Original image
YouTube / IBM

"What is IBM Watson?" 7 Videos from the Jeopardy! Era

Original image
YouTube / IBM

IBM Watson is a computer system that defines a new era of computing. Watson is IBM's answer to the next generation of computing challenges, in which people want to interact with systems that truly understand natural language rather than simply crunching numbers.

Today, I bring you three articles about Watson. First, let's take a look at Watson during its early days playing Jeopardy!, which I first covered starting in 2010. Here are some of the best Watson-on-Jeopardy! videos out there, with a special focus on the computer science involved. Keep in mind that Jeopardy! is only the first challenge Watson took on -- in our next two articles we'll see what it's been up to since then. But for now, get some popcorn!

1. IBM and the Jeopardy Challenge

In June 2010 IBM released this video, announcing its next Grand Challenge: can a computer compete against the world's best Jeopardy! players? After this brief intro, we'll roll through prep for battle, and the final match itself (highlights of it, anyway). But here's where it started.

2. Watson's Face and Voice

Watson itself is a huge collection of IBM POWER7 servers, and it started without a voice nor physical avatar to represent it onstage among humans. So what was going to stand at the podium on Jeopardy!, and what voice would come out of its virtual mouth? Generative artist Joshua Davis developed the code that created Watson's animation. He started with IBM's "Smarter Planet" logo and built on it; as a subtle tribute to Douglas Adams fans, he added 42 lines swarming around the planet, shifting color along with Watson's confidence in a given answer. In this video, we see both the creation of the avatar and Watson's voice.

3. The Science Behind an Answer

Taking a single Jeopardy! clue as a jumping-off point, we can understand how Watson arrives at an answer (technically, many answers) by combining a series of techniques and aggressively filtering them to arrive at its best answer. The clue? "The first person mentioned by name in 'The Man in the Iron Mask' is the hero of a previous book by the same author." If you're into the computer science aspects of Watson, this is the video for you.

4. Miles O'Brien vs. Watson

Prior to the big Jeopardy! televised games, many sparring rounds were held, hosted by Todd Alan Crain of The Onion. Here's a full game (posted by PBS NewsHour) in which journalist Miles O'Brien and IBM researcher Dr. David Gondek take on the machine. (You'll recognize Gondek from his appearance in many of the videos in this article.) Because this is a full game, it's pretty long -- you may want to save this one for later if you're pressed for time. (Or check out a 90-second roundup of Watson's performance on pop culture questions.)

5. Final Jeopardy! and the Future of Watson

This ten-minute documentary tells the story of the big Jeopardy! games, from the perspectives of the IBM research team as well as the humans who matched wits with the machine. There were a series of nail-biters in those games, and they're explained here in detail. In the areas where Watson made mistakes, this video explains why they happened. It also goes deep on the IBM research team, explaining how they felt watching their creation score one not just for computers, but for humankind. See for yourself:

6. NOVA: Smartest Machine on Earth

In 2012, the PBS series NOVA covered Watson in an hour-long documentary called Smartest Machine on Earth. The video is available online (or you can buy a DVD or iTunes version in higher quality).

Watch Smartest Machine on Earth on PBS. See more from NOVA.

7. They Were There: The IBM Centennial

Here's a bonus video! IBM turned 100 years old in 2011. To mark the occasion, they commissioned a documentary from director Errol Morris, with music by Philip Glass. I was most moved by the part about the space race, starting around 14:28, with an especially poignant Apollo 13 segment at 16:20.

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

Original image
Scientists Think They Know How Whales Got So Big
May 24, 2017
Original image

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]