Architecture of Emergence & Memetics, Part II

Here’s a core concept of my emergence engine: model based evolutionary value creation. Basically it means the following:
- You get stories depicting some domain, i.e., user answers or tweets
- You translate them to semantic concepts & statements
- You give these concepts & statements (model parts) behavior, which basically tries to create value for the end users, or the end users organization
– Simplest example is when some statement can bring value to some user if he learns about it
– Another example is when several statements reason the causal relations between them & infer what is the root cause to some phenomenon
- Having all of the model parts behave all the time is compute intensive, which has a cost
- Not all of the model parts have the potential to create value
- So, an evolutionary process can take place, in which only the model parts that succeed in creating value survive & get resources.

See also my initial post on the base concept.

 

Blood vessels inspired mechanism for driving social interaction across the enterprise & accelerating change

A. The business problem
Large organizations don’t work as one body. This is problematic, because of numerous reasons. To name just a few common ones:

• The agenda, priorities, focus & policies of higher management may not be known or implemented by the lower level units & employees, leading to garbage factories, missed targets & activities not aligned with company policies & agenda
• Adapting to changing business environment is slow in large organizations, because of the difficulty in changing both processes and value streams, as well as the mindset & knowledge of the people running them
• Problems and threats, or on the other hand innovative ideas and opportunities are not handled because they’re usually not propagated from the lower level units to the executive level
• A need for activities in one unit, with not enough resources, can’t use free resources in another unit, as well as existing know-how or goods already achieved in other units

B. An analogy: blood vessels
Blood is the source of life to all organs and cells in an organism’s body. It supplies all cells with the inputs required & returns their outputs. It carries commands sent from the brain to organs, via hormones, as well as the necessary chemicals & food the body tissues require. It also spreads solutions for disease agents across the body.
In order to reach every cell, in all organs of the body, the blood vessels are organized in an hierarchical structure, starting from the heart & lungs, and spreading downstream till every cell, & back to the lungs & heart.
Blood flows in frequent cycles, circulating the means of life continuously across the body. It interfaces with the cells via special membrane controlling the flow of materials in & out of cells.

A business enterprise is a large multi-cell organism, that uses knowledge to transform supplies into goods, in a complex value stream. What ties the multiple humans working in an enterprise into a large super-organism, are the communications between them, that coordinate the processes comprising the value stream to drive the enterprise to survive & grow in a dynamic market environment.
However, we believe that these vital human communications are based on paradigms that were established & formed in the pre-computer-mediated era, & for sure before the new social interactions paradigms of the Web 2.0.
We suggest a simple mechanism, inspired by the architecture of blood vessels, for making the communications inside the company flow from the top management to all employees, and also across the entire organizations, in order to help the enterprise work as one body.
C. Social media capabilities
We propose new social interaction “vessels”, flowing from the CEO, through the organization structure, to every employee, and also aggregating the responses from the employees all the way back to the CEO. The basic component of social interactions is a simple short text (the kind passed in services such as Twitter, normally limited to the size of Short Text Messages), along with simple discussion semantics such as “in-reply-to”, “for”, “re” prefixes. It’s highly important that the input from employees will be in this open-ended form, & not forced into any structured schema form, in order to promote the discovery of insights not known in advance, and the emergence of bottom-up new usages of the system. The text can contain marked tags, that characterizes its content, either given by the user, or automatically marked by the system. The system will query users every day for status messages, that may contain such information as their:

• priorities & focus
• risks, undesired effects & problems
• achievements, opportunities & value drivers
• work status, load & health
• requests & questions

Querying can be done in any communication channel used by employees (IM, SMS & Email, &c).
Every day, each employee will also receive a collection of messages from the hierarchy path above it, from the CEO, to its direct supervisors.
Using the tags, messages can be passed between users not in an hierarchical path, just on the basis of similar tags. Tags may also be collected in user’s profiles, so that a user who once wrote a message containing a certain tag, will receive future messages with that tag by other users.
Every manager will receive daily a collection of messages from the hierarchy path beneath it. Aggregation, clustering & classification can be done on the responses, to make the results arriving upwards summarized. Visualization methods can be used for presenting a GUI that enables both topsight view of the aggregated results, and ability to explore area of interest.
Messages may be marked with an importance indicator. The higher the importance a message is ranked, the more exposure it will have across the enterprise.
Users will also be able to vote on other users messages, & increase their rank & exposure.

