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Information and useful links to avail various Citizen Services

November 6, 2009

How Do I: National Portal of India
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This section provides you with information and useful links to avail various Citizen Services being provided by the Central & State/UT Governments in India . The list, however, is not exhaustive, as we are committed to adding more and more information about other services for which citizens and other stakeholders need to interact with the Government. Keep visiting this section for new updates !!


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Swine Flu – Authorized Government Centers: A Health Advisory ..

August 10, 2009

Dear All:

Swine Flu – Authorized Government Centers: A Health Advisory ..
The attached presentation would give you additional details about the swine flu, its symptoms and the actions that need to be initiated.

We can stop the spread of this EPIDEMIC and cure the affected ones with timely and correct treatment.  




City Hospital Address Contact
Chennai King Institute of Preventive Medicine (24/7 Service) Guindy, Chennai – 32 (044) 22501520, 22501521 & 22501522
Communicable Diseases Hospital Thondiarpet, Chennai (044) 25912686/87/88, 9444459543
Government General Hospital Opp. Central Railway Station, Chennai – 03 (044) 25305000, 25305723, 25305721, 25330300
Pune Naidu Hospital Nr Le'Meridian, Raja Bahadur Mill, GPO, Pune – 01 (020) 26058243
National Institute of Virology 20A Ambedkar Road, Pune – 11 (020) 26006290
Kolkata ID Hospital 57,Beliaghata, Beliaghata Road, Kolkata – 10 (033) 23701252
Coimbatore Government General Hospital Near Railway Station,
Trichy Road, Coimbatore – 18
(0422) 2301393, 2301394, 2301395, 2301396
Hyderabad Govt. General and Chest Diseases Hospital, Erragadda, Hyderabad (040) 23814939
Mumbai Kasturba Gandhi Hospital Arthur Road, N M Joshi Marg, Jacob Circle, Mumbai – 11 (022) 23083901, 23092458, 23004512
Sir J J Hospital J J Marg, Byculla, Mumbai – 08 (022) 23735555, 23739031, 23760943, 23768400 / 23731144 / 5555 / 23701393 / 1366
Haffkine Institute Acharya Donde Marg, Parel, Mumbai – 12 (022) 24160947, 24160961, 24160962
Kochi Government Medical College Gandhi Nagar P O, Kottayam – 08 (0481) 2597311,2597312
Government Medical College Vandanam P O, Allapuzha – 05 (0477) 2282015
Taluk Hospital Railway Station Road, Alwaye, Ernakulam (0484) 2624040  Sathyajit – 09847840051
Taluk Hospital Perumbavoor PO, Ernakulam 542 (0484) 2523138  Vipin – 09447305200
Gurgaon &
All India Institute of Medical Sciences (AIIMS) Ansari Nagar, Aurobindo Marg Ring Road, New Delhi – 29 (011) 26594404, 26861698  Prof. R C Deka – 9868397464
National Institute for Communicable Diseases 22, Sham Nath Marg,
New Delhi – 54
(011) 23971272/060/344/524/449/326
Dr. Ram Manohar Lohia Hospital Kharak Singh Marg,
New Delhi – 01
(011) 23741640, 23741649, 23741639
Dr. N K Chaturvedi – 9811101704
Vallabhai Patel Chest Institute University Enclave, New Delhi- 07 (011) 27667102, 27667441, 27667667, 27666182
Bangalore Victoria Hospital K R Market, Kalasipalayam, Bangalore – 02 (080) 26703294  Dr. Gangadhar – 94480-49863
SDS Tuberculosis & Rajiv Gandhi Institute of Chest Diseases Hosur Road, Hombegowda Nagar, Bangalore – 29 (080) 26631923  Dr. Shivaraj – 99801-48780






Fail fast to succeed sooner!

March 13, 2009

A few hundred years ago, when a kingdom went to war, they had to use canons which were slow and expensive. It took a group of four soldiers a total of 15 minutes to load the canon ball and set up its aim. Then, after firing if they missed, they just wasted a lot of time and money.

Therefore, the sensible strategy was: ‘Ready, Aim, Fire!’ Today, the story is quite different.

