Here's a joke I heard once at a music academy:

How do you keep a pianist from playing? You take away their music sheet.

How do you keep a guitarist from playing? You give them a music sheet.

This joke rings oh-so-true because it highlights a key point for those of us who tried to learn guitar by ourselves: the typical amateur guitar player doesn't know how to read music, and (s)he doesn't care about it. Somehow they manage, but how they do that remains a mistery to me.

Typical guitar tabs (those you buy at a shop or download from the internet) contain therefore little more than the lyrics for a song and the points at which you are supposed to switch from one chord to another. This works pretty well for your left hand, but how about the right one? Should I just move it up and down? And at which speed? "Well", says the guitar book, "you should just do whatever feels natural". This is of course useless - what am I, the song whisperer? What if nothing feels natural? Do I just sit there in silence?

Let's take the following example, which I borrowed from Ultimate-guitar.com

RHYTHM FOR INTRO AND VERSE:
    D                       G       A
E|--2--2-2-0-2---2-3-2-0-2--3--3-3--0--0--0-0-0-0-0---|
B|--3--3-3-3-3---3-3-3-3-3--3--3-3--2--2--2-3-2-2-2---|
G|--0--0-0-0-0---0-0-0-0-0--0--0-0--2--2--2-2-2-2-2---|
D|--------------------------2--2-2--0--0--0-0-0-0-0---|
A|--------------------------3--3-3--------------------|

INTRO
D   G  A   [play twice]

VERSE 1:
       D
You'll say
G      A           D
we got nothing in common
   G      A         D
no common ground to start from
    G       A      D     G  A
and we're falling apart

This is actually a fairly complete piece: it shows the lyrics and notes (lower half) along with an attempt at explaining how should the strumming (i.e., what to do with your right hand) be performed. But here's the thing: it's not clear at all which strokes should be "up" and "down", nor the duration and silences between them. You cannot derive rhytm from this information, which is pretty bad for a section titled "Rhytm for intro and verse". And here's an extra fact: the "D" section should actually be played exactly the same as the "G A" section, but good luck discovering that from this notation. This is a known bug of guitar tabs, and yet I have several books with songs that don't even include such a section, either because they don't care or because they realized it's useless.

This is one of those very few problems that is currently solved by, of all things, Youtube. It's not too hard to find a "How to play Breakfast at Tiffany's" video tutorial, where some dude will spend some time showing you slow motion strumming, so you can play the whole thing. But how come youtubers have fixed this problem so fast, while guitar books have remained the same for decades? Why isn't everybody complaining? My theory is that the typical amateur guitarist picks up a guitar, downloads one of these tabs, fails at getting anything out of it, and quits guitar forever saying "guitars are hard".

I don't really have a good solution, because any attempt at formalizing the strumming will undoubtedly require some knowledge about rhytm, and guitar players seem to hate that. Perhaps that's how we ended up in this mess in the first place. Or perhaps there's a super easy, totally intuitive method that I've always missed for one reason or another.

But then again: how can a method do any good if it's never taught?

This article is the fourth of a series in which I explain what my research is about in (I hope) a simple and straightforward manner. For more details, feel free to check the Research section.

Let's continue with our idea of guiding people around like I mentioned in the previous article. It turns out that people usually make mistakes, either because the instruction we gave was confusing, or because they weren't paying attention. How can I prevent those mistakes?

For my first research project at the University of Potsdam, we designed a system that took two things into account: how clear an instruction was, and what did the player do after hearing it. Let's focus on those points.

For the first part, which we called the Semantic Model, a system tries to guess what will the user understand after hearing an instruction. If the instruction says "open the door", and there's only one door nearby, then you'll probably open that one. But what if I tell you "press the red button" and there are two red buttons? Which one will you press? In this case, the model tells us "this instruction is confusing, so I don't know what the user will do", and we can use that to make a better instruction.

For the second part, which we called the Observational Model, a second system tries to guess what are your intentions based on what you are doing now. For instance, if you are walking towards the door with your arm extended, then there's a good chance you are going to open that door. Similarly, if you were walking towards a button, but then you stopped, looked around and walked away, then I'm sure you wanted at first to press that button but changed your mind.

When we put both models together, they are pretty good at guessing what you are trying to do: when the first one says "I'm sure you'll press one of the red buttons" and the second one says "I'm sure you'll press either this blue button or that red one", we combine them both and get "We are sure you'll press that red button". Even though neither of them were absolutely sure about what you'd do, together they can deduct the right answer.

Each system takes into account different clues to make their guess. The semantic model pays attention mostly to what the instruction says: did I mention a color? Is there any direction, such as "in front of"? Did I mention just one thing or several? And which buttons were visible when you heard the instruction? The other model, on the other hand, takes into account what you are doing: how fast you are moving, in which direction, which buttons are getting closer, and which ones are you ignoring, among others.

Something that both models like to consider is which buttons were more likely to call your attention, either because you looked at them for a long time or because one of them is more interesting. But there's a catch: computer's don't have eyes! They don't know what you are really looking at, right? Finding a way of solving this problem is what my next article will be about.

This article is the third of a series in which I explain what my research is about in (I hope) a simple and straightforward manner. For more details, feel free to check the Research section.

For my first research paper during my PhD, the basic idea was pretty simple. Imagine that, after recording several hours of people being guided around a room, I realize the following: everytime a player stood in front of a door, and someone told them "go straight", they walked through the door. So now I ask: if you are standing in front of a door, and I want you to walk through it, would it be enough for me to say "go straight", like before? My research team and I wanted to give this question an answer, so this is what we did.

