December 2006


Just before I’m finally getting my MacBook (ETA January 6th) & some time before all desktop apps are abandoned in favor of webtop ones, here’s a list of some general desktop apps I just couldn’t have lived without (BTW, they’re all free!). If you haven’t already, I really recommend giving each of them a try.

  1. FreeMind (All platforms) – Once you start using it, you just can’t go back to the boring text documents paradigm.
  2. John’s background switcher (Windows) – You just must use this one: fetch Flickr desktop wallpapers periodically, according to tags, text search, photograhpher or time!
  3. Virtual Dimension (Windows) – Why settle for just one desktop, when you can have N simultaneous ones?
  4. Ditto (Windows) – Great clipboard manager, so that you won’t lose what you’ve copied.
  5. Open Iris (Windows) – A Semantic Desktop from DARPA. So cool & useful!!!!
  6. Protégé (All platforms) – A very friendly & extremely useful Knowledge-Base editor, with interface for both simple (frame-based) ontologies & advanced (OWL-based) Semantic Web ones. (Good for ordinary people, really!!!)
  7. StarLogo TNG (All platforms) – The only type of tool letting you actually see & understand the complex systems around you. The TNG version allows non-programmers to easily create amazing simulations.
  8. Tomboy (Linux) – Cool wiki sticky notes.
  9. Stickies (Windows) – Useful sticky notes, with alarms.
  10. EverNote (Windows) – Cool note taking application, with timeline & categorization.
  11. Unlocker (Windows) – Useful utility that tells you & comes to the rescue when some resource is held by some program.
  12. i.Disk (Windows) – Let’s you see whose taking your disk space.
  13. FreeUndelete (Windows) – Let’s you easily recover deleted files. Works like hell!

I’ve removed the apps that are too obvious, although so vital & valuable (e.g., FireFox, Skype & SecondLife), & also those that are too technical however valuable (VMWare, Denim, YALE &c). For a more complete list of apps I’m using, check out http://myprogs.net/dibau_naum_h

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

 

The MIT emerging technology conference 2006 features some must hear talks:

  • Amazon’s founder Jeff Bezos presents their 3 new innovative web services – Mechanical Turk, S3 & EC2 – in way every person dealing with IT will be forced to change his mindset & immediately sign up for the services. The motivation of Amazon becomes very clear & extremely important: completely take from each comany or new venture every infrastructure service that isn’t core to its business, & offer it in a completely new & efficient business model.
  • Mark Chapman of IBM presents a remarkable survey of 750 CEO’s & the main conclusions that it yielded, which are also amongst the most powerful memes in the business world for some time now: Collaboration with the external eco-system & the importance of Business Model innovation. Another important message is that the only barrier for utilizing these vital concepts is internal – changing the culture & thinking of the organization.
  • & finally Sebastian Thrun head of the Stanford AI lab, gives an entertaining talk about his robot Stanley that won the DARPA grand challange. What I interpret from his talk is the key role that the software & its innovative architecture had in achieving this amazing challange, which basically is one of the greatest early versions of a real autonomous machine. Quite amazing to see the emotions Thrun have for his robot…

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