An important use case is with risk management. A user may enter a message describing a risk it wants to put to discussion, and rank it with high importance. The message will be exposed to many other employees across the enterprise, that are related to the tags in the message. Their responses will form a multiple stake-holders discussion, which is the best known way to prepare & address risks.

When considering the extended enterprise, certain units & employees interact with external people (suppliers, partners, customers &c). The flow of social interaction can flow through them to the external people, & back to the CEO. Similarly, the chain can start from above the CEO, e.g., main share holders, allowing them to both interact with, & learn on the overall, current status of the company.

The frequency cycle of this process should be as high as possible, such as 1-2 days, but can also be once or twice a week.

D. The utility of solving the business problem social media capabilities
Unlike the common communications practice in today’s common enterprises, in which the the frequency of communications between the top executives and the employees of the lower level units is limited to 1-4 times per year, and usually also limited to unidirectional communications, the suggested capabilities can foster bidirectional communications on a daily basis, flowing from top management to all employees, and also between employees across the enterprise. This is very likely to address the common reasons given above why enterprises don’t work as one body:

• The agenda, priorities, focus & policies of higher management will be known & implemented by the lower level units & employees, leading to the elimination of garbage factories, met targets & aligning all activities with the company policies & agenda
• The frequent circulation of knowledge on changing business environment, and the way the company adapts to them, can create positive feedback loops, that incrementally change & adapt processes and value streams, as well as the mindset & knowledge of the people running them, to the changes in the business environment. Using today’s practices such adaptations can take years, but may be reduced to only weeks using the suggested capabilities.
• Problems and threats, or, on the other hand, innovative ideas and opportunities will be properly propagated from the lower level units to the executive level, which will enable their effective handling & value extraction
• A need for activities in one unit, with not enough resources, will be communicated to other units with free resources, or relevant existing know-how or goods already achieved, which will enable collaboration, higher resource efficiency & considerable time saving.

The main benefit for the end-users will arrive from the much stronger bonding & involvement with the entire organization, that can make employees feel more motivated and appreciated. Other obvious benefits are greater responsiveness from their management, & stronger collaboration with other employees across the enterprise.

E. The information that will be collected and how it will be useful for the enterprise
The communications that will circulate in the social vessels may contain valuable information, of many kinds:

• Risks & threats that may be handled immediately after they are discovered, instead of after they cause their damage
• Opportunities, value-drivers and innovative ideas that will arrive to the executives that can understand their value & decide on their implementation
• Trends that may point out problems, and emerging changes, that can be handled before they are reaching a critical mass or serious effect
• Insights on external factors outside the company, that can arrive even from low-level employees, and hold important strategic opportunities or threats

Once the collected information reaches a sustainable size, it can be applied to analytics that can provide both macro insights on the status & health of the entire organization, and well as micro insights on bottle-necks and inefficiencies that can be removed to effect bottom-line profits. For example:

• Analyzing negative sentiment in messages, based on Natural Language Processing, can indicate trends in cultural health problems, in certain area of the company or across the organization
• Aggregated messages tagged with over-load, at certain units or type of resources, may indicate a bottle-neck that delays other units & processes

F. Ensuring privacy and security of information gathered
While the querying interface of the system to employees are common media channels, such as SMS, IM & Email, the outputs of the system (collection of message from the hierarchy above, and aggregated responses from the hierarchy below) are presented in a Web application, that can be protected by any standard of security, for both authentication protection and authorization. Messages can also be marked with security level, to make them available only to employees with access to this level. Any other organization policy, taking into account organization structure or roles, can be applied to determine authorization to view messages.

 

Hierarchical evolution?

Howard Bloom points out that evolution applies more to groups than individuals. So obvious!

I wonder whether it makes sense to say that evolution is really hierarchical, applying to composite elements in different levels: cells evolve, human evolve (Lamarckian style), communities evolve (families, towns, nations), species evolve, eco-systems evolve, worlds/universes too?

