Relentless Predator Upon the Obsolete

…a combination of relentless predator upon the obsolete and benevolent solver of the world’s problems. As ways of making money go, that’s pretty good. Startups are often ruthless competitors, but they’re competing in a game won by making what people want.

This is such a cool way to think about startups. I like the image of the relentless predator - some one on the hunt, looking to obsolete wasteful ways of doing things, saving people tons of money and making a few bucks in the process.

So how are startup ideas born?

1. If you are lucky, you will find a list like this to start with. It can fire your imagination and set you thinking to make your own list or flesh out the ideas a bit more.

2. You can watch out for problems and suddenly a better way solve some of them may pop-up in your head.

3. You can watch trends, think a bit ahead and build a few experimental proto-types and see what happens (You may be taking a bit of a risk with this approach and may end up building a solution looking for a problem).

4. Find the gap in an emerging technology space and fill a tiny bit of it with your solution.

5. Leverage a new technology to do something that has not been done before.

6. Pick some great idea that is successful and radically improve the implementation (make it simpler, easier, faster, more scalable).

7. The best, in my opinion, is to scratch your own itch and find something, for which you are the first user and see whether it has one of the above characteristics (an added bonus).

In our own startups , we have tried a few of these approaches. There may be many more. As Paul Graham says:

Consider this list to end with a giant ellipsis.

Dreaming up ideas can become a (nice) habit, so I keep an idealog. Not every idea is a good one or fit for a startup. But ideas trigger ideas and you never know where they may lead.

Wikis and Knowlets - A Concept Web for Knowledge Representation

Stumbled upon this site today. Seems to be taking the wiki based collaboration one level higher.

Wikiprofessional’s Concept Web Initiative is a global collaboration to innovate how knowledge is represented and expanded on the Internet.

Knowlets

The Knowlet summarizes the relations between the concepts and presents the strength of the relationship based on a value derived from three main factors: factual (F) statements found in scientific databases, the co-occurrence (C) of two concepts in a text, and a predictive (P) parameter based on the conceptual overlap of the two concepts.

An extension to MediaWiki

The Wiki is an extension of the MediaWiki software that enables Wiki editing capability in a relational database structure.

Just the beginning

The first incarnation of the wiki is for Life Sciences called WikiProteins.

While the Life Sciences are the starting point for the Concept Web, the Wikiprofessional collaboration intends to expand the knowledge representation and expansion dynamics of the Concept Web systematically in disciplines and languages throughout the world.

This will be an interesting development to watch. With an impressive list of Collaboration and Research Partners, WikiProfessional seems to be off to a good start.

Many questions remain.

• How will this be different from DbPedia and other similar efforts?
• What is the connection to the Semantic Web?
• Is the code an open source like MediaWiki (WikiProfessional is based on MediaWiki)
• Is Knowlets the right meme for knowledge representation? In all fields of interest?

Children of the Petabyte Age

Is it really the End of Theory, as Chris Anderson predicts?

Sixty years ago, digital computers made information readable. Twenty years ago, the Internet made it reachable. Ten years ago, the first search engine crawlers made it a single database. Now Google and like-minded companies are sifting through the most measured age in history, treating this massive corpus as a laboratory of the human condition. They are the children of the Petabyte Age.

At the petabyte scale, information is not a matter of simple three- and four-dimensional taxonomy and order but of dimensionally agnostic statistics.

I do believe, as they say in  AstronomyCast that knowing  “not just what we know but how we know what we know” gives you the ability to reason about hypotheses, models and theories. A new kind of analysis with petabytes of data may lead us down the path. But the examples are far fewer and not very convincing - yet.

I would love to watch this conversation and see where it takes us.

Updated: 1st Jul 08

I did not have to wait long. Here is a great article on Why the cloud cannot obscure the scientific method.

Technology Trends Talk at TiE Chennai

I gave a talk on Technology Trends and Gleaning Opportunities at TiE Chennai today. It was gratifying to hang out with the participants and swap stories. I just uploaded a copy of the presentation (in PDF format). Here is the link -  technology-trends-jun2008

I also uploaded a copy on slide-share. Here is the link to the presentation.

I would love to hear from you. I am going to keep updating this presentation and incorporate suggestions. I am also planning to spend some time expand my list as well as tools for tracking trends.

Wikis and Information Intelligence

Information Intelligence is the practice of gathering intelligence useful to an organization. It uses Open Source Intelligence to enrich an organization’s ability to gather intelligence for internal use.

Open Source Intelligence (OSINT) is an information processing discipline that involves finding, selecting, and acquiring information from publicly available sources and analyzing it to produce actionable intelligence. In the Intelligence Community (IC), the term “open” refers to overt, publicly available sources (as opposed to covert or classified sources); it is not related to open-source software. OSINT includes a wide variety of information and sources:

Media - newspapers, magazines, radio, television, and computer-based information.

Public data - government reports, official data such as budgets and demographics, hearings, legislative debates, press conferences, speeches, marine and aeronautical safety warnings, environmental impact statements, contract awards.

Observation and reporting - Amateur airplane spotters, radio monitors and satellite observers among many others have provided significant information not otherwise available. The availability of worldwide satellite photography, often of high resolution, on the Web (e.g., Google Earth) has expanded open source capabilities into areas formerly available only to major intelligence services.

Professional and academic - conferences, symposia, professional associations, academic papers, and subject matter experts.^[1]

In addition to these Media mentioned above there are several sources for Web Data Mining. There are several aspects of improving Information Intelligence:

1. Gathering information from a variety of openly available sources
2. Supplementing the open source intelligence with internal information
3. Providing a collaborative platform to share information
4. Enriching information - tagging, interlinking, annotating
5. Versioning information to keep it current
6. Providing a semantic layer for easy retrieval and integration with other tools
7. Providing both a horizontal view and specific vertical views of the information

Wiki is an ideal tool for managing Information Intelligence inside an organization. You can start with a base wiki technology like MediaWiki (used by Wikipedia) and build additional layers like Semantic Media Wiki or provide structured data access like DbPedia . You can get information on several vertical sharing information sites using MediaWiki here.

