So, it's that time of the year again. Apple has unveiled its new ad-blocking technology for the latest iPhone, and people on the internet are losing their minds about why ads are good for you, and how this will be the end of the internet as we know it.

To which I reply: no, they are not. And good riddance. Don't let the door hit you on your way out.

I can't properly outline the mess that online ads have brought into internet users. Slower loading times, annoyingly loud sounds, erosion of our privacy, malware, and constant surveillance of our browsing habits. "But wait", says the marketer, "if we track you constantly, then we can sell you better stuff. If ads are tailored to you, then you wouldn't be annoyed by them - in fact, you would be grateful". To this, I usually reply by not replying, because arguing with someone who thinks I enjoy getting sales pitches is a person I don't bother discussing with.

The technical point is more interesting. If the ad industry where to collapse today, bringing down every ad-supported website along with it, the resulting meltdown is something I'd love to witness. Imagine the ad bubble blows up tomorrow. Thousands of websites disappear overnight. Panic. Chaos. Bloggers on the streets yelling "LIKE ME!" to random pedestrians. Desperate tweens crying in restaurants because they don't know what to do with their food pictures. But then we'd all cool down our collective heads, and realize that most of the stuff we enjoy is already produced for free, is not supported by ads, or both. Without the marketers, the internet goes on.

I still remember the "older days" in which advertising was restricted to a couple pop-ups here and there. And I can tell you: that early internet was anything but "dead". There was an incredible amount of content long before the advent of advertising. Which is why I cannot understand how anyone would believe that the internet needs cash to survive. There was plenty of content before ads, and there will be plenty of content afterwards, too

In a following article I'll detail replacements for popular websites and apps based on technology we already have, to show that ads are more a convenience than a necessity. But I want to repeat that a volunteer-run internet would be incredible. Would we lose several of those San Francisco startups burning cash as fast as they can? Yes. But I'm still not sure why should I even care. Marketers are free to continue spreading the notion that they are the saviors of the internet if they want to. But I'm also free to keep recommending Adblock Edge and Disconnect to everyone who wants to enjoy the internet as it was meant to be.

A final thought: The fact that the side with more money has not prevailed in this discussion makes me a little less cynical. If their rooms full of paid commenters have not squashed this discussion yet, then we must be doing something right.

A couple days ago I found a very interesting article, titled A neural network in 11 lines of Python. I always wanted to get a visualization of how a neural network works, so I took this as my opportunity. Taking that article as a base, I created a nice visualization of the simple case, and it ended up looking very nice.

This post is a small version of that. I'm going to show you how a neural network does its magic, using the magic of Javascript and SVG graphics.

The computation should proceed as follows: for neuron 3, we'll first multiply neurons 0-2 by the value of the edge that connects both neurons, sum those three values, and apply the sigmoid function to the result. In formal terms, $$n_3 = sigmoid(n_0 w_{0,3} + n_1 w_{1,3} + n_2 w_{2,3})$$ where n_i is the i-th neuron, and w_i,j is the weight that connects the neuron w_i with the neuron n_j.
The sigmoid function guarantees that the value for neuron 3 will be a value between 0 and 1. We repeat the same process for neurons 4 and 5.

So here we can see what our network is actually computing. If you have not yet read the article to the end, there's a very good chance that our network is returning random garbage, and that's fine - we haven't trained it yet, so of course the output makes no sense. This is called the forward step, in which I test my network and see what it's being computed.
For the second step, backpropagation, we'll need to write a couple tables, and see how bad our results are.

So we have now applied our corrections to the green weights, but how about the red and blue ones? We'll also correct those by applying a variation of the same principle: once I corrected the value for the output, I have to distribute the amount of error into every weight that contributed to its computation. This will allow me to correct the values for neurons 3 and 4, which I'll finally use to correct the values for the remaining weights.
This process is called backpropagation, because I'm analyzing the network backwards to correct the errors I made when computing forwards.

Now all that remains for the training process is for me to repeat these steps over and over, until the er ror (shown underneath) is small enough.

This is a very simple neural network (although not the simplest), implemented in Javascript. I've skipped some details in order to make the whole process simpler - If you want something slightly more challenging, I definitely suggest you to read the original article, which goes into the right level of detail.

I still want to improve on this, but I'm not entirely sure how. I think it would be nice to see how some neurons are given more (or less) weight, but I'm not sure how this should look like. If you have any ideas, feel free to tweet me.

Note: If you are quick enough, you might have noticed that the bias unit is missing in the hidden layer. The short version is: yes, it is. I only noticed once it was too late. I'll try and fix it in future revisions of this article.

Dear Spotify,

I think it's time to realize that you are not the service you once where. At first it was subtle, like that time when you changed the shade of green of your logo to the ugly one you are using now. Then there was that issue with offline mode, in which I lost a whole playlist because your synchronization with Windows Phone doesn't work. I guess I should have seen the signals back then.

But now... now you changed. More specifically, you change your Terms of Use, and I can only use you if I agree for you to collect my pictures and track my location, among others. And that's where I have to draw the line. Spotify, I'm breaking up with you.

Wait, let me rephrase that: I already broke up with you 10 minutes ago, when I canceled my paid subscription. This is just me being polite.

Let's be honest here: I was not paying for music. I can get free music pretty much everywhere - call it Youtube, Vimeo, MP3 forums or torrents, finding free music is not particularly difficult. I was paying those € 10 because I preferred that to paying with my data, like so many other services. But if you are going to build a profile of me anyway that is related not to what I like to listen (which is what you are supposed to care about) but about what I do in my daily life (which is none of your business), then what's the point? I was paying to get away of the claws of marketing, and that is now gone. And so am I.

I guess I'll just go back to the old way, building my own music collection and listening to it wherever and however I want. I may even get back to my old idea of a streaming server. I know we had our issues before, like when I kept looking for videogame music and you kept showing me crappy piano versions of them. Or when you wouldn't change the title of Fabiana Cantilo's misspelled album even after I pointed it out repeatedly. But this time it's different. This time I'm gone for good.

Bye, Spotify. I'll show up later on to collect the titles on my playlist, so I can download them later. You can keep my e-mail address. It was a throwaway anyway.

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.