 

emergence & ccd

as much as emergence is powerful – social mechanisms yielding successful colonies, they can be amazingly self-destructive. Colony Collapse Disorder (CCD) is a model. 60% of bees in the states have disappeared. You could blame a specific material or conditions, as many people suggest, but does it make sense when the phenomena now emerging also all over the world?

We’re witnessing a global scale self-destruction reaction of bee colonies. Such reaction seems to me like an emergent behavior, in which the colonies suicide for the benefits of their genes, just as one bee commits suicide when it attacks, for the benefit of the colony.

As bee colonies are a good model for human colonies, I think there’s nothing more important than researching CCD, in order to be prepared for it in our species. A colony self-destruction gene is probably not endemic to bee colonies.

Efforts by visionaries to get humans to space as backup, may not help as the CCD may be not limited to the global.

If you’re researching CCD & need any help, I’m available for any task.

 

Architectures of Memetics & Emergence (part I)

Motivation: autonomous value creating applications

I’ve been working on a very pretentious platform which I hope can prove useful for innovative applications. The platform is based on 2 main principles, Memetics & Emergence. Both are originally taken from the world of human culture & sociology. In the architecture of this platform they are applied to a complex composite Multi Agent System. The motivation behind it is to try mimic the way human individuals, organizations & societies succeed in very large complex tasks, whereas it be a single human, small team, business corporation or a whole society. The fact is that a single human, or any organization of humans is usually good in doing something, called Creating Value. The fact that I earn money is because I create value for my employer; the fact that some company makes money is because it creates value for its customers. So the general motivation for an architecture that tries to mimic human or human organization is to enable software to create value. It isn’t that existing software today doesn’t create value, the only reason software exists is because it creates value. But, unlike software, humans aren’t (explicitly) programmed – they are given some initial knowledge (education/training), they are assigned some jobs, & they create value while collecting the knowledge & expertise in doing it. And this is the motivation. A task such as enabling applications to create value without being programmed seems complete Science Fiction today. So we require something very novel & innovative & something very new to basically be able to claim that we can build such applications, that create value without being programmed, except for some basic education: when assigned with a job, performing it, improving in it & creating value without being specifically programmed as to how to solve each case. The motivation is to create software that just like Humans, even when provided only with basic knowledge of what to do in each case, still can:

• Solve unexpected situations,

• Create value in unexpected ways,

• employ both common sense &

• the ability to learn from situations &

• improve its performance, i.e., the value created,

• by merely performing the job for sufficient amount of time.

 

Memetics & Emergence

So, what are the architecture components that we claim may produce this?

Let’s start with Memetics. Well, memetics basically is the theory that there is an evolution of ideas, where ideas are taken in the broadest sense of things that you copy/learn from others. This evolution is for what is called human culture, science, art, & basically our whole social life is based on memetics. For an ultimate introduction to Memetics, I highly recommend hearing or reading the proponent of this field, Dr. Susan Blackmore. This is the basic idea. This idea can apply not only to the humans world, but also to general intelligent agents. When applied to software, Memetics basically means Evolutionary Knowledge Engineering. The idea is that whereas in knowledge engineering we produce knowledge representations of a domain, including also knowledge required to perform tasks, i.e., Behavioral Knowledge, in Evolutionary Knowledge Engineering, we apply the evolutionary algorithm to this process of knowledge engineering. Meaning that, if we have variations of knowledge representations & if we have different versions of how to perform tasks, only the fittest of these pieces of knowledge will survive & be the base of the knowledge base population. The effect of this is improvement in our knowledge, which becomes more adapted & effective in it’s domain environment. So, to recap, memetics is all about people spreading ideas, & the ideas that are the fittest – most fruitful & valuable – are the ones that survive & base the population of ideas. Similarly, Evolutionary Knowledge Engineering is just Memetics applied not to the culture of humans, but to any society of agents performing knowledge engineering.