Soldiers use machine guns which have plenty of inexpensive bullets. Nobody wastes time trying to load individual bullets or planning their aim. In fact, they fire first and then adjust their aim. Therefore, the strategy today is ‘Ready, Fire, Aim!’

This analogy is true today for all of you who have just graduated and are starting your career from a clean slate. This is a great time in life for you to start a new business, especially when you do not have the responsibilities of a family or the pressures of a house mortgage payment.

Starting a new business has become much cheaper today because office rent, cost of advertising and cost of employees has gone down. You probably also have a group of friends who would like to work with you and all of you can pool your startup money together. Some of you have ideas, but are hesitant to act due to the fear of making mistakes.

Let me assure you that everyone makes mistakes when starting a new business. What is needed to succeed is the will to recognise your mistakes and to fix them quickly. As I learned from my mentors during my internship, ‘Fail fast to succeed sooner!’

Some of you may not yet have thought about any ideas for a business you can start. My company, BrainReactions, is in the business of identifying new opportunities for entrepreneurs and companies by generating creative new ideas. We not only generate ideas professionally for clients, but we also teach people methods of being more innovative systematically so they can create useful new ideas for their unique situation.

Perhaps we can share some business ideas with you here. Although the general sentiment today is quite negative, this is in fact, a great time to use the recession to your advantage.

Not all businesses are suffering in the recession. According to Barry Moltz’s recent survey, about a fifth of all businesses are such that they actually do better in a recession. Such businesses, called ‘countercyclical businesses’, present great startup opportunities right now. Businesses that help people save money generally tend to be in this category.

For example, in a recession, people prefer to buy more groceries or eat cheaper junk food than eat at a fine dining restaurant. Insurance agents that can save people money on their car insurance premiums also do well in a recession.

Funnily enough, in India, astrologers tend to make good money during a recession by capitalising on the general distress among people. Could you, perhaps, create a new product or service that helps people save money or reduce wastage in their homes and offices?

For new entrepreneurs, it is easier to set up service-based businesses that have a low startup cost. Businesses like tutoring, washing/ironing of clothes, dog food delivery, car wash service, event planning service, and a travel booking service are some examples.

Since you are reading this article on a computer, I would assume that you know how to use the internet and are open to ideas for making money online.

Sites like and provide opportunities for freelance writing, graphic or web design, programming, and even simple tasks like data entry and virtual assistance.

Similarly, Amazon’s Mechanical Turk at pays people for completing simple tasks online as well.

If you are good at photography, you can upload good quality photos to and get paid royalties. Metacafe pays users to upload videos that are popular.

Sites like and pay you to write reviews of Web sites on your free blog. Speaking of blogs, pays a revenue share to people who contribute articles to their site. has a database of unique business ideas from around the world that you could spend hours reviewing. The web is a huge resource of business ideas and for reaching out to other entrepreneurs who are available for providing guidance and help for your new business.

To get more new business ideas, I would recommend travelling to a new place that you have not been before, perhaps to a different country if you can. Experiencing a new place and culture can give you tremendous amount of fresh inspiration for new ideas. Also, check out the book called Successfully Launching New Ventures by Dr Bruce Barringer which features BrainReactions as a success case study in its second chapter.

Furthermore, you can double your chances of success by learning the fundamentals of systematic innovation through a six-week online course we deliver via webinars at where I will be happy to offer a discount for all Indians if you email and mention this article.

I hope that after reading this article you will not go back to your normal daily job-hunting and will actually use some of the ideas and resources that I have shared in order to create your own successful business and create new jobs for our country and our world.


Wolfram Alpha is Coming — and It Could be as Important as Google

March 10, 2009


A Computational Knowledge Engine for the Web

In a nutshell, Wolfram and his team have built what he calls a “computational knowledge engine” for the Web. OK, so what does that really mean? Basically it means that you can ask it factual questions and it computes answers for you.

It doesn’t simply return documents that (might) contain the answers, like Google does, and it isn’t just a giant database of knowledge, like the Wikipedia. It doesn’t simply parse natural language and then use that to retrieve documents, like Powerset, for example.

Instead, Wolfram Alpha actually computes the answers to a wide range of questions — like questions that have factual answers such as “What is the location of Timbuktu?” or “How many protons are in a hydrogen atom?,” “What was the average rainfall in Boston last year?,” “What is the 307th digit of Pi?,” “where is the ISS?” or “When was GOOG worth more than $300?”