A good example of both horizontal and vertical views is demonstrated by the US Government initiatives Diplopedia and Intellipedia.

Recent congressional testimony from Jimmy Wales, the founder of Wikipedia,^[5] notes the difference between vertical and horizontal information sharing and suggests that both could be successful e-government endeavors. Intellipedia is an excellent example of sharing information horizontally across agencies, and Diplopedia has found similar success in sharing information within the Department of State bureaucracy. Statements on both wikis encourage cross posting of relevant information as appropriate.

Wikis provide a great foundation for Information Intelligence. Enriching Wikis with semantic annotations, providing more powerful viewing options, granular addressing and increasing the quality of links may go a long way in increasing their effectiveness.

Meta:

This entry was triggered by an email invite to an Intellipedia session at the MIT Center for Collective Intelligence.

LinkLog: Opportunity Computing

We have heard about Utility Computing. The new buzzword in this space seems to be Opportunity Computing. Here is an image of the Opportunity Computing Stack from Etelos. More details on Opportunity Computing and a comparison to Utility Computing can be found here.

I found this link through this ZDNet Blog post.

meta: twitter:zdnet microblog -> article -> etelos page

Innovation Propagation

Democracy was probably one of the greatest innovations in the world. How did it propagate? For a visualization of this story visit March of Democracy. While you are there explore other maps too.

Where has democracy dominated and where has it retreated? This map gives us a visual ballet of democracy’s march across history as the most popular form of government. From the first ancient republics to the rise of self-governing nations, see the history of democracy: 4,000 years in 90 seconds…!

This is a great and a very powerful way to track how a certain event or movement propagates around the globe. This is also a great way to teach history. Moving from the video, to the meta problem it solves, we can think of a tool to track propagation of innovation and other events. Many examples come to mind:

1. Historic events - spread of religions, spreading of culture, propagation of ideas. These and many others originate in one or two places and spread globally over a period of time.
2. This may also be a great tool for teaching economics, history and diffusion of various other types of innovation.
3. I would love to see a map of the way Mathematics or Science spread.

With the advent of internet, ideas spread through packets. Bloggers, definitely are catalysts for propagating information and ideas. Hopefully, we can trace the spread at a more granular level and understand why certain ideas spread and why others dont.

Tech Jobs

More and more articles are appearing on talent crisis. In an earlier blog, I pointed to Deloitte’s prediction of Talent Crisis. Here is another from Kurt Cagle whom I respect (read his XML column regularly) on The Coming Tech Labor Crunch:

… there is a continuing drain upon the IT field due to programmers moving on or moving out, coupled with an impending drop due to raw demographics coupled with an overall negative image being painted of the IT field at the college level that are whittling away all but the most dedicated developers before they even graduate.

… unfortunately. University level curricula take three to eight years to implement, typically, which means that they are usually quite adept at teaching what was contemporary half a decade before. Thus, it’s likely that courses in Ruby or AJAX will likely not appear in the typical university catalog until 2009 at the earliest. The community colleges are generally more nimble, though they too have a lag time that they have to fight.

While this can be seen as a necessary brake on “fad languages”, the reality is that from the time that a person enters college, they will be looking at seven to eight years before they are even at a journeyman programmer level, of which nearly half that ends up being borne as “on-the-job” training to companies.

While the short term looks good for developers as a consequence, the longer term benefits for the industry overall is considerably more unsettled. Tech jobs in general are quite attractive to most politicians - they pay well (and consequently can be taxed well), they tend to attract a core of intelligent people who often participate disproportionately in the cultural life of their community, tech jobs are generally “green” in that they require comparatively little polluting infrastructure to sustain, and they tend to have a comparatively light footprint in terms of crime, drug abuse, and other societal “ills” compared to other groups.

How can the industry solve this problem? The talent crisis is not just for software. It is going to hit almost every high tech field where even a journeyman will require 6-8 years of training.

Internet that is proactive, predictive and context-aware

Not a bad thing to dream about. Looks as if Intel is trying to make that happen with more powerful and less power-hungry processors. Here is Intel CEO at the Consumer Electronic Show (CES):

“It’s an internet that is proactive, predictive and context-aware.”

Explaining that devices would be location-aware, and would access the internet over Wimax wireless connections, he said: “Instead of going to the internet, the internet comes to us.

“We need a ubiquitous, wireless broadband infrastructure. Eventually we will blanket the globe in wireless broadband connectivity.”

Mr Otellini said for this vision of the future to be fulfilled it would “require exponentially more powerful processors, using less and less power”.

This is great. It may happen in a few years. But as software professionals, how can we leverage these new developments?  Here are some things to think about:

• Does size (of applications) matter?
• Will we have hundreds of small, loosely connected, context-aware applications, instead of just a few?
• Do we need new frameworks to build these apps?
• What will be the evolution of mobile operating systems? Which one is ready for the challenge?
• How will the IDEs and development tools evolve?
• What new languages or new language features emerge?
• What are the user interaction design challenges? (the current mobile apps suck big time)

There seems to be many opportunities in this space.

LinkLog: Python as language of 2007

TIOBE declares Python as the programming language of 2007. I track this site using InfoMinder. The yellow highlights are changes detected by Infominder.

Look at both Delphi, Cobol, FoxPro and Lua. Lua is an increasingly popular language for writing games.