 

 

The 2^nd concept called Emergence, is basically a claim that high-level intelligent behavior can be obtained from low-level simple agents, whether it be animals, software or any object with some behavior, when you combine them into a group, that works together. So any time you take a bunch of agents & combine them into a group, even though each of them has a very simple low level behavior, that may not present any intelligence whatsoever, i.e., any complexity, any reasoning behind it, nevertheless, when you combine them into a group, that works together & collaborate, suddenly the group has an higher-level intelligent behavior, in other words, the intelligence emerges from nowhere, by just combining the agents into a bigger unit. For a great introduction to this concept, with numerous eye-opening examples, I highly recommend reading the book on this concept, by Steven Berlin Johnson. Normally, we think of emergence in situations when the intelligent behavior emerges unexpectedly, but I prefer to include any high-level behavior formed by the collaboration of lower-level parts. E.g., a Power Ranger has this amount of power, but when a team of Power Rangers connect together & morph into a giant all-mighty robot, I also see it as emergence. Now this of course may recurse, for example, if you take a group of A-type agents, & combine them into a group, called B-type, & then combine several B-type agents, into a group called C-type. Now the C-type agent can then manifest even higher level of intelligence than B-types agents, & this is like multiplication of the power of emergence, because we start from simple very low-level unintelligent A-type agents, & multiple the emergence effect & get C-type intelligent agents.

Illustration of emergence: combination of many simple pixels into a group, creates complex intelligent picture (Original image by Matt Champlin)

And another illustration: it’s hard to model a 3D shape, e.g.:

 

But if you zoom to a much lower level, you can model the shape, e.g., using many simple triangles:

 

Emergence examples are all around us, everywhere you look, & it’s enough to mention the extreme intelligence (learn & behold) of Ant colonies as a very obvious example. Each ant doesn’t manifest high-level intelligent behavior, but when you combine them into a group, you get a very powerful & successful intelligent behavior. Ants are a very good example, but if you think about it, take any group of humans, whether it be a family, team, community, organization, city, nation, any group of people, is strong because it has more intelligence & more power, that is ability to solve large problems (i.e., Intelligence), only by combining individuals into a higher-level group. In corporations, or hierarchical organizations, we see the emergence multiplication effect, where we take several people into a team, & then take several teams into a department, & then take several department into a division & so forth, we see that more power & more intelligence, more high-level behavior, come out of the group as we multiply the emergence effect. We must understand that it is not the sum of power of the individual components. Take for example a branch of a fast-food chain. The power & intelligence of it, isn’t the sum of the power of the staff running it. The added power & intelligence of these workers isn’t enough to feed thousands of people each day. These young people don’t necessarily understand the process & knowledge, & the sum of their intelligence isn’t enough. Put them all in a room, & you get no special intelligence & power to feed many people. The intelligence is in the fact that working together they create some higher-level machine. They create something that is very powerful, feed thousands of people, but it is not the sum of their intelligence & power. The intelligence is in the combination of them into a collaborating team. Everyone are doing their low-level job, & you get a very powerful higher-level machine. Once they combine you get the emergence effect. Suddenly a bunch of teen-agers feed thousands of people. (This is just an illustration, please don’t take it personal if you happen to be a teen working in a fast-food branch…)

 

 

You could say that both Emergence & Memetics, are nothing but metaphores, ways to see things, which humans have always known. But as any science theory is just a way to see stuff, judged by its fruitfulness in predicting measurements, I believe with these concepts you understand how come human ideas & knowledge improves all the time, & how come the teaming of humans into special types of groups yields so much power, & once you understand it, you can harness this in human life, to create new types of mechanisms, for example as the social services harness the concept of emergence, file-sharing networks, Web2.0 social services, all exemplify it in numerous examples. You can also harness these principles into architecture of software agents, which is what the platform I’ve been working on is all about.

 

Illustration of a simple composite architecture based on Memetics & Emergence

 

 

On emergence in music concerts & similar religious events

I find music concerts to be a beautiful example of emergence (as are also other religious events, such as sport events, movies & parades). A group of people, inspired by the same art/activity, becomes one body (in a higher level), having for a limited time period unified perception, feelings, goals & perhapse even consciousness.

(Images from a great concert of Eran Zur, Assaf Amdursky & Shlomy Shaban taken by my friend Ran Mendelson)
Why does this emergence happen? What purpose is the higher-level creature – the congregation – serving? I would like to learn/think more to answer this, but in the meantime will just continue to enjoy it.

Of course, it requires one to choose the event & group of people matching it, & of course we must be extremely careful from the daunting horrors unified groups of people can together do (too too many examples in the previous century).