Think about that for a minute. It computes the answers. Wolfram Alpha doesn’t simply contain huge amounts of manually entered pairs of questions and answers, nor does it search for answers in a database of facts. Instead, it understands and then computes answers to certain kinds of questions.

How Does it Work?

Wolfram Alpha is a system for computing the answers to questions. To accomplish this it uses built-in models of fields of knowledge, complete with data and algorithms, that represent real-world knowledge.

For example, it contains formal models of much of what we know about science — massive amounts of data about various physical laws and properties, as well as data about the physical world.

Based on this you can ask it scientific questions and it can compute the answers for you. Even if it has not been programmed explicity to answer each question you might ask it.

But science is just one of the domains it knows about — it also knows about technology, geography, weather, cooking, business, travel, people, music, and more.

It also has a natural language interface for asking it questions. This interface allows you to ask questions in plain language, or even in various forms of abbreviated notation, and then provides detailed answers.

The vision seems to be to create a system wich can do for formal knowledge (all the formally definable systems, heuristics, algorithms, rules, methods, theorems, and facts in the world) what search engines have done for informal knowledge (all the text and documents in various forms of media).

How Smart is it and Will it Take Over the World?

Wolfram Alpha is like plugging into a vast electronic brain. It provides extremely impressive and thorough answers to a wide range of questions asked in many different ways, and it computes answers, it doesn’t merely look them up in a big database.

In this respect it is vastly smarter than (and different from) Google. Google simply retrieves documents based on keyword searches. Google doesn’t understand the question or the answer, and doesn’t compute answers based on models of various fields of human knowledge.

But as intelligent as it seems, Wolfram Alpha is not HAL 9000, and it wasn’t intended to be. It doesn’t have a sense of self or opinions or feelings. It’s not artificial intelligence in the sense of being a simulation of a human mind. Instead, it is a system that has been engineered to provide really rich knowledge about human knowledge — it’s a very powerful calculator that doesn’t just work for math problems — it works for many other kinds of questions that have unambiguous (computable) answers.

There is no risk of Wolfram Alpha becoming too smart, or taking over the world. It’s good at answering factual questions; it’s a computing machine, a tool — not a mind.

One of the most surprising aspects of this project is that Wolfram has been able to keep it secret for so long. I say this because it is a monumental effort (and achievement) and almost absurdly ambitious. The project involves more than a hundred people working in stealth to create a vast system of reusable, computable knowledge, from terabytes of raw data, statistics, algorithms, data feeds, and expertise. But he appears to have done it, and kept it quiet for a long time while it was being developed.

Computation Versus Lookup

For those who are more scientifically inclined, Stephen showed me many interesting examples — for example, Wolfram Alpha was able to solve novel numeric sequencing problems, calculus problems, and could answer questions about the human genome too. It was also able to compute answers to questions about many other kinds of topics (cooking, people, economics, etc.). Some commenters on this article have mentioned that in some cases Google appears to be able to answer questions, or at least the answers appear at the top of Google’s results. So what is the Big Deal? The Big Deal is that Wolfram Alpha doesn’t merely look up the answers like Google does, it computes them using at least some level of domain understanding and reasoning, plus vast amounts of data about the topic being asked about.

Computation is in many cases a better alternative to lookup. For example, you could solve math problems using lookup — that is what a multiplication table is after all. For a small multiplication table, lookup might even be almost as computationally inexpensive as computing the answers. But imagine trying to create a lookup table of all answers to all possible multiplication problems — an infinite multiplication table. That is a clear case where lookup is no longer a better option compared to computation.

The ability to compute the answer on a case by case basis, only when asked, is clearly more efficient than trying to enumerate and store an infinitely large multiplication table. The computation approach only requires a finite amount of data storage — just enough to store the algorithms for solving general multiplication problems — whereas the lookup table approach requires an infinite amount of storage — it requires actually storing, in advance, the products of all pairs of numbers.

