Web Wide Matrix

For many years, I’ve been really suffering from the problem of information overload: I’m subscribed to many content feeds, which are very valuable to me, but don’t have any way to effectively consume them – even if I’d sit all day & read them, I won’t be able to cover all of them.

Thinking about this problem I understood 3 things:

  1. The problem starts with the medium – the interface in which we consume content feeds today is like a baggage carousel, where you need to look at a list of items, arriving in random order, until you see what you’re looking for. This interface is somewhat effective with few dozens items, but will never work with thousands. On the other hand, an interface such as a supermarket is effective for selecting a few items out of thousands of items, because it’s an organized space that you can walk into & see all items, & since you go there frequently you know exactly where to find what interests you & discover more along the way.
  2. There are many good technologies today to automatically process large amounts of information, in order to enable people to make value out of it, but to use these technologies you need a team of engineers working for weeks or months building a solution for some specific content feed & use-case. This is similar to the situation before the Web, where if you wanted to connect to some information you needed to build the software, protocols & infrastructure for accessing information remotely. The Web introduced a simple & powerful standard way to access information, that removed these barriers & enabled anyone to publish & access information from anywhere in the world. Similarly, we need a simple & powerful way to enable anyone to process any content feed & make value out of it. This should be as simple as writing a simple HTML file.
  3. Until today, every person needed just an internet connection & a device to access information – something to run a browser on & access information & services. However, by now we stretched to the limit our ability to consume & make value from the sea of information available to us – Slack/Email/Articles/Twitter/Data/Opportunities/Events/&c. The introduction of powerful machine-learning & autonomous software agents can now enable every person to have a team of bots working for him by processing information & creating value out of it. So, a browser & internet connection is not enough anymore – you need another software/service to cope with the never ending streams of information flooding you, that needs to be available & affordable for everyone, just like an internet connection & a browser.

So I set out to design an architecture & solution based on these ideas, which I call the Web Wide Matrix. Inspired by the Matrix movie, it is a

  • Virtual Reality interface for consuming content feeds as an organized space
  • generated by a personal team of hacker bots, working inside software hovercrafts to process your content feeds
  • using training courses that are as simple to write as HTML documents

To make this a reality, I’m building this as an open-source initiative, led by a non-profit organization (called the Wachowski groupoid, in honor of the Matrix creators), currently consisting of just myself. I’ve written an initial POC & put up an initial web site for the project. Check it out to learn more & drop me a note if you’d like to join me in solving this problem once & for all.

Screen Shot 2017-04-03 at 6.30.56 AM


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 2nd 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.

Emergence (before)

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

Emergence (after)

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

 3D object

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

 3D triangulation

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.

Logical structure of an architecture employing Memetics & Emergence


Illustration of a simple composite architecture based on Memetics & Emergence


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.

Economic models in Multi-agent systems

I’ve read today in the AgentLink newsletter on some new economics-based models in multi-agent systems. Such systems are themselves intended to bring economic value, by performing some task for their human users. Their human users are themselves part of a multi-agent system (some corporation, or organization) intended to bring economic value to some other human users. The whole humansphere is a multi-agent system based on economic models.

The question is: should the multi-agent systems of the artificial agents be based on economic models in order to bring more economic value to their users, or should we apply economic models to the integrated multi-agent system comprising both artifical & human agents?

I tend to believe the 2nd option is more future compliant, although pragmatically people tend to accept the 1st.