Long time ago, I imagined a movie ending scene in which a train passanger suddenly takes out a violin, & starts playing the slow movement from L’estro Armonico #9 by Vivaldi. After a while, some other passangers take their violins out as well & join the playing. Eventually, all train passangers play the music, & as they go out from the train, everyone else on the street join them.

Just strange memes flowing in my mind.

 

Engineering decentralized multi-human value vending machines

I’ve been working for a few years on Knowledge & Emergence Engineering, combined also with Machine learning, & am very excited to prototype engines based on the synthesis of these technologies. What these technologies enable are new types of adaptive & autonomic information systems, capable of delivering value without programming & maintenance. It only needs to receive initial common-sense & domain knowledge, including the knowledge on how to create & engineer value, & from there it goes on by itself to pursue its goals, & constantly improve its effectiveness. Really exciting.

Now, one of the types of mechanisms that could benefit from such engines, are a new kind of social mechanisms, that dynamically bind humans & computers into economic machines, i.e., decentralized dynamic companies.

Such companies exploit the powers of both carbon & silicon based agents, & are glued together by the management mechanism implemented by the emergence engines. Think Amazon Mechanical Turk, but with a learning automatic managers, constantly working (without human intervention) on driving end-to-end business processes.

Such mechanisms could eventually leverage the unexploited time & intelligence of many unemployed human population, without requiring special knowledge or initial expertise, & help them make money. Normally, their common-sense & social knowledge will be enough to achieve their queue of tasks, automatically assigned to them by the managing engine.

I don’t have enough time to play with these engine, in the required amount, so I can’t promise anything. But I’ll most certainly be happy to get rid of the exploiting-evil & overhead of millions of human managers, that could efficiently be replaced by automatic mechanisms, for the benefit of their poor subordinate egg-heads.

 

Test case for yielding emergence

I was thinking on the simplest way to test my emergence engine, & came up with an extremely simple task – the reactive algorithm of a thermostat: measure the temperature, & turn the heating on & off to maintain a given temperature. It sounds indeed very simple to code a program that does that, but what I’m going to experiment is how to do it without any programming.

Emergence engine is a kind of general AI, capable of achieving goals, without being programmed how to solve them. It’s based on the assumption that you don’t need to build real intelligence, rather just create many many simple software workers, having only very simple tools & logic, & let them swarm their way toward the system’s given goals.

So, here’s how I hope my engine will handle the test case:

• It should 1st learn by elicitation the model of a room, having a temperature, thermometer & heating unit.
• It should also learn the relevant beliefs on the effect of using the thermometer on the accuracy of the model, & the effect of turning the heating on & off on the room’s temperature
• It should then learn what’s the desired temperature
• From this it should start deriving action plans & execute activities to achieve the goal of maintaining the desired temperature
• It should also adapt to changes in the room, e.g., a door is open & there’s need to use more heating, or alternatively the heating doesn’t work & we need an alternative heating unit

I’m saying it but of course what’s doing all this are many collaborating agents, working together to achieve the goal. This is done by breaking the value in the goal into smaller value “summs” given to states & activities leading to the goal, & having the agents collaborate on creating all these summs.

Although the design is very simple, & intended for complete autonomous behavior, I noticed that I’ll be able to effect the engine & help it reach its goal, by changing the knowledge driving it, i.e., the learnt beliefs, according to which the agents work.

So, I can’t wait to see how the engine will handle this, which will actually test whether the simple emergence design is enough to yield emergence, even if the value it delivers is so small & simple.

 

Post by the cell

in light of the recent (past ~100 hours/days/monthes/years, all the same) events in my organism (jewish ethnic group of the israeli nation), I want to ask a small question: how can a cell make a change?

Those recent events include violent conflicts with neighboring organisms (Palestinians, Lebanese) in an endless vicious circle, with no real progress in solving it’s root causes. These violent conflicts include terrorist attacks on soldiers, followed by bombings of these terrorists taking the lives of children &/ their parents.

Normally, a cell has limited effect, as his micro behavior & choices are just aggregated along with those of millions of other cells, creating a statistical emergence of macro behaviors & trends.