(Note: If we really want to store the products of ALL pairs of numbers, it turns out this is impossible to accomplish, because there are an infinite number of numbers. It would require an infinite amount of time to simply generate the data, and an infinite amount of storage to store it. In fact, just to enumerate and store all the multiplication products of the numbers between 0 and 1 would require an infinite amount of time and storage. This is because the real-numbers are uncountable. There are in fact more real-numbers than integers (see the work of Georg Cantor on this). However, the same problem holds even if we are speaking of integers — it would require an infinite amount of storage to store all their multiplication products, although they at least could be enumerated, given infinite time.)

Using the above analogy, we can see why a computational system like Wolfram Alpha is ultimately a more efficient way to compute the answers to many kinds of factual questions than a lookup system like Google. Even though Google is becoming increasingly comprehensive as more information comes on-line and gets indexed, it will never know EVERYTHING. Google is effectively just a lookup table of everything that has been written and published on the Web, that Google has found. But not everything has been published yet, and furthermore Google’s index is also incomplete, and always will be.

Therefore Google does and always will contain gaps. It cannot possibly index the answer to every question that matters or will matter in the future — it doesn’t contain all the questions or all the answers. If nobody has ever published a particular question-answer pair onto some Web page, then Google will not be able to index it, and won’t be able to help you find the answer to that question — UNLESS Google also is able to compute the answer like Wolfram Alpha does (an area that Google is probably working on, but most likely not to as sophisticated a level as Wolfram’s Mathematica engine enables).

While Google only provide answers that are found on some Web page (or at least in some data set they index), a computational knowledge engine like Wolfram Alpha can provide answers to questions it has never seen before — provided however that it at least knows the necessary algorithms for answering such questions, and it at least has sufficient data to compute the answers using these algorithms. This is a “big if” of course.

Wolfram Alpha substitutes computation for storage. It is simply more compact to store general algorithms for computing the answers to various types of potential factual questions, than to store all possible answers to all possible factual questions. In then end making this tradeoff in favor of computation wins, at least for subject domains where the space of possible factual questions and answers is large. A computational engine is simply more compact and extensible than a database of all questions and answers.

This tradeoff, as Mills Davis points out in the comments to this article is also referred to as the tradeoff between time and space in computation. For very difficult computations, it may take a long time to compute the answer. If the answer was simply stored in a database already of course that would be faster and more efficient. Therefore, a hybrid approach would be for a system like Wolfram Alpha to store all the answers to any questions that have already been asked of it, so that they can be provided by simple lookup in the future, rather than recalculated each time. There may also already be databases of precomputed answers to very hard problems, such as finding very large prime numbers for example. These should also be stored in the system for simple lookup, rather than having to be recomputed. I think that Wolfram Alpha is probably taking this approach. For many questions it doesn’t make sense to store all the answers in advance, but certainly for some questions it is more efficient to store the answers, when you already know them, and just look them up.


Where Google is a system for FINDING things that we as a civilization collectively publish, Wolfram Alpha is for COMPUTING answers to questions about what we as a civilization collectively know. It’s the next step in the distribution of knowledge and intelligence around the world — a new leap in the intelligence of our collective “Global Brain.” And like any big next-step, Wolfram Alpha works in a new way — it computes answers instead of just looking them up.

Wolfram Alpha, at its heart is quite different from a brute force statistical search engine like Google. And it is not going to replace Google — it is not a general search engine: You would probably not use Wolfram Alpha to shop for a new car, find blog posts about a topic, or to choose a resort for your honeymoon. It is not a system that will understand the nuances of what you consider to be the perfect romantic getaway, for example — there is still no substitute for manual human-guided search for that. Where it appears to excel is when you want facts about something, or when you need to compute a factual answer to some set of questions about factual data.

I think the folks at Google will be surprised by Wolfram Alpha, and they will probably want to own it, but not because it risks cutting into their core search engine traffic. Instead, it will be because it opens up an entirely new field of potential traffic around questions, answers and computations that you can’t do on Google today.

The services that are probably going to be most threatened by a service like Wolfram Alpha are the Wikipedia, Metaweb’s Freebase, True Knowledge, and any natural language search engines (such as Microsoft’s upcoming search engine, based perhaps in part on Powerset’s technology among others), and other services that are trying to build comprehensive factual knowledge bases.

