The Top 500 Worst Passwords of All Time

From the moment people started using passwords, it didn’t take long to realize how many people picked the very same passwords over and over. Even the way people misspell words is consistent. In fact, people are so predictable that most hackers make use of lists of common passwords just like these. To give you some insight into how predictable humans are, the following is a list of the 500 most common passwords. If you see your password on this list, please change it immediately. Keep in mind that every password listed here has been used by at least hundreds if not thousands of other people.
There are some interesting passwords on this list that show how people try to be clever, but even human cleverness is predictable. For example, look at these passwords which are a little interesting:
  • ncc1701: The ship number for the Starship Enterprise
  • thx1138: The name of George Lucas’s first movie, a 1971 remake of an earlier student project
  • qazwsx: Follows a simple pattern when typed on a typical keyboard
  • 666666: Six sixes
  • 7777777: Seven sevens
  • ou812: The title of a 1988 Van Halen album
  • 8675309: The number mentioned in the 1982 Tommy Tutone song. The song supposedly caused an epidemic of people dialing 867- 5309 and asking for “Jenny”
Approximately one out of every nine people uses at least one password on the list shown in the table below!! And one out of every 50 people uses one of the top 20 worst passwords.
Here’s the list of top 500 worst passwords of all time, not considering character case:
No.
Top 1-100 passwords
Top 101–200 passwords
Top 201–300 passwords
Top 301–400 passwords
Top 401–500 passwords
1
123456
porsche
firebird
prince
rosebud
2
password
guitar
butter
beach
jaguar
3
12345678
chelsea
united
amateur
great
4
1234
black
turtle
7777777
cool
5
pussy
diamond
steelers
muffin
cooper
6
12345
nascar
tiffany
redsox
1313
7
dragon
jackson
zxcvbn
star
scorpio
8
qwerty
cameron
tomcat
testing
mountain
9
696969
654321
golf
shannon
madison
10
mustang
computer
bond007
murphy
987654
11
letmein
amanda
bear
frank
brazil
12
baseball
wizard
tiger
hannah
lauren
13
master
xxxxxxxx
doctor
dave
japan
14
michael
money
gateway
eagle1
naked
15
football
phoenix
gators
11111
squirt
16
shadow
mickey
angel
mother
stars
17
monkey
bailey
junior
nathan
apple
18
abc123
knight
thx1138
raiders
alexis
19
pass
iceman
porno
steve
aaaa
20
fuckme
tigers
badboy
forever
bonnie
21
6969
purple
debbie
angela
peaches
22
jordan
andrea
spider
viper
jasmine
23
harley
horny
melissa
ou812
kevin
24
ranger
dakota
booger
jake
matt
25
iwantu
aaaaaa
1212
lovers
qwertyui
26
jennifer
player
flyers
suckit
danielle
27
hunter
sunshine
fish
gregory
beaver
28
fuck
morgan
porn
buddy
4321
29
2000
starwars
matrix
whatever
4128
30
test
boomer
teens
young
runner
31
batman
cowboys
scooby
nicholas
swimming
32
trustno1
edward
jason
lucky
dolphin
33
thomas
charles
walter
helpme
gordon
34
tigger
girls
cumshot
jackie
casper
35
robert
booboo
boston
monica
stupid
36
access
coffee
braves
midnight
shit
37
love
xxxxxx
yankee
college
saturn
38
buster
bulldog
lover
baby
gemini
39
1234567
ncc1701
barney
cunt
apples
40
soccer
rabbit
victor
brian
august
41
hockey
peanut
tucker
mark
3333
42
killer
john
princess
startrek
canada
43
george
johnny
mercedes
sierra
blazer
44
sexy
gandalf
5150
leather
cumming
45
andrew
spanky
doggie
232323
hunting
46
charlie
winter
zzzzzz
4444
kitty
47
superman
brandy
gunner
beavis
rainbow
48
asshole
compaq
horney
bigcock
112233
49
fuckyou
carlos
bubba
happy
arthur
50
dallas
tennis
2112
sophie
cream
51
jessica
james
fred
ladies
calvin
52
panties
mike
johnson
naughty
shaved
53
pepper
brandon
xxxxx
giants
surfer
54
1111
fender
tits
booty
samson
55
austin
anthony
member
blonde
kelly
56
william
blowme
boobs
fucked
paul
57
daniel
ferrari
donald
golden
mine
58
golfer
cookie
bigdaddy
0
king
59
summer
chicken
bronco
fire
racing
60
heather
maverick
penis
sandra
5555
61
hammer
chicago
voyager
pookie
eagle
62
yankees
joseph
rangers
packers
hentai
63
joshua
diablo
birdie
einstein
newyork
64
maggie
sexsex
trouble
dolphins
little
65
biteme
hardcore
white
0
redwings
66
enter
666666
topgun
chevy
smith
67
ashley
willie
bigtits
winston
sticky
68
thunder
welcome
bitches
warrior
cocacola
69
cowboy
chris
green
sammy
animal
70
silver
panther
super
slut
broncos
71
richard
yamaha
qazwsx
8675309
private
72
fucker
justin
magic
zxcvbnm
skippy
73
orange
banana
lakers
nipples
marvin
74
merlin
driver
rachel
power
blondes
75
michelle
marine
slayer
victoria
enjoy
76
corvette
angels
scott
asdfgh
girl
77
bigdog
fishing
2222
vagina
apollo
78
cheese
david
asdf
toyota
parker
79
matthew
maddog
video
travis
qwert
80
121212
hooters
london
hotdog
time
81
patrick
wilson
7777
paris
sydney
82
martin
butthead
marlboro
rock
women
83
freedom
dennis
srinivas
xxxx
voodoo
84
ginger
fucking
internet
extreme
magnum
85
blowjob
captain
action
redskins
juice
86
nicole
bigdick
carter
erotic
abgrtyu
87
sparky
chester
jasper
dirty
777777
88
yellow
smokey
monster
ford
dreams
89
camaro
xavier
teresa
freddy
maxwell
90
secret
steven
jeremy
arsenal
music
91
dick
viking
11111111
access14
rush2112
92
falcon
snoopy
bill
wolf
russia
93
taylor
blue
crystal
nipple
scorpion
94
111111
eagles
peter
iloveyou
rebecca
95
131313
winner
pussies
alex
tester
96
123123
samantha
cock
florida
mistress
97
bitch
house
beer
eric
phantom
98
hello
miller
rocket
legend
billy
99
scooter
flower
theman
movie
6666
100
please
jack
oliver
success
albert