Though, there seems to be exceptions, in which a small action brings a huge (normally very negative) snow-ball effect, e.g., an assasination of a king, or taking over airplanes using simple knives & flying them into buildings.

But normally, cells make changes only by small & steady growing effect on their neighboring cells, e.g., a pacifict attorney convincing people to non-violently fight for their emancipation.

As the macro behavior emerges from the communication & interactions between the cells, you might expect that changes in the messaging medium of the interactions may effect the emergence, & its speed. Do super human organisms, using mass media & Internet communications change their macro behavior faster as a result of cells micro behavior changes?

Maybe the influence is always similar in pattern: a cell’s micro behavior change may have some influence on his neighboring cells, which may recursively distribute it further, climbing a gaussian curve of adoption, depending on the power/value of the change, until it passes or not the line between micro & macro behavior.

This mechanism of change propagation happens in the mechanism of memes, which spread in the population, as a function of their value/survival power, & their success in propagation through communication.

In my super organism, it was a small meme held by very few cells that the conflicts with neighboring organisms are the results of root causes, which drive the conflicts & fuel it. For example, the meme of proffessor Yishayahu Leibovitch claiming in 1967 that the Israeli occupation is bad & must end, took almost 40 years until it has spread & held even by the politician leaders.

But, alas, it seems like it will take many more years, & many more tragedies, until the macro behavior of middle east organisms will change & adopt the intelligent powerful peaceful coexistence behavior, as adopted in other areas of the world, located somewhere else in the memetic evolution.

So, how can a cell make/accelerate a macro change? In the past, people that made such changes were assumed to have super-natural powers, such as Jesus & Jeanne D’Arc. Does the magic of new technology endows individual cells with super-natural powers?

I was thinking of several directions, such as:

• Execute memetic “terrorist” actions, that will effect people by non-violent art (social sculptures), toward the memes of peaceful coexistence, & compassion toward different organisms’ cells.
• Use social technologies, that group many cells & providing them more macro power.

The effectiveness of such directions needs to be assessed, & then be used as fast as possible, because every day that passes, so many people are being so much hurt.

“The law of God, is the law of change.” (G. B. Shaw)

 

Michal Rovner’s art & a local demonstration

I was lucky to see today an exhibition of this great artist, providing magnificent visualization of emergence, using projected video of many humans, seen as an higher level phenomena.

After the exhibition, in the Tel Aviv museum of Art, I passed by a demonstration against the keeling in Lebanon & Israel. It was an unusual voice, coming out of people deviating from the super-organism of Israeli society, which is quite united behind the massive attacks on the Iranian-backed pro-Palestinian Hizbolla. One must have a quite topsight vision to see beyond the eyes of his tribe & feel for the people in another one, with which it is in a war. Though obviously those other people are quite not to be blamed for the war inflicted upon them due to the terrorist activities done by a group backed by another country. It’s really sad that this area knows only the language of violance, & the poor people that just want to live their lives have to die or suffer. It’s only slightly re-assuring to see people saying that it’s wrong, & I’m quite admiring their vision & courage to say it.

 

StarLogo TNG

I mentioned StarLogo before as an Emergence-based framework, & just found out that they have a new version – The Next Generation. Take a look on the new interface:

Seems like their trying to make programming accessible & simple as visual puzzle building, & the runtime view as intuitive as video games. What a cool approach to simplifying programming: suit it for children.

I've worked with a process-based development environment, which I believe is the enterprise-class state-of-art visual programming (Tibco's BusinessWorks), but the StarLogo approach to logic programming is completely different, taking the physical machine building metaphore instead of the process diagram one.

Another interesting new feature is the ability to see the simulation from one of the agent's POV (& in cool Quake-engine-based 3D…). This doesn't provide much more emergence-visualization insight, but it does help educate the observer about the relevance of emergence simulation & our world: we're simply part of a multi-agent emergence-based "simulation". The visualization is very fast, as the simulation speed is normally faster than life, but is still quite amazing. It reminds me the accelerated speed in Reggio's films, which was his trick of educating his viewers on the machine of which they're part.

