Based on the vast number of queries we have been receiving from users all around the world, we thought it would be very interesting to draw some inferences from it. We started with “Human Body Measurements”, one of the many topic areas in Wolfram|Alpha. We thought it would be a safe assumption to make that in more cases than not, when users query for data based on weight or height values, they are most likely looking for data about themselves (narcissism, thy name is Homo sapiens). Based on this assumption, we plotted all of the height and weight inputs and ended up with the following distribution:

We can see from this that the average Wolfram|Alpha user is an individual who weighs about 154 pounds and is between 5′ 9″ and 5′ 11″ tall. This translates to a BMI of between 21.5–22.7 for men or women. From these results, we see that the average user falls within normal distribution.

Let us see how this hypothetical Wolfram|Alpha user compares with the average American male or female:

Similarly, we can compare user heights with the height distribution of the general population in America: More »

Since Wolfram|Alpha launched in 2009, we’ve had numerous requests to add data on climate. As part of our one-year anniversary release, we recently added a vast set of historical climate data, drawing on studies from across the globe, which can be easily analyzed and correlated in Wolfram|Alpha.

You can now query for and compare the raw data from different climate model reconstructions and studies, as reported in peer-reviewed journals and by government agencies, many of them covering more than a thousand years of history. The full set of reconstructions was chosen from as broad a collection of sources as possible, from well-known records such as ice cores and tree rings, to corals, speleothems, and glacier lengths—and even some truly unusual ones, like grape harvest dates.

Or are you more interested in global greenhouse gas concentrations?

If you’re interested in exploring this vast area of climatology yourself, you can start by looking at a detailed summary of the most prominent models in literature: simply ask Wolfram|Alpha about “global climate”, which will bring up a selection of data sets that have figured prominently in the news over the past few years.

Wolfram|Alpha can also compute a more local analysis of recorded temperature variations. For example, you can compare the temperature variations recorded in specific parts of the globe, like the Northern Hemisphere. Or you can ask about studies conducted in specific countries, like the United Kingdom or Japan. More »

We’re in the midst of major enhancements to military data in Wolfram|Alpha, with newly added information on army, navy, and air force personnel for over 150 countries as well as statistics on many armaments, including stockpiles of nuclear warheads.

Let’s start with the big numbers. Type “army size of all countries” and you’ll see China, India, and the Korean Peninsula topping the list. China’s army alone includes 1.4 million soldiers and dwarfs the population of many smaller countries. The size of its combined army, navy, and air force is nearly equal to the entire population of Macedonia.

There’s an abundance of data on armaments, around the world as well, including estimates on nuclear stockpiles of the nine countries known to have detonated nuclear weapons; according to the latest available estimates, Russia has the largest stockpile with 13,000 warheads. Also new in Wolfram|Alpha are figures on conventional weapons, including aircraft carriers, battle tanks, and fighter jets. Try comparing countries’ armaments, such as “tanks USA vs Russia”, or asking about the number of submarines in the NATO alliance. More »

We recently added data on health indicators for more than 200 countries and territories. We now have World Health Organization data on health care workers, immunizations, water and sanitation, preventive care, tobacco use, weight, and more.

Data is also now available on specific types of health care personnel, such as physicians, nurses, and dentists, and Wolfram|Alpha can also compute per capita figures for each type of health professional. Check out the figures on midwives in South Africa or dentists in Iceland—or for a particularly interesting view, try asking about doctors per capita in all countries.

Other intriguing indicators include figures on hospital beds, drinking water and sanitation, tobacco use, weight and obesity, and reproduction and contraception.

Some data, such as for infant immunizations (including DTP, MCV, hepatitis B, and Hib), spans several years—which allows you to see dramatic increases in immunizations in many developing countries, as well as surprising declines in some first-world nations. More »

When Wolfram|Alpha was introduced, Stephen Wolfram blogged about it being the first “killer app” that resulted from his work on A New Kind of Science (NKS). We can now use this application of NKS to further our exploration and study within the NKS field. For example, one class of systems discussed in NKS is that of substitution systems. Now that a host of string substitution systems have been integrated into Wolfram|Alpha, we can explore a variety of these systems—not just the ones that are well known.

A string substitution system is composed of two parts: a string and a set of rules. The string looks like a series of numbers, say “0” and “1”. The rules describe what happens to each number in the string; for example,  “1” -> “0” and “0” -> “10”. Under our rules, our example string, “1”, transforms to “0”. In true NKS fashion, repeated iteration of these simple rules can give interesting behavior. Our example, which seems deceptively simple, can model the Fibonacci numbers. We simply document the length of the string each time we apply the rules to find that the series of lengths obtained at the end of each substitution corresponds to the Fibonacci series: {1, 1, 2, 3, 5…}. We see this in the following result:

Similarly, there is a string substitution system that models the Cantor set. The rules that define this substitution system are 1->101 and 0->000: More »

New curated data flows into Wolfram|Alpha every day. One addition that we haven’t highlighted before is crime data from the U.S. Department of Justice Statistics, including historical information on crimes and crime rates for all 50 states and thousands of individual cities.

A simple query for “U.S. Crime” will return the nation’s overall crime rate (the number of crimes per 100,000 people) and details on individual categories of violent and property crimes.

But Wolfram|Alpha’s true strength shows when you perform more-advanced comparisons and computations. For example, try comparing the crime statistics for two cities, such as “Crime Seattle vs. New York”; you can see clearly that although crime rates have fallen gradually over the last fifteen years, Seattle’s crime rate has maintained a level around 2.5 times that of New York City. More »

Need a tutor for solving equations? Solving equations is just one of hundreds of mathematical tasks that can be done using Wolfram|Alpha. Wolfram|Alpha can solve equations from middle school level all the way through college level and beyond. So next time you are stumped on an equation, consult Wolfram|Alpha for a little help.

