bit cryptic, shows that there is a view that better representation of knowledge leads to computational efficiency.
Part 5: Knowledge Representation is a Medium of Human Expression.
Of course knowledge representation is part of how we communicate with each other or with machines. Human beings use natural language to convey knowledge and this natural language can include the use of vocabularies of words with agreed upon meanings. This vocabulary of words may be found in various dictionaries including the urban dictionary and we rely on these dictionaries as a sort of knowledge base.
What is a decentralized Knowledge Base?
To understand what a decentralized knowledge base is we must first describe what a knowledge base is. A knowledge base stores knowledge representations which are described in the above examples. This knowledge base in more simple terms could be thought of as representing the facts about the world in the form of structured and or unstructured information which can be utilized by a computer system. An artificial intelligence can utilize a knowledge base to solve problems and typically this particular kind of artificial intelligence is called an expert system. The artificial intelligence in the most simple form will just reason on this knowledge base through an inference engine and through this it can do the sort of computations which are of great utility to problem solvers.
When we think of Wikipedia we are thinking about an encyclopedia which the whole world can contribute to. When we think about the problems with Wikipedia we can quickly see that one of the problems is the fact that it's centralized. We also have the problem that the knowledge that is stored on Wikipedia is not stored in a way which machines can make use of it and this means even if Wikipedia can be useful for humans to look up facts it is not in the current form able to act effectively as a decentralized knowledge base. DBPedia is an attempt to bring Wikipedia into a form which machines can make use of but it still is centralized which means a DDOS or similar attack can censor it.
Decentralized knowledge is important for the world and a decentralized knowledge base is critical for the development of a decentralized AI. If we are speaking about an expert system then the knowledge base would have to be as large as possible which means we may need to give the incentive for human beings to contribute and share their knowledge with this decentralized knowledge base. We also would have to provide a knowledge representation language so that human beings can share their knowledge in the appropriate way for it to enter into the knowledge base to be used by potential AI.
Knowledge representation is a necessary component for the vast majority of attempts at a truly decentralized AI. If we are going to deal with any AI then we must have a way for human beings to convey knowledge to the machines in a way which both the human beings and machines can understand it. The use of a knowledge representation language makes it possible for a human being to contribute to a knowledge base and this ultimately allows for machines to make use of it's inference engine capabilities to reason from this knowledge base. In the case of a decentralized knowledge base then the barrier of entry is low or non-existent and any human being or perhaps any living being or even robots can contribute to this shared resource yet at the same time both humans and machines can gain utility from this shared resource. An artificial intelligence which functions similar to an expert system can make use of an extremely large knowledge base to solve complex problems and a decentralized knowledge base combined with open and decentralized access to this artificial intelligence can benefit humanity and life on earth in general if used appropriately.
Discussion of example projects.
One of the well known attempts to do something like this is Tauchain which will have both a knowledge representation system and a decentralized knowledge base. In the case of Tau there will be a special simple knowledge representation language under development which resembles simplified controlled English. This knowledge representation language will allow anyone to contribute to the collective knowledge base. Tauchain eventually will have a decentralized knowledge base over the course of it's evolution from the first alpha.
Unfortunately upon reading the Lunyr whitepaper and following their public materials I fail to see how they will pull off what they are promising. I do not think the current Ethereum can handle concurrency which probably would be necessary for doing AI. I also don't see how Ethereum would be able to do it securely with the current design although I remain optimistic about Casper. The lack of code on Github, the lack of references to their research, does not allow me to completely analyze their approach. I can see based on the fact that they are talking about a decentralized knowledge base that their approach will require more than the magic of the market combined with pretty marketing. They will require a knowledge representation language, they will require a true decentralized knowledge base built into IPFS. This true decentralized knowledge base will have to scale with IPFS and through this maybe they can achieve something but without a clear plan of action I would have to say that today I'm not confident in their approach or in Ethereum's ability to handle doing it efficiently.
Fuente / Source: Original post written by Dana Edwards. Published on Steemit: The value of Knowledge Representation and the Decentralized Knowledge Base for Artificial Intelligence (expert systems).
Logo by CapitanArt
Enlaces / Links
Logo by CapitanArt
Archivos / Archives
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.