The semantic web - the future of the www

The internet as we all knows it, the Web 2.0, was designed with one thing in mind - people. The web is all about the social aspect of the internet: it was designed in order to help people communicate and share information. The Semantic Web, on the other hand, is designed for machines. While the Web needs a human operator to run it, by using computer systems, it is not possible for a computer to do tasks such as search for information without a human to guide it.

So what is exactly "the semantic web"?

The semantic web is actually an evolving extension of the World Wide Web that adds new data and metadada to Web documents. The idea in general is that web content will be expressed not only in natural language, but also in a format that can be read and used by software agents, thus permitting them to find and integrate information more easily. In other words the Semantic Web focus is to change the focus from people communicaton tool to computer understanding availability platform. This extension is what will soon allow machines to process data on its own or manually. With this being successful, the need of humans to help operate computers would be eliminated, or at the very least minimized.

There are already several examples of Semantic Web potential are several applications that are already in use today:

FoaFA

Popular application of the semantic web is Friend of a Friend (or FoaF), which uses RDF to describe the relationships people have to other people and the "things" around them. FOAF permits intelligent agents to make sense of the thousands of connections people have with each other, their jobs and the items important to their lives; connections that may or may not be enumerated in searches using traditional web search engines. Because the connections are so vast in number, human interpretation of the information may not be the best way of analyzing them. FOAF is an example of how the Semantic Web attempts to make use of the relationships within a social context.

Twine

Twine claims to be the first mainstream Semantic Web app. Twine automatically learns about you and your interests as you populate it with content - a "Semantic Graph". When you put in new data, Twine picks out and tags certain content with semantic tags - e.g. the name of a person. An important point is that Twine creates new semantic and rich data. But it's not all user-generated. They've also done machine learning against Wikipedia to 'learn' about new concepts. And they will eventually tie into services like Freebase.

For additional applications you can visit: http://www.readwriteweb.com/archives/10_semantic_apps_to_watch.php

In my opinion the next important IS developments will be related to the efficacy of Information processing rather than Information gathering or storage. In addition, I think that the next important Information processing leap will be related to web intelligence and specifically, in the near future, to Semantic Web. This tendancy is already shown in the large investments volume of the AI comapnies in the area as well as to the real demand and popularity to some of the preliminary applets. I also think that the development of an automatic computerized system that will successfuly used semantic coding-retrieving system will change the whole way we know and research the IS field today.

The following video explain elaboratively and demonstrate possible application of the semantic web:

Waves of Innovation

In my last post I was trying to describe an analogy between technological innovations cycle and the Scientific revolutions cycle. Recently, I have found out that some of elements of this analogy have already been described in an article by Giovanni Dosi (1982): "Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change".

In the paper the author also stressed the similarity of the procedures and the nature of “technologies” with those of sciencentific research. In particular, the author coined a new term: “technological paradigms” (or research programmes) that are performing a similar role to the “scientific paradigms” (or research programmes) and proposed a model based on this similarity.

The model Dosi proposed, tries to account for both continuous changes and discontinuities in technological innovation. Continuous changes are often related to progress along some technological trajectory which defined by a technological paradigm, while discontinuities are associated with the emergence of a new paradigm (Paradigm shift). Dosi claim that the origin of a new tecnological paradigm stems from the interplay between scientific advances, economic factors, institutional variables, and unsolved difficulties on established technological paths.

The differentiation between continuous innovation and discontinuous innovation may be positive for understanding initiation of a new paradigm as well as position and diffusion of a specific technology or knowledge. For example, the figure below (Hargroves and Smith (2005)) shows six waves of Sci-Tech innovation between 1785-2020, which can also be regarded as six different paradigms. A continuous innovation is what happened in the same wave, while discontinuous innovation is the jump from one wave to the next wave.

Sources

Dosi, G. (1982): Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change, Research Policy, 11 (3), pp.147-162.

http://ideas.repec.org/a/eee/respol/v22y1993i2p102-103.html#abstract

The Natural Advantage of Nations: business opportunities, innovation, and governance in the 21st century. K Hargroves, MH Smith (2005). page 17

The Innovative cycle and The structure of scientific revolutions

What are the basic steps and processes towards new innovation? According to the innovative cycle there are two main parallel processes: Exploration and Exploitation.