NRI bank accounts: Some facts

Today, there a number of Indians working abroad. Therefore, the banking services catering to the transfer of funds, savings, earnings, investments, and repatriation of Non-Resident Indians have grown tremendously.

Banking laws for NRIs allow for the following deposit schemes, or simply put, accounts with authorized dealers -- banks and financial institutions authorized by Reserve Bank of India [Get Quote] to deal in foreign exchange -- to be maintained in Indian rupees and in foreign currency:

  • FCNR -- Foreign Currency Non-Resident Account
  • NRE -- Non-Resident External Rupee Account
  • NRO -- Non-Resident Ordinary Rupee Account

The special features for the above mentioned accounts are:

FCNR Accounts

These accounts are only for term (fixed) deposits with the maturity ranging from one year to three years.

NRE Accounts

NRE accounts are opened in Indian rupees and all foreign exchange deposits received for credit of these accounts are first converted to Indian rupees at the buying rates by the banks.

NRO Accounts

A bank account, held by a person designated as NRI, in India is designated as an Ordinary Non-resident Account (NRO Account).

FCNR Accounts

NRE Accounts

NRO Accounts

Currency used:

Pounds Sterling

US Dollars

Japanese Yen

Euro

Features:

  • Accounts only for term deposits ranging from one year to three years.
  • Principal, as well as interest, earned on these accounts is transferable outside India in the same currency or in other convertible currency.
  • The interest, earned on these deposits, is exempt from Indian Income Tax.

Currency used:

Indian rupees (all foreign exchange deposits received need to be first converted to Indian rupees at the buying rates by the banks)

Features:

  • Withdrawals in foreign currency are permitted provided The Indian rupees in the account are converted to foreign currency at the selling rate. This conversion loss is to be borne by the account holder.
  • Deposits for one year and above carry interest at rates higher than those available to residents in India.
  • Deposits and their interests are free of Indian Income-tax.
  • The entire credit balance (inclusive of interest earned accumulated) can be repatriated (send back) outside India at any time without requiring permission from the RBI.
  • Local disbursement from the accounts can be made freely.
  • Account holders can avail of loans/overdrafts from banks against security of fixed deposits in their NRE accounts.
  • The balances in the accounts are free of Wealth-tax and gifts to close relatives in India are free of any Gift-tax.