As a side-note, my own service,, is NOT trying to do what Wolfram Alpha is trying to do, fortunately. Instead, Twine uses the Semantic Web to help people filter the Web, organize knowledge, and track their interests. It’s a very different goal. And I’m glad, because I would not want to be competing with Wolfram Alpha. It’s a force to be reckoned with.

Relationship to the Semantic Web

During our discussion, after I tried and failed to poke holes in his natural language parser for a while, we turned to the question of just what this thing is, and how it relates to other approaches like the Semantic Web.

The first question was could (or even should) Wolfram Alpha be built using the Semantic Web in some manner, rather than (or as well as) the Mathematica engine it is currently built on. Is anything missed by not building it with Semantic Web’s languages (RDF, OWL, Sparql, etc.)?

The answer is that there is no reason that one MUST use the Semantic Web stack to build something like Wolfram Alpha. In fact, in my opinion it would be far too difficult to try to explicitly represent everything Wolfram Alpha knows and can compute using OWL ontologies and the reasoning that they enable. It is just too wide a range of human knowledge and giant OWL ontologies are too difficult to build and curate.

It would of course at some point be beneficial to integrate with the Semantic Web so that the knowledge in Wolfram Alpha could be accessed, linked with, and reasoned with, by other semantic applications on the Web, and perhaps to make it easier to pull knowledge in from outside as well. Wolfram Alpha could probably play better with other Web services in the future by providing RDF and OWL representations of it’s knowledge, via a SPARQL query interface — the basic open standards of the Semantic Web. However for the internal knowledge representation and reasoning that takes places in Wolfram Alpah, OWL and RDF are not required and it appears Wolfram has found a more pragmatic and efficient representation of his own.

I don’t think he needs the Semantic Web INSIDE his engine, at least; it seems to be doing just fine without it. This view is in fact not different from the current mainstream approach to the Semantic Web — as one commenter on this article pointed out, “what you do in your database is your business” — the power of the Semantic Web is really for knowledge linking and exchange — for linking data and reasoning across different databases. As Wolfram Alpha connects with the rest of the “linked data Web,” Wolfram Alpha could benefit from providing access to its knowledge via OWL, RDF and Sparql. But that’s off in the future.

It is important to note that just like OpenCyc (which has taken decades to build up a very broad knowledge base of common sense knowledge and reasoning heuristics), Wolfram Alpha is also a centrally hand-curated system. Somehow, perhaps just secretly but over a long period of time, or perhaps due to some new formulation or methodology for rapid knowledge-entry, Wolfram and his team have figured out a way to make the process of building up a broad knowledge base about the world practical where all others who have tried this have found it takes far longer than expected. The task is gargantuan — there is just so much diverse knowledge in the world. Representing even a small area of it formally turns out to be extremely difficult and time-consuming.

It has generally not been considered feasible for any one group to hand-curate all knowledge about every subject. The centralized hand-curation of Wolfram Alpha is certainly more controllable, manageable and efficient for a project of this scale and complexity. It avoids problems of data quality and data-consistency. But it’s also a potential bottleneck and most certainly a cost-center. Yet it appears to be a tradeoff that Wolfram can afford to make, and one worth making as well, from what I could see. I don’t yet know how Wolfram has managed to assemble his knowledge base in less than a very long time, or even how much knowledge he and his team have really added, but at first glance it seems to be a large amount. I look forward to learning more about this aspect of the project.

Building Blocks for Knowledge Computing

Wolfram Alpha is almost more of an engineering accomplishment than a scientific one — Wolfram has broken down the set of factual questions we might ask, and the computational models and data necessary for answering them, into basic building blocks — a kind of basic language for knowledge computing if you will. Then, with these building blocks in hand his system is able to compute with them — to break down questions into the basic building blocks and computations necessary to answer them, and then to actually build up computations and compute the answers on the fly.

Wolfram’s team manually entered, and in some cases automatically pulled in, masses of raw factual data about various fields of knowledge, plus models and algorithms for doing computations with the data. By building all of this in a modular fashion on top of the Mathematica engine, they have built a system that is able to actually do computations over vast data sets representing real-world knowledge. More importantly, it enables anyone to easily construct their own computations — simply by asking questions.