The main idea is that the process which includes the initiation of new ideas, the creation and development of new inventions and the utilization them, have a form of a cycle that include two main spheres/directions:

Exploration - Creating new patterns, inventing new technologies. Include things captured by terms such as search, variation, risk taking, experimentation, play, flexibility, discovery, innovation, long term.

Exploitation - Optimizing an existing pattern by making a small steps. Include things as refinement, choice, production, efficiency, selection, implementation, execution, short-term, immediate, certain benefits.

An organization, such as a firm, a government or a political party has to choose how much of their resources to allocate to each of these activities. The innovation cycle is used broadly to describe the strategy of which a company is choosing in order to balance the need for new innovations with the urge to improve the existing ones and fully exploit their potential (An excellent example of the continuous rivalry between the two strategic needs of Pfizer).

This model reminds me some of the ideas described by Thomas Khun on his famous book: "on the structure of scientific revolutions". In his book (published in 1962) Khun made an analysis of the history of science and was trying to establish a model of the basic mechanism underlying the progress and revolutions in science. In his book, Kuhn's argues that the evolution of scientific theory does not emerge from the straightforward accumulation of facts, but rather from a set of changing intellectual circumstances and possibilities. In fact, the basic mechanism described by

Khun includes three main phases of progress:

The first phase, which exists only once, is the pre-paradigm phase, in which there is no consensus within the scientists on any particular theory, though the research being carried out can be considered scientific in nature. If the scientific community eventually gravitate to one of these conceptual frameworks and ultimately to a widespread consensus on the appropriate choice of methods terminology etc, then the second phase, normal science, begins.

In The second phase puzzles are solved within the context of the dominant paradigm. As long as there is general consensus within the discipline, normal science continues. Over time, progress in normal science may reveal anomalies, facts that are difficult to explain within the context of the existing paradigm. While usually these anomalies are resolved, in some cases they may accumulate to the point where normal science becomes difficult and where weaknesses in the old paradigm are revealed. Kuhn refers to this as a crisis, and they are often resolved within the context of normal science.

However, after significant efforts of normal science within a paradigm fail, science may enter the third phase, that of revolutionary science, in which the underlying assumptions of the field are reexamined and a new paradigm is established. After the new paradigm's dominance is established, a process known as Paradigm shift, scientists return to normal science, solving puzzles within the new paradigm. A science may go through these cycles repeatedly, though Kuhn notes that it is a good thing for science that such shifts do not occur often or easily. In order for a Paradigm shift to occur, Khun claim that a new young, unbiased, enthusiastic scientists needs to enter the field. The reason is that a fresh new innovative ideas could only grow in a clean-of the old paradigm minds…

The similarity lines between the Normal Science era to the Pattern Optimizing stage and of the Scientific Revolution to the Pattern Creating stage are clear. In order for a new idea/invention to come there is sometimes a need for a Paradigm shift something that is based on the accumulated subtle signs of “anomalities”.


Sources
http://www.des.emory.edu/mfp/kuhnsyn.html
http://en.wikipedia.org/wiki/The_Structure_of_Scientific_Revolutions
http://www.mit.edu/~pjl/page2/files/exploration_exploitation.pdf
http://visualsignifier.com/kuhnhome.html

Artificial Creativity

Last IS class we discussed the main charachteristics of the innovative process and tried to identify, define and modelized the main stages of it. Although the innovation process holds a substantial significance to the implemetation phase - the team work, the prototyping and adjusting the idea in regard to feedbacks - , it seems to me that the most important step in the innovation process is actually the idea initiation.

In my opinion, the ability to come up with a new idea or finding a new solution for a problem is tightly linked to the question on the roots of creativity. Most of the people tends to see creativity as a gift or as something that people either have it or not, and even as something that cant really be learned but only to be developed assuming one has the right "genes"for it. This train of thoughts led many to the assumption that creativity couldnt be modelize and therefore that it is impossible to create a creative machine - a computer- that could produce brand new ideas or products.

However, along the years this assumption proved to be more and more questionable and today abundant of examples exist that proves it to be wrong (at least to some extent). The main examples comes from the relatively new field of Artificial Creativity. Artificial Creativity (or computational creativity) is a branch of Artificial Intelligence that deals with the development and exploration of systems that exhibit creative behavior. This includes systems capable of such things as scientific invention, visual artistry, music composition and story generation.