Currency used:

Indian rupees

Features:

  • Accounts can also be opened with funds deposited from abroad.
  • As funds in this type of account are non-repatriate, they cannot be deposited abroad to the account holders or transferred to their NRE Accounts without the Reserve Bank's prior permission.
  • Interest earned on these deposits is not exempt from Indian Income-tax.

Some important facts that you should know about such accounts:

  • NRI accounts cannot be opened/ operated by a Power-of-Attorney holder in India on behalf of the NRI: However, the latter can operate the accounts for the purpose of local payments to be made on behalf of the NRI account holder. The Power-of-Attorney holder is not permitted to make gifts from these accounts and is not allowed to make remittances outside India.
  • NRIs can invest in shares and securities of Indian companies, government securities, etc. NRIs can invest in units of domestic mutual funds and deposits with Indian companies, immovable properties in India, and proprietorship/ partnership concerns in India.
    These investments can be done using a NRO or NRE account. However, an important point to remember is that if you use an NRO account, the funds sourced from any investments cannot be repatriated. This problem is solved by using a NRE account.
via:http://www.rediff.com/money/2009/mar/27perfin-nri-bank-accounts-some-facts.htm

Just married: Wow or woe?

If you thought marriage was a bed of roses or a walk in the clouds, you have another thing coming.

And maybe the prophets were right. But what they didn't tell us that was that most couples feel the full intensity of stormy winds in the first year of marriage.

Dr Anjali Chhabria, Mumbai-based Consultant Psychiatrist says, "Conflict and misunderstanding happens in any marriage. In the first year many couples tend to compare their relationship with that of others. 'Oh my God they are so in love; they coochi-coo so much; etc' and then start thinking that something is wrong with their relationship."

Although the first year of marriage may be tough, the fact that you've come this far together should make you want to turn your first couple year into a journey of intimacy and discovery. For that, you need to first "Make a commitment to romantic love," says author Willard F. Harley Jr. in his book Five Steps to Romantic Love: A Workbook for Readers of Love Busters and His Needs, Her Needs.

According to this book, love that implies 'care' is a behaviour that actually meets someone's needs. Romantic love, on the other hand, is a feeling we experience when someone meets our most important emotional needs. The two concepts of romantic love and care come together in marriage. You care for your spouse when you meet his or her most important emotional needs. That in turn causes your spouse to feel romantic love for you. When your spouse cares for you and meets your needs, you feel romantic love for your spouse.

When you build a strong foundation of care and emotional love in the first year, you set the stage for a life-long, meaningful marriage.

Dealing with a stranger your spouse

Some couples (mostly women) face mild depression immediately after the wedding. Many are also often shocked when they see the man they knew before marriage radically transformed into this phantom stranger.

Mona, a homemaker married Karan after three years of dating him. Mona assumed that because they had known each other for three years, life together would be a breeze. But the first year shook Mona's world because her romantic expectations came crashing around her shoulders.

She felt that Karan suddenly became complacent, unromantic and took her for granted-something he never used to do before. Karan's view was that as partners for life, they needed to get real.

Passionate declarations of undying love would have to take a back seat-to him life meant paying bills, running a home, planning a future. At first, Mona was surprised, hurt and resentful when she faced the unromantic aspects of marriage like home maintenance, household help, and food (trivial as they may seem).

But after a while she realised that Karan really did love her. He showed her that in other ways-by making sure she was okay and all her needs were met.

Dr Chhabria says: Normally, in love marriages partners tend to presume certain things about each other. And once they get married, they realise that these were presumptions and not reality. When reality strikes, there's disappointment and resentment. Consequently, small non-issues get blown out of proportions.

Not communicating

When Diandra married Sean, all of a sudden she was learning to deal with his piques, preferences and idiosyncrasies. They fought on their first dinner date after the wedding because Sean gave Diandra some spiel about paying more attention to his mother.

Diandra thought that the conversation was totally unnecessary because she seemed to be adjusting quite well with her mother-in-law. She was shocked and angry.

In Diandra's case, the situation may have had to do with the wedding, a rush of post-wedding emptiness, stress about her new life and adjustments, a sense of loss (she moved out of her parents' familiar home and into a new one with her husband (or in-laws).

