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The Dow: Then & Now

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There's so much talk of the markets crashing, the Dow dropping, then rising, then dropping again. Under 9,000 today, over 9,000 tomorrow. But what do these numbers even mean? What is this "˜Dow' on which investors and the media fixate themselves? And is it really comprised of only 30 stocks?

The Dow is a market index, which is a listing of stocks that share some similar characteristic; they could belong to the same industry or they could all have similar market cap (how much a company is worth).

The Dow, the NASDAQ, and the S&P500 are the three main market indexes, providing the basic signal of how markets perform. The Dow is the most widely publicized and discussed, thus I'll follow suit.

First, some history.

The Dow Jones Industrial Average was started in 1896 by Charles Dow, the editor of the Wall Street Journal's precursor, the Customer's Afternoon Letter. Dow had a vision to create a benchmark that would project the general market conditions. Interesting to note that of the original 12 stocks of the DJIA, General Electric remains a part of the average (though it was taken out and reinstated twice). Today it is made up of 30 of the largest blue chip companies in the US that are traded on the NYSE, as selected by the editors of the Journal. The composition of the Dow changes fairly often. In fact, here's a list of the deletions and additions over the past 30 years.

936 points. So what? Even though it's called the Dow Jones Industrial AVERAGE, they don't simply add up the stock prices of the 30 companies and divide by 30. No, the average is price-weighted, meaning each stock influences the Dow in proportion to its share price. In order to account for stock splits (when a company's existing shares are divided into multiple shares making, for instance, a $100 share worth two $50 shares, which is typically done to make shares seem more affordable when a company's stock gets too high, or higher than that of other companies in the same industry) and stock dividends, they create a Dow divisor by which the sum of the 30 companies' prices is divided. That divisor is constantly changing depending on the stocks in the average, the splits and extra shares issued.

Can I buy the DJIA as if it were a stock? Well, not really. You can't buy the DJIA as if it were a security, but you can buy a Diamonds ETF, which basically gives you a small ownership of the 30 stocks in the Dow. An ETF is an Exchange-Traded Fund that puts a bunch of pieces of stock into one overarching stock. You may be more familiar with the SPDR—Standard & Poor's Depository Receipt, or Spider—which is an ETF for stocks in the S&P500. ETF's hedge your risk - if one stock does poorly, you have others to back you up. 

The Dow: Now and Then

Last week, the Dow almost crashed. Almost, but not quite - because by common definition a market crash is a 20% decline in a single day or several days. Last week, the Dow fell 18.2% or 1,874.19 points, making it the worst week in market history (prior to last week, the largest weekly percentage drop was the week ending July 22, 1933, when it fell 17%).

We saw a brief rebound, when history was made again. Monday, the Dow rose 936 points, the largest single day gain in market history. Then down again. Then back up. Then...

After the market crashed in 1929, it took twenty-five years to return to the pre-crash peak of 381.17 (late 1954). But this is not 1929. We have stronger governmental agencies to help fix the mess (The Fed has more power, and the FDIC provides deposit insurance.) We are coming down from an all-time high (14,164.53 a year ago). We acted fast and have allocated over $1 trillion (of our tax dollars) to fix this up. Despite these differences, however, history may not bode well for us.

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Scientists Think They Know How Whales Got So Big
May 24, 2017
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It can be difficult to understand how enormous the blue whale—the largest animal to ever exist—really is. The mammal can measure up to 105 feet long, have a tongue that can weigh as much as an elephant, and have a massive, golf cart–sized heart powering a 200-ton frame. But while the blue whale might currently be the Andre the Giant of the sea, it wasn’t always so imposing.

For the majority of the 30 million years that baleen whales (the blue whale is one) have occupied the Earth, the mammals usually topped off at roughly 30 feet in length. It wasn’t until about 3 million years ago that the clade of whales experienced an evolutionary growth spurt, tripling in size. And scientists haven’t had any concrete idea why, Wired reports.

A study published in the journal Proceedings of the Royal Society B might help change that. Researchers examined fossil records and studied phylogenetic models (evolutionary relationships) among baleen whales, and found some evidence that climate change may have been the catalyst for turning the large animals into behemoths.

As the ice ages wore on and oceans were receiving nutrient-rich runoff, the whales encountered an increasing number of krill—the small, shrimp-like creatures that provided a food source—resulting from upwelling waters. The more they ate, the more they grew, and their bodies adapted over time. Their mouths grew larger and their fat stores increased, helping them to fuel longer migrations to additional food-enriched areas. Today blue whales eat up to four tons of krill every day.

If climate change set the ancestors of the blue whale on the path to its enormous size today, the study invites the question of what it might do to them in the future. Changes in ocean currents or temperature could alter the amount of available nutrients to whales, cutting off their food supply. With demand for whale oil in the 1900s having already dented their numbers, scientists are hoping that further shifts in their oceanic ecosystem won’t relegate them to history.

[h/t Wired]