The scientific and philosophical underpinnings of Wolfram Alpha are similar to those of the cellular automata systems he describes in his book, “A New Kind of Science” (NKS). Just as with cellular automata (such as the famous “Game of Life” algorithm that many have seen on screensavers), a set of simple rules and data can be used to generate surprisingly diverse, even lifelike patterns. One of the observations of NKS is that incredibly rich, even unpredictable patterns, can be generated from tiny sets of simple rules and data, when they are applied to their own output over and over again.

In fact, cellular automata, by using just a few simple repetitive rules, can compute anything any computer or computer program can compute, in theory at least. But actually using such systems to build real computers or useful programs (such as Web browsers) has never been practical because they are so low-level it would not be efficient (it would be like trying to build a giant computer, starting from the atomic level).

The simplicity and elegance of cellular automata proves that anything that may be computed — and potentially anything that may exist in nature — can be generated from very simple building blocks and rules that interact locally with one another. There is no top-down control, there is no overarching model. Instead, from a bunch of low-level parts that interact only with other nearby parts, complex global behaviors emerge that, for example, can simulate physical systems such as fluid flow, optics, population dynamics in nature, voting behaviors, and perhaps even the very nature of space-time. This is the main point of the NKS book in fact, and Wolfram draws numerous examples from nature and cellular automata to make his case.

But with all its focus on recombining simple bits of information according to simple rules, cellular automata is not a reductionist approach to science — in fact, it is much more focused on synthesizing complex emergent behaviors from simple elements than in reducing complexity back to simple units. The highly synthetic philosophy behind NKS is the paradigm shift at the basis of Wolfram Alpha’s approach too. It is a system that is very much “bottom-up” in orientation. This is not to say that Wolfram Alpha IS a cellular automaton itself — but rather that it is similarly based on fundamental rules and data that are recombined to form highly sophisticated structures.

Wolfram has created a set of building blocks for working with formal knowledge to generate useful computations, and in turn, by putting these computations together you can answer even more sophisticated questions and so on. It’s a system for synthesizing sophisticated computations from simple computations. Of course anyone who understands computer programming will recognize this as the very essence of good software design. But the key is that instead of forcing users to write programs to do this in Mathematica, Wolfram Alpha enables them to simply ask questions in natural language and then automatically assembles the programs to compute the answers they need.

Wolfram Alpha perhaps represents what may be a new approach to creating an “intelligent machine” that does away with much of the manual labor of explicitly building top-down expert systems about fields of knowledge (the traditional AI approach, such as that taken by the Cyc project), while simultaneously avoiding the complexities of trying to do anything reasonable with the messy distributed knowledge on the Web (the open-standards Semantic Web approach). It’s simpler than top down AI and easier than the original vision of Semantic Web.

Generally if someone had proposed doing this to me, I would have said it was not practical. But Wolfram seems to have figured out a way to do it. The proof is that he’s done it. It works. I’ve seen it myself.

Questions Abound

Of course, questions abound. It remains to be seen just how smart Wolfram Alpha really is, or can be. How easily extensible is it? Will it get increasingly hard to add and maintain knowledge as more is added to it? Will it ever make mistakes? What forms of knowledge will it be able to handle in the future?

I think Wolfram would agree that it is probably never going to be able to give relationship or career advice, for example, because that is “fuzzy” — there is often no single right answer to such questions. And I don’t know how comprehensive it is, or how it will be able to keep up with all the new knowledge in the world (the knowledge in the system is exclusively added by Wolfram’s team right now, which is a labor intensive process). But Wolfram is an ambitious guy. He seems confident that he has figured out how to add new knowledge to the system at a fairly rapid pace, and he seems to be planning to make the system extremely broad.

And there is the question of bias, which we addressed as well. Is there any risk of bias in the answers the system gives because all the knowledge is entered by Wolfram’s team? Those who enter the knowledge and design the formal models in the system are in a position to both define the way the system thinks — both the questions and the answers it can handle. Wolfram believes that by focusing on factual knowledge — things like you might find in the Wikipedia or textbooks or reports — the bias problem can be avoided. At least he is focusing the system on questions that do have only one answer — not questions for which there might be many different opinions. Everyone generally agrees for example that the closing price of GOOG on a certain data is a particular dollar amount. It is not debatable. These are the kinds of questions the system addresses.

