Solar eclipses happen when the Moon moves between the Earth and the Sun. The Moon blocks the Sun’s light, which puts a kink into our daily expectations, making it get dark during the day. A fuzzy estimate puts the frequency of total solar eclipses at about one or two per year. This number can vary quite a bit. Often, these eclipses are only visible along narrow paths that are in out-of-the-way places that make it difficult for them to be observed (e.g. over the open ocean). Cruise ships are often booked for the sole purpose of chasing these solar eclipses for those people willing to set sail and pay the money to do so.

On May 20 of this year, people in the western United States and Pacific Ocean islands will be in a position to observe one of these solar eclipses without having to travel on the high seas.

Unfortunately for those of us in the Wolfram|Alpha headquarters in Champaign, Illinois, we will only be able to see the beginning stages of the eclipse, since the Sun sets before the show gets off to a great start. The last time the central US was graced with an eclipse of this type was in 1994. I still have fond memories of that eclipse, and even managed to get a few pictures. All of the tiny holes in the leaves acted like small pinhole cameras and projected hundreds of tiny crescents on the ground as mid-eclipse approached.

If you live in Texas, along a line to northern California, you will have a better show this time around.

The traditional view of a solar eclipse is that the Moon blocks the Sun entirely, allowing you to look at the Sun directly and see the faint glow of the Sun’s outer atmosphere, a rather beautiful sight. As usual, reality is more complicated. The Moon is not always at the same distance from Earth. On May 20 of this year, the Moon is near its farthest distance from the Earth, which means that the apparent size of the Moon is smaller than average, and so in fact will appear smaller than the Sun. So, when the Moon moves directly in front of the Sun, it won’t block all of it. A ring of the Sun’s disk will still be visible around the Moon, which is why this is called an annular eclipse.

This is enough sunlight to still make it dangerous to look directly at the Sun, so to observe this eclipse, you will need special glasses or observation methods. Check with your local astronomy clubs or observatories to see if they are hosting any events that might make use of these special tools if you don’t have any of your own.

This event is a nice warm-up to another eclipse, of sorts, in June. During that event, it’s not the Moon that will move in front of the Sun, but the planet Venus. Expect more information on this popular event soon.

The x, y value for the color is shown on the CIE 1931 chromaticity diagram that follows. Alternatively, specific colors can be queried directly by specifying a color value in corresponding spaces such as #DFFF00, RGB 0.58, 0.77, 0.45, HSV 96 degrees, 42%, 77%, XYZ 70 40 50, and Lab 80 30 20.

Another group of interesting properties is the various types of nearby and related colors:

Different types of related colors can be queried individually too. For example, complement red, or simply - cyan, finds the complementary color:

Besides computing various properties of individual colors, Wolfram|Alpha allows users to do arithmetic computation with multiple colors, that is, mixing colors. Green + orange by default gives the result of additive color mixing:

The alternative results using subtractive color mixing and color blending are compared at the bottom:

The color arithmetic queries can have coefficients: 2 red + 0.5 light orange + 0.3 brown. Queries with negative coefficients by default assume subtractive color mixing: green - 0.3 chartreuse.

In addition to answering questions about particular colors, we can investigate the properties of all reds or the set of all greens.

While the span of various colors can be hard to define, in a CIE chromaticity diagram, we can generally agree on certain categories according to the colors of the visible spectrum. If we take these colors to the central white point of the diagram, we can consider the cones thus defined as the set of a particular color.

Wolfram|Alpha can show us where a color exists on the x, y chromaticity diagram and how much space it occupies out of the total volume in various different color spaces. We can also see visually how it fills a given color space.

We might also be interested in specific named colors such the set of red HTML colors. We will be adding more grouping of colors like this in the coming months.

Looking further into the colors of the spectrum, Wolfram|Alpha is able to tell us about specific colors of light such as yellow light.

This reveals a wealth of information about the physical properties of colored light that we can delve into further: What is the effective temperature of yellow light? What is the bandwidth of blue light? What is the momentum of a red photon? We can also look into questions like the color of 500 nm light.

The sun is our largest producer of visible light, and we can ask Wolfram|Alpha how much solar radiation is green. We can also look into other emitters of visible light: spectral lines of violet, red lasers, and which organisms give off cyan light.