Let’s start with the simpler stuff. Wolfram|Alpha can easily solve linear and quadratic equations, and even allows you to view a step-by-step solution of each problem.

What if the roots of the equation are complex? No worries; Wolfram|Alpha has no trouble solving equations over the complex plane.

Wolfram|Alpha can also solve cubic and quartic equations in terms of radicals.

Of course, some solutions are too large or cannot be represented in terms of radicals; Wolfram|Alpha will then return numerical solutions with a “More digits” button. More »

We’ve blogged about Wolfram|Alpha’s name data before—but as we stroll into the 2010 movie-awards season here in the United States, we wanted to remind you about this particular tool and to point out a few interesting movie-related queries.

Marlon Brando’s breakthrough film role was 1951’s A Streetcar Named Desire, which was followed quickly by major roles in Viva Zapata! (1952), Julius Caesar (1952), The Wild One (1953), and On the Waterfront (1954), which brought him his first Academy Award. It’s hard to attribute the growing popularity of the name “Marlon” in the early 1950s to anything but his growing star power—the name just cracked the top 1000 U.S. names in 1950, but rose to #344 in 1955. His award-winning performance in 1972’s The Godfather prompted an ever bigger jump: “Marlon” became the 218^th most popular name in the U.S. that year.

The name “Dustin” didn’t register among the top 1000 U.S. names at all until 1968—one year after Dustin Hoffman’s appearance in The Graduate—when it entered at #368. The name grew steadily in popularity through the early 1980s, hovering around #42 from 1981 through 1986. Film buffs may wonder whether the legendary box-office flop Ishtar (1987) had anything to do with the subsequent decline in the popularity of “Dustin”—even though Mr. Hoffman brought home an Academy Award for Rain Man in 1988.

Even science-fiction fans might be surprised by this one: in 1999, the year that The Matrix was released, the female name “Trinity” made a startling jump in the ranks to #209, from #525 the previous year; and even though that movie’s sequels (both released in 2003) were somewhat less well received, the name stayed popular—climbing all the way to #48 in 2004. More »

During the holidays we posted “New Features in Wolfram|Alpha: Year-End Update” highlighting some of the most notable datasets and enhancements added to Wolfram|Alpha since its launch this past May. We are thrilled by the questions and feedback many of you posted in the comments section. Your feedback is incredibly valuable to the development of Wolfram|Alpha. Many of the additions presented in the post were the result of previous suggestions from Wolfram|Alpha users.

We hope to continue this dialogue as we update Wolfram|Alpha’s ever-growing knowledge base in 2010. You wrote 170-plus comments to the “Year-End Update” post, and we’ve sent questions from those comments to Wolfram|Alpha’s developers and domain experts for answers. We’ll be reporting their responses in a series of blog posts.

So without further ado…

On the other hand, the more you add, the more holes you’ll find as people search and then become frustrated when specific things they want aren’t available. Please keep tracking your “cannot find” results!

A: Although we haven’t yet covered every possible domain of knowledge, that’s certainly our goal—and feedback like yours is definitely considered and added to our “to-do” list. Each time a query produces one of those “Wolfram|Alpha isn’t sure how to compute an answer from your input” messages, it shows up in our logs. Sometimes we have the data, but need to tweak Wolfram|Alpha’s linguistic code so it recognizes more types of questions. If we don’t have the data, someone looks closely at your question and at sources that might be able to answer such questions, and more often than not those sources are incorporated into our planning. Many of the features mentioned in our year-end review were direct responses to user requests, and many more are in the works.

1. My first query to W/A, about Olympic marathon winners, failed “Could not connect to a W/A server” or something like that. I thought the point of the downloaded version was to free you from wi fi restrictions.
2. Given the ever changing nature of knowledge and your impressive programme of developments, can iPhone customers expect updates in the future?

A: As we’ve noted before, the iPhone and iPod touch are terrific platforms, but they simply aren’t powerful enough to solve many queries in a reasonable amount of time, if at all; the Wolfram|Alpha App for the iPhone does require an internet connection. Users of the app will therefore benefit from all the same data and algorithm updates that are added weekly to the main Wolfram|Alpha website, as well as ongoing bug fixes and enhancements to the app itself. More »

Four hundred years ago, on January 7, 1610, Galileo pointed his telescope at the planet Jupiter and discovered that it had its own moons. This discovery changed our perspective on the universe.

Prior to Galileo’s discovery, the Earth-centric Ptolemaic system was the standard view of the cosmos where Earth was the center–heaven was above and Earth was below. Copernicus had proposed a heliocentric model, but it was a mental exercise meant to simplify the complicated Ptolemaic system. Galileo’s discovery was the first one that showed evidence that something was orbiting a body other than Earth. If Jupiter had things in orbit around it, why couldn’t other bodies?

At the time telescopes were cutting-edge, and only a few people had them. What Galileo did was an instructive example on how to combine technology and curiosity.

Today you can recreate the moment with today’s technology by typing “Jupiter” into Wolfram|Alpha.

Among the pods about Jupiter, there is a graphic showing the current configuration of the so-called “Galilean moons”, the ones Galileo saw 400 years ago: Io, Europa, Ganymede, and Callisto.

Type “Galilean moons” to find out more about them. Or for historical curiosity, try “January 7, 1610” and find out more about that day.

You can even virtually recreate Galileo’s observations for yourself. Here’s how he depicted what he saw 400 years ago on the night of January 7:

And here is what he saw a few days later:

In Galileo’s diagrams, the circle represents Jupiter, and the asterisks represent the moons he observed. He didn’t know they were moons until the second observation, when they had changed position. More »