But even for some supposedly factual questions, there are potential biases in the answers one might come up with, depending on the data sources and paradigms used to compute them. Thus the choice of data sources has to be made carefully to try to reflect as non-biased a view as possible. Wolfram’s strategy is to rely on widely accepted data sources like well-known scientific models, public data about factual things like the weather, geography and the stock market published by reputable organizatoins and government agencies, etc. But of course even this is a particular worldview and reflects certain implicit or explicit assumptions about what data sources are authoritative.

This is a system that reflects one perspective — that of Wolfram and his team — which probably is a close approximation of the mainstream consensus scientific worldview of our modern civilization. It is a tool — a tool for answering questions about the world today, based on what we generally agree that we know about it. Still, this is potentially murky philosophical territory, at least for some kinds of questions. Consider global warming — not all scientists even agree it is taking place, let alone what it signifies or where the trends are headed. Similarly in economics, based on certain assumptions and measurements we are either experiencing only mild inflation right now, or significant inflation. There is not necessarily one right answer — there are valid alternative perspectives.

I agree with Wolfram, that bias in the data choices will not be a problem, at least for a while. But even scientists don’t always agree on the answers to factual questions, or what models to use to describe the world — and this disagreement is essential to progress in science in fact. If there is only one “right” answer to any question there could never be progress, or even different points of view. Fortunately, Wolfram is desigining his system to link to alternative questions and answers at least, and even to sources for more information about the answers (such as the Wikipeda for example). In this way he can provide unambiguous factual answers, yet also connect to more information and points of view about them at the same time. This is important.

It is ironic that a system like Wolfram Alpha, which is designed to answer questions factually, will probably bring up a broad range of questions that don’t themselves have unambiguous factual answers — questions about philosophy, perspective, and even public policy in the future (if it becomes very widely used). It is a system that has the potential to touch our lives as deeply as Google. Yet how widely it will be used is an open question too.

The system is beautiful, and the user interface is already quite simple and clean. In addition, answers include computationally generated diagrams and graphs — not just text. It looks really cool. But it is also designed by and for people with IQ’s somewhere in the altitude of Wolfram’s — some work will need to be done dumbing it down a few hundred IQ points so as to not overwhelm the average consumer with answers that are so comprehensive that they require a graduate degree to fully understand.

It also remains to be seen how much the average consumer thirsts for answers to factual questions. I do think all consumers at times have a need for this kind of intelligence once in a while, but perhaps not as often as they need something like Google. But I am sure that academics, researchers, students, government employees, journalists and a broad range of professionals in all fields definitely need a tool like this and will use it every day.

Future Potential

I think there is more potential to this system than Stephen has revealed so far. I think he has bigger ambitions for it in the long-term future. I believe it has the potential to be THE online service for computing factual answers. THE system for factual knowlege on the Web. More than that, it may eventually have the potential to learn and even to make new discoveries. We’ll have to wait and see where Wolfram takes it.

Maybe Wolfram Alpha could even do a better job of retrieving documents than Google, for certain kinds of questions — by first understanding what you really want, then computing the answer, and then giving you links to documents that related to the answer. But even if it is never applied to document retrieval, I think it has the potential to play a leading role in all our daily lives — it could function like a kind of expert assistant, with all the facts and computational power in the world at our fingertips.

I would expect that Wolfram Alpha will open up various API’s in the future and then we’ll begin to see some interesting new, intelligent, applications begin to emerge based on its underlying capabilities and what it knows already.

In May, Wolfram plans to open up what I believe will be a first version of Wolfram Alpha. Anyone interested in a smarter Web will find it quite interesting, I think. Meanwhile, I look forward to learning more about this project as Stephen reveals more in months to come.

One thing is certain, Wolfram Alpha is quite impressive and Stephen Wolfram deserves all the congratulations he is soon going to get.

World Famous Men in One Single Artwork

October 23, 2008

I am in love again……..

September 22, 2008

20 Parts of Your Body You Don’t Need

August 13, 2008

You might hear the NERD bells go off in your head when you saw this article, but I still thought it was extremely interesting. Here are parts of your body that you actually don’t need. Check out point 13. LOL.