But whatever the situation, she didn't ignore the negativity she felt-she told Sean exactly how she felt and he saw her point. In a new marriage positive communication is a prerequisite.

Dr Chhabria says: Talk to each other about your relationship-discuss the ground realities of marriage, your expectations and your interpretations. After all, you did take those vows together, so it's only natural that you confide in each other.

Not maintaining your own individuality

Maintain your separate individual identities. Don't impose your needs on him or try to change him.

Leena got used to Gautam taking her out a lot during their courtship. After marriage, he would hastily excuse himself from going out. Initially she felt rejected and moped around. But after a few months of sulking she decided to do her own thing. She would round up her friends and have fun shopping and going out with the girls.

When she took this step two things changed: She got what she wanted and Gautam, feeling guilty, started initiating going out together more often.

Dr Chhabria says: Marriage is only a change of address! -- If you look at it this way, you'll be happy. A happily married couple is one that can go out to dinner or watch television in silence because they are truly and totally comfortable with each other-in each other's silence. If you're bored, just do your own thing. Don't blame your spouse for your boredom.

Before he came along, you were perfectly capable of managing your needs-why should things change now because you have a partner?

Trying to change things about each other

Many couples become embroiled in a pointless project during the first year-"you must start feeling, thinking or doing as I would." Menka faced this situation with her new husband Kabir.

As the months rolled by, she began feeling claustrophobic with Kabir breathing down her neck, trying to control everything in her life and making her do things his way-right from the density of the early morning coffee to how she dressed.

Initially she fought back; met him head on, and both suffered marriage-threatening emotional wounds.

Finally she talked to him, told him how uncomfortable it was to be controlled all the time and asked him if he would be able to live with someone who did it to him? He accepted his fault and agreed to leave her to her individual interests.

Dr Chhabria says: A lot of couples would like to change things about each other and some may even think that marriage makes that happen automatically. Somewhere they enforce their preferences on the other partner and feel that 'if you love me you'd do this for me.'

But the partner who is at the receiving end decodes the message as 'I don't like you the way you are'. This can cause resentment because love is supposed to be about accepting each other the way you are. Once love starts becoming conditional, bitterness builds up.

Keeping score

It's futile to keep a scorecard for your partner's faults if you really want to be happily married because the goal of a marriage is to be "for each other" not "at each other".

Anushka and Samar, a pair of newlyweds fought everyday and with each fight Samar retreated deeper into his shell. He just clammed up and refused to talk.

The reason: Anushka had the habit of dredging up the past during their fights. What would start out as a mild difference of opinion would spiral into a verbal battle when Anushka would start losing sight of the current issue of contention and dwell on what Samar did a few days ago.

Anushka would rage on, blind to the fact that she was isolating Samar completely. The first few months of marriage are when a couple should be ideally cementing their relationship-not alienating each other.

Those who do not forgive minor faux pas will remain mired in a regressive relationship awash with mistrust, resentment and hurt. Forgiving minor contraventions means making allowances for those simple, everyday human errors that we all happen to make every now and then. Keep in mind that his errors may be different from yours-but at the end of the day, they're just that, errors.

Dr Chhabria says: If you constantly bring up the past, it means that the hurt of the fight is still there. Look back and find out what bothered you then and address it. Whenever you're arguing with your spouse, think about what you're trying to achieve.

Agree that even if you've had the worst fight, you'll still hug each other and go to sleep at night so that you can move on peacefully the next day. If you're not able to do that, speak to an unbiased third party-preferably a counsellor or someone who knows you really well and will be frank with you.

Excerpts from: Five Steps to Romantic Love: A Workbook for Readers of Love Busters and His Needs, Her Needs. By Willard J. Harley Jr.

The 5 Toughest Questions a Woman Can Ask a Man

The questions are:

What are you thinking about?
Do you love me?
Do I look fat?
Do you think she is prettier than me?
What would you do if I died?

What makes these questions so difficult is that every one is guaranteed to explode into a major argument if the man answers incorrectly (i.e tells the truth). Therefore, as a public service, each question is analyzed below, along with possible responses.

*********

Question # 1: What are you thinking about?

The proper answer to this, of course, is: "I'm sorry if I've been pensive, dear. I was just reflecting on what a warm, wonderful, thoughtful, caring, intelligent woman you are, and how lucky I am to have met you." This response obviously bears no resemblance to the true answer, which most likely is one of the following:

Baseball.
Football.
How fat you are.
How much prettier she is than you.
How I would spend the insurance money if you died.