Absorption properties of light are equally important. Plants absorb various wavelengths of light at different rates, which can effect their growth under different light sources. Asking about the chlorophyll absorption of green light will show us the relative absorption. We might cross-reference that with emission by looking for green light sources, which shows the amount of light produced for various common light sources within a given spectral range.

One of the more important absorbers of visible light is our eyes. The chromaticity diagram shows us the range of human vision while the color-matching curves that are also provided highlight the human eye’s response to differing components of XYZ color space.

We hope all this new functionality is going to be exciting for the Wolfram|Alpha community, and as always, we look forward to hearing more from our users on additional functionality they would like to see in this thoroughly “colorful” world of colors.

But thanks to the outstanding work of our friends at the US Census Bureau, we’ve been able to take some big steps toward filling this “data gap.” The annual American Community Survey (ACS) is designed to replace the old long-form decennial census questionnaire, covering information about age, sex, race, ethnicity, education, income, and much more. In 2006, the Census Bureau released the first single-year ACS estimates, but only for areas with populations over 65,000; in 2008, three-year estimates came out for areas with populations of 20,000 or more; and in 2010, the first five-year estimates were released, covering every geographic area in the country.

What does this mean for Wolfram|Alpha? It means that when we add new data from the five-year ACS estimates, we can immediately compute answers to a new set of questions about virtually every city, school district, congressional district, county, metropolitan area, and state in the country—as well as questions about the nation overall. You can ask about a specific place, compare several specific places, or generate distributions and rankings for a single property mapped over a large set of places.

Let’s start with one of the most fundamental—and most frequently requested—demographic breakdowns: population by age and sex. I’ve always been able to ask Wolfram|Alpha simple questions like “What’s the population of the city of Mars, PA?” (my tiny hometown). But I couldn’t dig any deeper into those numbers.

Now that we’ve added some ACS estimates to our knowledge base, I can ask for a population pyramid for Mars, PA, or I could ask what fraction of the population of the city is female, or even what fraction of the population are girls age 0 to 4. But then I might be curious about how the city proper compares to my old school district. Or I might want to analyze and rank the proportion of school-age children among school districts in my home county. Since it’s an election year, I also find myself asking Wolfram|Alpha to do things like compare the middle-aged male population fraction of PA congressional districts or compare the senior citizen population fraction of Florida and Nevada, two other supposed swing states in the upcoming election.

Even limiting myself to questions about population by age and sex, I’ve squandered a probably-unhealthy amount of time comparing the shape of specific cities’ age pyramids. Consider the distinctive spikes of college towns like Champaign, Illinois or Binghamton, NY—or the dramatically different “bulges” for Manhattan and Staten Island.

And those are only questions related to a single table of ACS estimates. We’ve already added data on race, Hispanic origin, and poverty; estimates of educational attainment, school enrollment, household income, and more are coming within the next few weeks. Because each of these topics represents such a large volume of data and such a wealth of new things to compute with Wolfram|Alpha, we plan to publish a new blog post each week for the next month or so. We’ll focus on one or two new topics, with lots of examples of new ACS-based queries and other computations that mash up ACS estimates with other datasets in Wolfram|Alpha.

We’ll also be making some subtle improvements to Wolfram|Alpha’s ability to understand complex, natural-language queries about this data, but, as always, it helps to have lots of real test cases from users. So dig in, play around, and let us know what works—or what could work better. We’re excited to make this rich data more accessible to the general public and eager to hear what you think about it.

Going back to the home improvement store, we are presented with the large stacks of construction lumber. Typically, construction lumber is made from softwoods like pine or fir. These are cheaper and lighter than hardwoods like oak, and so it’s uncommon to find something like a 2x4 made of oak. The exact type of wood used to make construction lumber varies depending on where you live, typically favoring what is abundant and affordable in your area.

When you buy a 2x4 or other related piece of dimensional lumber, you are actually getting less than you might think. In today’s world, a 2x4 is only a 2x4 when it’s rough cut from a log. After finishing and drying, these boards will shrink. By the time you buy this from your local store, it is smaller than its rough cut size. Wolfram|Alpha includes information on both the original, nominal, and actual size you find in the store. Combined with the knowledge Wolfram|Alpha already has about types of wood, we can compute things like weight ranges based on the families of wood being used.