1. VOMERONASAL ORGAN (VNO), or Jacobson’s organ: a tiny hole on each side of the nasal bridge that is considered to be connected to nonfunctional chemical receptors. Could be all that is left from our once great ability to detect pheromones.

2. EXTRINSIC EAR MUSCLES: These three muscles most likely made it possible for our ancestors to move their ears independently of their heads, as rabbits and dogs do. We still have them, which is why most people can learn to wiggle their ears.

3. WISDOM TEETH: Early humans had to chew a lot of plants to get enough calories to survive, making another row of molars helpful, but unless you chew a lot of branches, these will eventually come out in a painful procedure. Only about 5 percent of the population has a healthy set of these third molars.

4. NECK RIB: A set of cervical ribs—possibly leftovers from the age of reptiles, still appear in less than 1 percent of the population. They often cause nerve and artery problems.

5. THIRD EYELID: A common ancestor of birds and mammals may have had a membrane for protecting the eye and sweeping out debris. Humans retain only a tiny fold in the inner corner of the eye, exactly there where you always catch a spec of dust or debris.

6. DARWIN’S POINT: A small folded point of skin toward the top of each ear is occasionally found in modern humans. It may be a remnant of a larger shape that helped focus distant sounds.

7. SUBCLAVIUS MUSCLE: This small muscle stretching under the shoulder from the first rib to the collarbone would be useful if humans still walked on all fours. Some people have one, some have none, and a few have two.

8. PALMARIS MUSCLE: This long, narrow muscle runs from the elbow to the wrist and is missing in 11 percent of modern humans. It may once have been important for hanging and climbing. Surgeons harvest it for reconstructive surgery.

9. MALE NIPPLES: Lactiferous ducts form well before testosterone causes sex differentiation in a fetus. Men have mammary tissue that can be stimulated to produce milk. This just makes me angry; I’ve been spending a fortune on milk all these years! I’ll have to test this tomorrow with my Special K.

10. ERECTOR PILI: Bundles of smooth muscle fibers allow animals to puff up their fur for insulation or to intimidate others. Humans retain this ability (goose bumps are the indicator) but have obviously lost most of the fur.

11. APPENDIX: This narrow, muscular tube attached to the large intestine served as a special area to digest cellulose when the human diet consisted more of plant matter than animal protein. It also produces some white blood cells. Annually, more than 300,000 Americans have an appendectomy.

12. BODY HAIR: Brows help keep sweat from the eyes, and male facial hair may play a role in sexual selection, but apparently most of the hair left on the human body serves no function.

13. THIRTEENTH RIB: Our closest cousins, chimpanzees and gorillas, have an extra set of ribs. Most of us have 12, but 8 percent of adults have the extras.

14. PLANTARIS MUSCLE: Often mistaken for a nerve by freshman medical students, the muscle was useful to other primates for grasping with their feet. It has disappeared altogether in 9 percent of the population.

15. MALE UTERUS: A remnant of an undeveloped female reproductive organ hangs off the male prostate gland.

16. FIFTH TOE: Lesser apes use all their toes for grasping or clinging to branches. Humans need mainly the big toe for balance while walking upright, the other four are for holding when you slam them on a coffee table at night!

17. FEMALE VAS DEFERENS: What might become sperm ducts in males become the epoophoron in females, a cluster of useless dead-end tubules near the ovaries.

18. PYRAMIDALIS MUSCLE: More than 20 percent of us lack this tiny, triangular pouch-like muscle that attaches to the pubic bone. It may be a relic from pouched marsupials.

19. COCCYX: These fused vertebrae are all that’s left of the tail that most mammals still use for balance and communication. Our hominid ancestors lost the need for a tail before they began walking upright. All they’re good for now is give us painful falls on the butt.

20. PARANASAL SINUSES: The nasal sinuses of our early ancestors may have been lined with odor receptors that gave a heightened sense of smell, which aided survival. No one knows why we retain these perhaps troublesome mucus-lined cavities, except to make the head lighter and to warm and moisten the air we breathe.

Make your own calendar

April 25, 2008

Create a printable monthly calendar using your photographs and make your own recycled display case. Plan ahead! Impress your friends with your pre-cognitive powers! All you need is a photo from that fancy digital camera of yours. Heck, go nuts and make a whole year.