We looked at our recorded data. Whenever we saw a player moving somewhere, we took notes about where the player was, where is the player now, and what was the instruction that convinced the player to move from one place to the other. We then created a big dictionary, where each entry reads "to move the player from point A to point B, say this". Quite smart, right?

The most important part about this idea is that we don't need to teach our computer how to understand language - in fact, when our system reads "turn right" in our dictionary, it has no idea about what "turn" or "right" mean. All our system cares about is that saying "turn right", for some strange reason, causes people to look to the right. This makes our system a lot simpler than other systems that try to understand everything.

Now, let's complicate things a bit: let's say I tell you "walk through the door to your left". You turn left, walk through the door, take 7 steps, give a full turn to look at the room, and then you wait for me to say something else. Which of those things you did because I told you, and which ones you did because you felt like it?

Since we didn't really know the answer, we tried two ideas: in the first case, we decided that everything you did was a reaction to our instruction (including the final turn), while in the second one we only considered the first action (turning left), and nothing else. As you can see, neither approach is truly correct: one is too short, and the other one is too long. But in research we like trying simple ideas first, and we decided to give these two a try.

Our results showed that the second approach works better, because if you advance just one step I can guide you to the next, but if you do too many things at once there's a chance you'll get confused and lost. Also, since our system is repeating what other humans said before, players thought the instructions were not too artificial.

Not bad for my first project, right?

This article is the second of a series in which I explain what my research is about in (I hope) a simple and straightforward manner. For more details, feel free to check the Research section.

The GIVE Challenge is a competition started in the University of Saarland, created to collect data about human behavior. Since most of my research is based on that data, it's a good moment to explain what is it about.

We all know GPS by now - whenever we go by car somewhere new, we just type the direction and the GPS guides us. But have you ever thought about how hard it is to give instructions, like your GPS does? For instance, if we are in a roundabout and I say "take the third street to your right", does that mean I have to count all streets, or should I ignore wrong ways? And how much time do you need to react to my directions? These are important question, because they reveal a bit more about how humans act and think.

If we want answers, we need to collect data (reaction times, distance to other cars, misunderstandings, etc), and that data is very difficult to get. For our example, you would have to drive while wearing special glasses, a military GPS, and keep track of all the cars and pedestrians around you. So you might wonder, couldn't we make something simpler, but still useful? My adviser and other researchers asked themselves this exact same question in 2007, and that is how the GIVE Challenge was born.

In GIVE, a person sits in front of a computer, and they play a game. The game is pretty easy - all the person has to do is walk around a virtual house and press some buttons in a certain order. Just like a GPS, they receive instructions telling them where to go and what to do.

In the first variant of the GIVE Challenge, the instructions are given by a person using a computer in a different room. We then record all the information about how the player reacts to the instructions: if the instruction says "turn right", how much does the player turn? Do they just turn, or do they walk too? And how long does it take them? By recording every single movement of the player inside this game, we can answer questions like that.

There's also a second variant: we can write a program that guides the person inside the game, and see how good (or how bad) its instructions are. While a common GPS only cares about streets, our programs have a harder time: humans are not limited to just following streets like cars do, so the instructions are more complex. GIVE is a good way of testing how smart our computers are, and that's why we've been using it for many years now.

We've so far recorded over 340 hours of human movements, divided in 2500 games. Believe it or not this is not too much data, but it's a good start. We have extracted several interesting results from this data, some of which I talk about in future articles.

This article is the first of a series in which I explain what my research is about in (I hope) a simple and straightforward manner. For more details, feel free to check the Research section.

In research, we often want to teach computers how to do a new task, but that is difficult because computers are not too smart, and teaching them even a simple task takes a lot of work. So let's say I want my computer to tell me whether an e-mail is important or not. If I could teach my computer that, then it could show me important e-mails first and save me the trouble of sorting through them daily.

One way of teaching tasks to computers is by doing the job myself, and then make the computer repeat what I did. This is something scientists have been doing for a long time, and today we have a set of steps that every researcher should follow.

The first step is to collect as many e-mails as possible, both important and not. In science, such a big set of e-mails is called a corpus.

Now, just like you wouldn't know what kind of e-mails I consider important, neither does a computer. So the second step is to go through all those e-mails I collected, and mark which ones are important. I'll create two groups, one called "training" and another one called "testing". The first group will contain 4 out of 5 emails, picked at random, while the second group will have the remaining ones.

The third step, unsurprisingly called the training stage, requires the computer to analyze all the e-mails I put in the training group and decide what makes an e-mail important. We would expect our computer to understand, for instance, that since every e-mail containing the word "SALE" was marked as unimportant, then it might be a good idea to mark all e-mails with commercial offers as unimportant. This is by far the hardest step, and there are many ways in which I can influence how well the computer will learn.

The fourth and final step is to give our computer a test, to see whether it learned something useful or not. For this step, called the testing stage, I'll go through each e-mail from the testing group, show the computer the e-mail's text, and ask whether it's important or not. Then I compare the computer's answers with mine, and I'll use that result to decide how good (or how bad) my computer learned the task. If the results are not good enough I can always go back, change how are the e-mails analyzed, and try again. If the results are good, on the other hand, I can trust my program to sort my e-mail from now on.

This is pretty much half my daily work. Collecting enough data (e-mails) is either complicated, expensive, takes a lot of time, or all of that together. And remember I said there are several ways in which a computer might learn? We have to try some of those alternatives too.

Finally, training is usually very slow - in my last project, it took almost a week.

I usually dedicate that time to play Solitaire.