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).
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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.