 

Finally understood the architecture of the world

The famous programmers quote that “to iterate is human, to recurse, divine” has some good insight. The architecture of the world is simply: recursive multi-agent system. The pattern is so visible & clear: watch every piece of the world & you’ll see many agents, interacting with each other & pursuing their goals. Zoom in & you’ll see that each agent is composed of many smaller agents, that are similarily interacting & pursuing their goals. Zoom out & you’ll see that from the agents interactions & goals pursuing, emerges a higher-level behavior, which turns all of them into a larger agent.

(the great Powers of Ten movie by IBM)

If this is how the world is designed, all the OOP/AOP architectures have a long way until they’ll be able to model anything properly, instead of just stuffing tons of logic into useless huge complexity. Only some Emergence based new architectures (such as Echo & StarLogo) are starting to model the world as it really is.

 

Imagination-Bases

Data-Bases describes the data of the existing world, i.e., the empirical recording of facts, used for machines that were programmed to process the world.
Knowledge-Bases describe the existing world, i.e., the ontological knowledge transfer required for machines (or aliens) that need to sense & affect the world.

Imagination-Bases describe possible worlds, i.e., the simulation of potential future worlds, that humans & machines can build & live inside.

IB’s require the same knowledge representation facilities as KB’s, but also Simulation facilities & the sensual-stream generation capabilities.

There are some interesting IB projects (spring-alpha, second-life), but too bad we don’t have the legendary Imagination Engineers of the past, such as Jonathan Swift.

“Imagination is more important than Knowledge” (Albert Einstein)

 

Kak’s article

Reader of this blog, if you haven’t already, please proceed to read Subhash Kak’s article: Artificial and Biological Intelligence

Some remarks:

• A principle of emergence is self-organizing. Reorganization is a primary process of intelligence.
• the self-awareness of the humanity animal is somewhere else, encoded in a different language & world model.
• an ai could work on the science of understanding humans, as they themselves can’t
• quantum computing theory may explains the brain (remember von Neumann’s quote in Dyson’s article, that logic will have to pseudo-morphose into neuroscience, & not the other way around.)
• “unification of minds or consciousnesses”
• (to be continued)

 

Remarks after reading George Dyson article on Turing, von Neumann & Google

Very cool article: Edge: TURING’S CATHEDRAL by George Dyson

From the perspective of an agents framework’s developer, I have these remarks:

1. ontologies specify behavior. edit the ontology & the software behavior changes. why?
2. create a full corporation of software agents, with hundreds of agent types & thousands of agent workers.
3. the ontology defines the behavior of all agents – their genes, then the ontology also contains the knowledge learnt by the agents – their brain.
4. the key ingredient for agents corporation is communication: normal communication for regular behavior, & spontaneous communication for mutated adaptive behavior.
5. Google is the access method to the global knowledge base of unstructured data. Google should be used extensively by each agent, in order to find new knowledge relevant to its behavior.
6. There is a barrier between machines & humans today: machines can only answer questions that programmers defined well & hard-coded the procedure for answering. most questions people have are not (yet) well-defined & programmers still didn’t define procedures for them. Agentier should be able to communicate directly with people in order to solve “questions whose answers are, in principle, computable, but that, in practice, we are unable to ask in unambiguous language that computers can understand.”
7. Basically this means that a good agent framework should be human brain compatible: it should solve problems that the human brain normally solves.

Quotes:

“An argument in favour of building a machine with initial randomness is that, if it is large enough, it will contain every network that will ever be required.” (Irving J. Good)

“It is much easier to find explicit answers than to ask explicit questions. And some will be answers to questions that programmers wil never have to ask.” (G. Dyson)

 

Human tissue project

Picture:

human tissue, made of 6B humans. what makes it a tissue are connections. connections are made thru “chemicals”, manifested as feelings felt by the humans. such connections bond families, friendships, organizations, communities. other connections are done by means of social mechanisms.

the imagined outcome is a clear zoommable picture, that explains the bonds, the layers of the tissue, & the macro behavior of the tissue: what kind of animal is it, what does it do.

to get there, I can take the normal biological investigation methodology, or try a more rigid one, using description logic (OWL) & simulations (StarLogo). I can try use Wolfram’s New-Kind-Of-Science methodology, which seems to be so fruitefull.

Next task: map the chemicals. (not yet the social mechanisms)