(Perhaps the best response to this question was offered by Al Bundy, who once told Peg, "If I wanted you to know what I was thinking, I would be talking to you!")

*********

Question # 2: Do you love me?

The proper response is: "YES!" or, if you feel a more detailed answer is in order, "Yes, dear." Inappropriate responses include:

I suppose so.
Would it make you feel better if I said yes?
That depends on what you mean by love.
Does it matter?
Who, me?

*********

Question # 3: Do I look fat?

The correct answer is an emphatic: "Of course not!" Among the incorrect answers are:

Compared to what?
I wouldn't call you fat, but you're not exactly thin.
A little extra weight looks good on you.
I've seen fatter.
Could you repeat the question? I was just thinking about how I would spend the insurance money if you died.

*********

Question # 4: Do you think she's prettier than me?

Once again, the proper response is an emphatic: "Of course not!" Incorrect responses include:

Yes, but you have a better personality
Not prettier, but definitely thinner
Not as pretty as you, when you were her age
Define 'pretty'
Could you repeat the question? I was just thinking about how I would spend the insurance money if you died.

*********

Question #5: What would you do if I died?

A definite no-win question. (The real answer, or course, is "Buy a Corvette.")

No matter how you answer this, be prepared for at least an hour of follow-up questions, usually along the these lines:

*********

She....Would you get married again?

He.....Definitely not!

She....Why not - don't you like being married?

He.....Of course I do.

She....Then why wouldn't you remarry?

He.....Okay, I'd get married again.

She....You would? (With a hurtful look on her face)

He.....Yes, I would.

She....Would you sleep with her in our bed?

He.....Where else would we sleep?

She....Would you put away my pictures, and replace them with pictures of her?

He.....That would seem like the proper thing to do.

She....And would you let her use my golf clubs?

He.....She can't use them; she's left-handed.

You know you’re into Lean Six Sigma when….top 10 signs

A friend was telling me about a Lean project he is currently working on. Implementing Kanban, Lean Six Sigma training, set up reduction and quality initiatives, among other things, this person is currently consumed in Lean. My friend said that he thinks it’s time for a break because he is even dreaming about Kanban cards! My advice to him was to get use to it. Once you’re into Lean Six Sigma, you are always thinking Lean. We then came up with our Top 10 list of: You know you’re into Lean Six Sigma when…

  • Loading the dishwasher, you put the spoons, knives and forks together so it is faster to unload and put back in the drawer.
  • You wonder why it takes so many key strokes to get a boarding pass printed at the counter when you can do so fast checking in over the web. You also wonder what video game the ticket agent is really playing.
  • You think your neighbors should put their garbage cans next to each other and cut the number of garbage truck stops by at least 50%.
  • You now think garage sales are a good idea because it gives you a chance to kick off you’re “at home” 5S program.
  • Friends ask if you are taking a second language course because you are throwing words like muda and kaizen around.
  • Speaking of kaizen, although you’re kids admit to not knowing much about Lean, they are pretty sure that cleaning the backyard does not count as a kaizen event even though you’ve advertised it as such.
  • You don’t know why the kids aren’t excited to help clean the backyard since they will get another skill added to their Cross Training Matrix.
  • You now get why your wife wants you to ask for or follow directions (Process doc, standardization…)
  • You admire the bartender that can pour 2 drinks at the same time because you think they are into cycle time reduction.
  • During a break in the game you are using empty beer bottles to explain a 2-bin replenishment system to your buddies.
via: http://blog.kinaxis.com/2009/03/you-know-youre-into-lean-six-sigma-whentop-10-signs/

Fail fast to succeed sooner!

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 eLance.com and odesk.com 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 mturk.com pays people for completing simple tasks online as well.

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

Sites like ReviewMe.com and PayPerPost.com pay you to write reviews of Web sites on your free blog. Speaking of blogs, Squidoo.com pays a revenue share to people who contribute articles to their site.

SpringWise.com 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 http://training.brainreactions.com where I will be happy to offer a discount for all Indians if you email info@brainreactions.com 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.

via: http://www.rediff.com/getahead/2009/mar/12starting-a-business-on-your-own.htm

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

Via: http://www.twine.com/item/122mz8lz9-4c/wolfram-alpha-is-coming-and-it-could-be-as-important-as-google

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.

Competition

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, Twine.com, 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.