The liquid paradigm, feedback loops, the virtuous cycle and Tauchain. By Dana Edwards. Posted on Steemit. December 31, 2017.
What do I mean by the concept of "liquid platform"? This is merely a re-articulation of the concept of self amendment and self definition. In other words it is very much like an autopoietic design. Bruce Lee once said to "be like water", and the reason is because water can adapt to any environment it is placed it by taking the form of the container it is put into.
So by liquid paradigm I mean that the core feature of true next generation platform design is going to be focused on maximum adaptability.
Feedback loops and the virtuous cycle
How can we have a platform which promotes continuous self improvement? If you have a platform with no hard coded "self" then even the design of the platform is under constant negotiation and creation. This is key because it means Tauchain will be able to adapt quicker than all other competing platforms. Quicker than Tezos because Tezos merely provides self amendment but lacks the virtuous cycle, the meta language, etc.
The Tau Meta Language allows for self definition at the level of languages. This means even the communication mechanism between humans and machines can be updated continuously. This continuous updating is the key design breakthrough of Tauchain because it means Tauchain will always be state of the art in any area. Think of a platform like Wikipedia where anyone can update any part of it in real time continuously so that every part of it is always the state of the art.
Starting at languages, the feedback loop can be created between humans and intelligent machines. Humans must make decision on how to design Tau. These design decisions benefit from the virtuous cycle due to the feedback loop between humans and machines allowing the decision making ability itself to be upgraded. This could even allow for the humans to transcend traditional human capabilities by relying on intelligent machines to assist in design which means better future designs, which means better decision making, which means better future designs which leads to better decision making, this represents the "virtuous cycle" by way of a feedback loop between humans to machines to humans to machines to humans etc. The humans improve the quality of the machines by feeding knowledge, feeding new algorithms, feeding just enough for the machines to become intelligent enough to help the humans to help the machines even more efficiently in the next iteration of Tauchain, over and over again.
Humans and machines will seek more good and less bad for the formal specification of Tau itself. Good and bad designs will be defined collaboratively by the human participants by way of intelligent discussion. As discussion scales, bigger crowds means more human minds involved, which means improved design, which leads eventually to a better and perhaps wiser Tau, which of course would lead to wiser even more intelligent discussions, which can lead to an improved formal specification, and to a better Tau. So that is a loop. It is also a loop between improving Tau, improving society, improving Tau, improving society.
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).
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?
Moralidad artificial: Agentes morales y Tauchain. Por Dana Edwards. Post traducido al español por Tokuyama y publicado en Steemit. 28 de junio de 2017.
En un artículo previo debatimos el valor de la ampliación de la inteligencia con el ejemplo de la ampliación a la moral. Ahora voy a entrar en algunos detalles acerca del concepto de la “moralidad artificial” que pertenece a los agentes autónomos. A un nivel filosófico hay diferentes caminos sobre los agentes autónomos, pero haré una lista de algunos.
Para este debate vamos a hablar del tercer punto pensando en los agentes autónomos como una extensión de tú personalidad digital. Específicamente, como personalidad digital me estoy refiriendo a cómo será cuantificación y digitalizado. Nota: No voy a elaborar en este artículo sobre “lo que será” o si existe o no “el libre albedrío”, ya que es un debate filosófico enteramente aparte, sino simplemente utilizaré esta definición como una forma de pensar sobre la moralidad, la responsabilidad y los agentes autónomos.
¿Qué es la agencia moral?
Teniendo en cuenta la cita anterior, ¿Hay alguna razón para creer que un agente autónomo no puede llegar a entender la moralidad humana? Podría predecir que no sólo los agentes autónomos entenderán la moralidad humana, sino que podrá entenderla mejor que la mayoría de los humanos. Los seres humanos no tienen una comprensión muy clara de la moralidad humana debido a las limitaciones del cerebro humano, la complejidad del mundo y de las otras personas. Los agentes autónomos son la manera de manejar esta complejidad que redunda en un beneficio para los seres humanos.
En Tauchain habrá una base de conocimiento a nivel mundial (KB “Knowledgebase”) con alguna similitud con la Wikipedia. Este conocimiento (KB “Knowledgebase”) si se estructura de forma correcta será capaz de aceptar la contribución de personas humanas y AI agents (Agentes de Inteligencia artificial). El conocimiento de la moralidad al nivel del sentido común será posible, pero ¿hasta dónde podemos llevar el enfoque de la base del conocimiento base (KB) + el acercamiento de la inferencia? La moral deontológica se puede utilizar fácilmente en este contexto porque la lógica deontológica puede ser entrada en Tau que permite el razonamiento automatizado sobre la base del conocimiento de acuerdo con la moral deontológica. Pero, ¿pueden los agentes autónomos ser responsables de sus acciones?
¿Qué es la moralidad artificial?
Los agentes morales artificiales, tales como los agentes autónomos que han sido facultados para tomar decisiones, pueden tomar decisiones morales. No sólo los agentes morales artificiales pueden tomar decisiones morales a la par con los tomadores de decisiones humanos, sino que pueden superar las habilidades de los humanos para la toma de decisiones.
En algún momento del futuro los agentes autónomos podrán tener una mejor comprensión de las normas sociales actuales que cualquier ser humano en particular. Este conocimiento de las normas sociales y de la moral común ayudaría al agente autónomo a navegar dentro de un paisaje social, un paisaje legal y más. La teoría de juegos y la teoría cooperativa de juegos destacan cómo los jugadores racionales procederían bajo ciertas condiciones de información limitada. Los agentes autónomos son capaces de ser a la vez actores racionales, pero también con cierto nivel de comprensión moral y, lo que es más importante, una capacidad para procesar mucha más información que cualquier persona individual. Esto significaría que un agente autónomo tendría una comprensión completa de las leyes, y sería capaz de reducir el riesgo de las consecuencias jurídicas mejor que cualquier humano que tendría que trabajar con una comprensión limitada de las leyes.
La prueba moral de Turing
¿Cómo medimos el desempeño de los agentes morales artificiales? La Prueba Moral de Turing puede ser la respuesta. No basta simplemente con crear agentes morales que creemos o esperamos que van a actuar de manera moral en situaciones difíciles, por lo tanto, en su lugar podría ser necesario probarlos y aceptar sólo a los agentes morales artificiales que pueden pasar la prueba. Además, las simulaciones y otros enfoques pueden ayudar también, pero porque no todos los eventos se pueden predecir con antelación debe haber una manera de mejorar continuamente el diseño de los agentes morales ganadores y por esta razón será importante permitir que los seres humanos lleven a cabo mediciones y revisiones de la conducta de los agentes morales como un medio para promover una especie de evolución artificial.
Los agentes autónomos pueden mejorar el mundo, pero en mi opinión debe hacerse hincapié en asegurarse de que estos agentes autónomos son de alto estándar. Esto incluiría, al menos, que de alguna forma tuvieran ética y, para hacerlo, podría ser necesario hacer experimentos de moralidad artificial. Los agentes autónomos en Tauchain pueden ser agentes morales y necesariamente tienen que serlo. Si se hace correctamente, entonces los humanos podrán controlar a estos agentes autónomos si se salen de control, para mantenerlos dentro de la moralidad, e incluso los pueden diseñar para evolucionar continuamente, de este modo pueden ser cada vez más morales al aprender nuestra moralidad, como individuos y como grupo.
Post traducido por tokuyama. Post original de Dana Edwards en Inglés con permiso del autor para su traducción al español: Artificial morality: Moral agents and Tauchain
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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.