Here are some of the famous examples for Artificial Creativity:

• Computer-robot that paints original paintings by himself: Created by Harold Cohen, "Aaron" is a AI-based program (robot-artist) that actually creates original paintings each one completely different. Aaron paintings are so amazing that if a human created paintings like AARON, we would regard him or her as an acclaimed artist. Indeed hard copies of AARON paintings have hung in museums around the world (London's Tate Modern Galley, Amsterdam's Stedelijk Museum, San Francisco Museum of Modern Art, Brooklyn Museum, and Washington Capital Children's Museum, to name a few).
  here are some examples of his original paintings:
  
  
  
  http://www.scinetphotos.com/auction.html
  
  http://www.stanford.edu/group/SHR/4-2/text/cohen.html
  

• Computer that compose original music - Experiments in Musical Intelligence is an original and provocative study of computational creativity in music. David Cope, meriti Professor at the University of California at Santa Cruz, asks whether computer programs can effectively model creativity—and whether computer programs themselves can create. Defining musical creativity, and distinguishing it from creativity in other arts, Cope presents a series of experimental models that illustrate salient features of musical creativity. In his web site you can actually listen to some pieces of music composed by a computer in here: http://artsites.ucsc.edu/faculty/cope/mp3page.htm

• Chatting? - A.L.I.C.E. (Artificial Linguistic Internet Computer Entity) is a natural language processing chatterbot - a program that engages in a conversation with a human by applying some heuristical pattern matching rules to the human's input. It is one of the strongest programs of its type and has won the Loebner Prize, awarded to accomplished humanoid, talking robots, three times. However, the program is still unable to pass the Turing test, as even a casual user will often expose its mechanistic aspects in short conversations. Why dont you try chat with Alice? :

Many more chatterbots available today, including Fred the psychonalyst... Some of the famous could be find in this link: http://www.simonlaven.com/fred.htm

• Poems:

"More than iron, more than lead, more than gold
I need electricity.
I need it more than I need lamb or pork
or lettuce or cucumber.

I need it for my dreams."

(Racter, from The Policeman's Beard Is Half Constructed)

Well.. not exactly Shakespeare but imperssive enough considering that it was made by a computer... Here are some more examples of poems written by Ray Kurzweil’s Cybernetic Poet:

Page

Sashay down the page

through the lioness

nestled in my soul.

(A Haiku written by Ray Kurzweil’s Cybernetic Poet after reading poems by Wendy Dennis)

Imagine Now And Sing

Imagine now and sing,

creating myths

forming jewels from the falling snow.

Soul

You broke my soul

the juice of eternity,

the spirit of my lips.

I Think I'll Crash

I think I'll crash.

Just for myself with God

peace on a curious sound

for myself in my heart?

And life is weeping

From a bleeding heart

of boughs bending

such paths of them,

of boughs bending

such paths of breeze

knows we've been there

• Telling a joke? At Edinburgh University, AI researcher Kim Binsted has developed Jape-1, a program for telling jokes. The program builds up the jokes according to simple 'templates', such as 'What do you get if you cross an X with a Y ?', and chooses words for X, Y and the pay-off word Z according to properties of the words, such as their sound and associations. Can you spot the Jape-1 jape, and the two from The Crack-a-joke Book by human joke-merchant Kaye Webb?

A - What do you give a hurt lemon? Lemonade.
B - What kind of tree can you wear? A fir coat.
C - What runs around a forest making other animals yawn? A wild boar.

Those examples led me to the question: Is it possible to have an artificial innovators? in other words, would it be possible to have a computerised system that will be creative and innovative in a sense of being able to come up with totaly different ideas and to invent actually new processess or new tools?

*B is by Jape-1

Sources

http://www.thinkartificial.org/artificial-creativity/
http://www.thinkartificial.org/category/artificialcreativity/

http://www.thinkartificial.org/aesthetics/absolut-machines/

http://en.wikipedia.org/wiki/Computational_creativity

http://www.stanford.edu/group/SHR/4-2/text/cohen.html
http://www.mindfully.org/Technology/2004/Creativity-Machine-Thaler24jan04.htm