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5 Ways IBM Watson Changes Computing

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IBM Watson has already changed our perception of what computers can do -- it beat the best Jeopardy! champions, and it's being used for medical diagnoses. But what sets Watson apart? What makes it different?

1. It Reads Unstructured Text

When you feed data into a computer, traditionally it has been highly structured -- think a table listing all the U.S. Presidents, with columns for when their terms started and ended. Watson can read that kind of data, sure. But it specializes in reading raw human writing, also known as "unstructured data." You can feed it the biography of a president, and it will pick apart every sentence to learn what facts are contained in there. It will figure out all sorts of information within that huge body of text, and it doesn't require humans to put it all into a structured format first.

This ability to take in unstructured data is a huge strength for Watson. It means that the system can take in new bodies of knowledge quickly. You want it to know about medicine? Feed it the text of every medical journal you can find. You want it to learn Bible trivia? Feed it the Bible.

As we produce lots of information in unstructured form (for example, this blog post!), Watson is ready to consume it and make sense of it. As a trivia junkie, I can't wait to ask Watson some questions of my own.

2. We Train It

In addition to just dumping text into Watson, humans actually train the system to understand what's most important and reliable within the text. For instance, Watson pulled in all of Wikipedia prior to its Jeopardy! appearance, and stored that data offline. But it also had a huge corpus of other knowledge. Humans can tell Watson to trust one source of information (say, a biography of Bob Dylan) more than another (say, his Wikipedia entry). That doesn't mean the system ignores the less-trustworthy data -- but it knows which source to trust if there are conflicting facts.

But going deeper, when we think about Watson as a computing platform, we don't actually program Watson for new applications, per se. Instead of programming the computer, we train the computer using new data and human understanding of a topic. For instance, as a doctor you might train Watson to prefer newer medical journals over older ones -- so that data from the 1800s is taken with a grain of salt.

This shift from programming to training is part of why IBM calls this effort "Cognitive Computing." In the future, we will rely less on rote calculation, and more on interaction and learning.

3. It Asks Clarifying Questions

When Watson handles a tricky question in its current applications (like health care), it comes back with a set of possible results -- but it's also able to ask clarifying questions. It's clever enough to know that with a bit more information, it would be able to rule out an answer, or increase confidence in one of the answers it's already offering.

In health care, this could take the form of ordering a medical test. Presented with a series of facts about a patient, Watson could effectively say, "If you run this blood test, I'll have more confidence in my answer, or you can rule out these diseases." That's a very unusual thing for a computer to do, because it requires the computer to understand both what it knows and what it doesn't know. Knowledge may be power, but knowledge of your limitations is a superpower.

4. It Handles Open-Domain Questions

Most Question Answering systems are programmed to deal with a defined set of question types -- meaning you can only answer certain kinds of questions, phrased in certain ways, in order to get a response. Apple's Siri is an example of a closed-domain system. If I ask Siri a question, it has to be one of those questions Siri has been pre-programmed to answer (that's why so often, Siri gets confused and just offers to Google it for me). It's great when it works, but if you ask something just slightly out of its domain, the system falls apart.

But Watson is different. Watson handles "open-domain" questions, meaning anything you can think of to ask it. It uses Natural Language Processing (NLP) techniques to pick apart the words you give it, in order to "understand" the actual question being asked, even if you ask it in unusual ways. It also handles questions on any topic, combing through all the data it has, looking for the subject you're asking about.

IBM actually published a very helpful FAQ about Watson and IBM's DeepQA Project, a foundational technology used by Watson in generating hypotheses. My favorite question from that FAQ is: Is this going to be like HAL in 2001: A Space Odyssey? The answer is instructive (and I've added emphasis below):

Not exactly. The computer on Star Trek is a more appropriate comparison. The fictional computer system may be viewed as an interactive dialog agent that could answer questions and provide precise information on any topic. A primary goal for DeepQA is to greatly improve information seeking tasks over natural language content but ultimately, we would like to see the underlying technology help make computers more effective at communicating in human terms. Watson uses the DeepQA technology to push the envelope in natural language processing and automatic question answering. A powerful and fluent conversational agent, like the Star Trek computer, is a driving vision for this work.

I'll take the Trek computer over HAL any day. One to beam up!

5. It Shows Its Work

When Watson answers a question, it goes through a bunch of work to get there. First, Watson has to parse what kind of question is being asked, and what kind of answer is being sought. Second, Watson builds a series of hypothetical answers -- building a huge volume of possibilities, even if they're wrong. Third, it tests these hypotheses using a variety of different techniques, mostly based on the quality of the evidence. Finally, it merges and scores the possible answers: using its own question-answering history, the past reliability of various sources, and other techniques, Watson chooses the top answers, and presents them to a person.

But what's transformational here is that the person may then dig in and examine the underlying reasons that Watson chose those answers. During Jeopardy! we just got to see the top answers and a confidence score, but in a less time-sensitive application (like in a doctor's office, or when evaluating a given investment), humans can look at the answers as well as the supporting evidence. Because of this, humans can apply their own experience and expertise to decide whether that evidence is reliable. It's also easy to see how the evidence itself points to new areas of research -- if Watson tells you a medical study gave it confidence that an answer is correct, a doctor might want to go and read the whole study to see what else is in there.

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iStock // Ekaterina Minaeva
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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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Opening Ceremony
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These $425 Jeans Can Turn Into Jorts
May 19, 2017
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Opening Ceremony

Modular clothing used to consist of something simple, like a reversible jacket. Today, it’s a $425 pair of detachable jeans.

Apparel retailer Opening Ceremony recently debuted a pair of “2 in 1 Y/Project” trousers that look fairly peculiar. The legs are held to the crotch by a pair of loops, creating a disjointed C-3PO effect. Undo the loops and you can now remove the legs entirely, leaving a pair of jean shorts in their wake. The result goes from this:

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Opening Ceremony

To this:

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Opening Ceremony

The company also offers a slightly different cut with button tabs in black for $460. If these aren’t audacious enough for you, the Y/Project line includes jumpsuits with removable legs and garter-equipped jeans.

[h/t Mashable]

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