You can also vary the length and wood type as desired.

While Wolfram|Alpha can’t do the carpentry work for you, it can help you understand exactly what you need to do the job.

During my years as a graduate student I had the chance to live in three different countries and experience different working environments: other than my native Italy, I lived in Paris, where my PhD was based at ENS, and in Princeton, where I was lucky enough to spend time at the Institute for Advanced Study. However, at the end of my PhD, I felt that most of my interest in what I was doing was gone and that I needed to try something new.

Once at the Summer School, I had the chance to meet and chat with Stephen Wolfram as he helped me come up with a problem to work on. One of the first things I told him was that I was weary of open-ended academic kinds of problems and I was afraid no one was ever going to read my papers. I said that I wanted to deal with intellectual challenges, but I also wanted to tackle something that had a clear beginning and end.

His reply came as a disappointment, since what he suggested I work on was both completely outside my area of expertise and clearly one of those impossibly wide problems that I was now skeptical of. What did he say?

Stephen asked me to devise a system to generate a plain English description of a time series. My disappointment vanished quickly when I realized that while a general answer to this kind of question was well beyond the scope of the three weeks I had at the Summer School, there was a combination of neat heuristics and information theory ideas that might do a reasonable job in most cases.

Little did I know that I loved this kind of experimental coding and that it was going to become my full-time job as a Wolfram employee a few months later. My project turned out to be a success, and soon I was encouraged to apply for a job at Wolfram Research, the company that made Mathematica–a product my friends and I had long considered a godsend for our work.

In my first four months at the company, I got involved in a very exciting project that has taken Wolfram|Alpha in an entirely new direction. I worked in a small group on what was known internally by the name tabular input (or TI to its friends). Along with image upload, file upload, and data download, TI formed one of the foundations of our subscription service, Wolfram|Alpha Pro.

The idea behind this project was to treat data as if it were language: columns of a single type of data, like numbers, dates, places, or what have you, act as words in a sentence. And just like sentences of human language, groups of these words are more than the sum of their parts. To give a particularly important example, think about a time series, which is a column of dates plus a column of one or more numeric quantities.

We knew we had at our disposal a huge amount of data coming from Wolfram|Alpha as well as the power of the parser to recognize what this data was all about. We could parse dates in any format, various currencies, and units. This made it natural to think along these lines: This column is about currency. It appears alongside a column of dates. We happen to have data about inflation in that country. We can do an inflation adjustment on the currency values!

So we had the notion of parsing data to determine what kinds of analysis to perform. What about those analyses? How should they look and feel? How should we rank them if many different ones were possible?

Now, along with a small group of smart people coming from wildly different backgrounds–economics, computational biology, pure math, and statistics, to mention a few–we started thinking about what this new data language was trying to say. (Interestingly, most of these people were Summer School alumni from several past years.)

A key fact about our group was that, while everyone had the vocabulary of mathematics in common, no single person knew it all. Take me, for example. As a physicist, I am perfectly at ease with nonlinear fits, but I couldn’t read a regression table to save my life! But to my colleague, an economist, regression tables were second nature.

And in arguing about how to display these results in a way that made sense to a non-expert and an expert both, we got to the crux of each analysis. In fact, this was also how I came up with the idea of spelling out in plain English the key results of our data analysis and, so, funnily enough, this is where my Summer School project came full circle.

For example, one of the sample datasets was the passenger list from the Titanic: age, class, gender, and whether or not they had made it to the lifeboats. Now, a logistic regression is the perfect tool to see if being a woman or a child actually increased the chances of surviving, but can you actually interpret one? As you can see below, we’ve done the job for you! And in case you were wondering what the result is, you had a better chance of survival if you were female, young, and a first-class passenger.

Each one of us had to think: “What would I, as an expert in this field, do with this data? How would I visualize it? What kind of analysis would I perform on it?” It turns out this is one of the key insights of Wolfram|Alpha: to bring expert knowledge to the tips of everybody’s fingers (or vocal cords, if you happen to use Siri).

What I ended up finding really addictive about this job is that I get to wrap my mind around research-grade problems, but after I understand them, I have to quite quickly turn my ideas into workable features of a website that is used by a lot of people daily. At Wolfram, I get the intellectual challenge I was looking for, and thousands read my research results every day.