Duke Nukem is not coming back

I loved Duke Nukem 3D when I was a child. I replayed so many times, even very recently. So good. I heard about the Duke Nukem Forever fiasco several times but I never looked into it. Until I did:

Oh my god. I want that video erased from my memory. Do not watch it. It’s not the youtuber. It’s the game. Embarrassing.

What I was expecting? That the game would be unfinished, or not really playable, or bad in graphics or bugs.

What I got? An exaltation of Americanism, sexism (specially sexism), and how cool it is to be brain-dead.

I was going to buy the game regardless of how bad it was and play it just for the giggles. But like that? I’m not touching that even with a ten foot pole.

I’m not clear what happened here, some fingers point to Mr. Randy Pitchford. I have no idea. But what it is crystal clear is that the vision of Duke Nukem went south, very fast, very quickly, years before Duke Nukem Forever was even envisioned.

There’s no problem on the game per se, but on what they attempted to do. That’s why I think Duke Nukem is totally unsalvageable, there’s no coming back for it. Whatever they try to do, it will be bad.

The stupid “ego meter” was on games before DNF already! Come on! who thinks this is a good idea?

Someone thought that it was unrealistic for Duke to regain health by taking a piss, so they thought that it would be better to rename it to Ego. Nice, now your character becomes someone derailed that thinks that their own ego would deflect bullets or close wounds. Maybe they thought it was a good joke. Nope, it is not.

And in Duke Nukem Forever they pushed this kind of stupidity to the max. I don’t want to talk about it. The amount of nudity and sex scenes, but more importantly, how these relate to the main characters is what makes the game terrible, as you portray all this as okay; not only that, but it clearly sends the signal that you don’t know where to draw the line between light joke and sexual assault.

My fundamental criticism is how it actively normalizes and celebrates harmful attitudes and behaviors like sexism, anti-intellectualism and regressive social views. The game starts by immersing the player in first-person sexual encounter, framing women as mere objects for pleasure.

Rather than thoughtful satire, DNF presents degrading and dangerous perspectives as acceptable through both its gameplay and writing. It reinforces backwards stereotypes around gender, intelligence and social norms without subverting them. Media reflect society, but also shape it.

Even if it intends to parody outdated mentalities, it takes no care to avoid making bigotry seem normal. As entertainment, it has a social responsibility which it ignores. We cannot dismiss or trivialize these issues by hiding behind humor. Creative works should not promote unethical principles then defend it as just a joke. DNF goes directly against this.

Hey, if you don’t know how to write stories, just don’t write any. We never needed one in the 90s. Aliens invade the Earth and Duke is the only one that can kill them all. Good story. There’s no more background needed. But now Duke is some sort of Batman guy, filthy rich, with duke-mobile and duke-plane.

My shock on DNF was exacerbated with how good Doom 2016 is, I just played it a few days ago. A very good tribute to the original, and I really cannot bend my mind on how they managed to pull it off. Modern, yet classic. DNF is neither modern or classic. It’s just plain bad.

I also went to have a look on the 2001 Duke Nukem Forever leak on Youtube, and the restoration project. After an hour of pain suffering from watching that, it become pretty clear that in 2001 they already were going for the same things and the same errors. The story seems the same, the plan was the same. How come no one saw the problem on this game idea for so many years?

The success for me of Duke Nukem 3D was on the maps, specially the first ones, where the battles were in a realistic setting, not only that, they were everyday places. The cinema, library, apartments. The narrow hallways and passages.

If you look the maps for DNF you’ll notice scaling issues that were common back then, the maps are much bigger than the characters, creating a lot more space and room to walk than what it should be in real life. This removes a lot of the charm. And Duke’s mansion? that has nothing to do with everyday places, which adds to the disconnect.

It’s too late. I was a kid when I first played Duke Nukem, and I’m losing interest over time on first person shooters. If Duke ever returns proper, it is likely it will be too late to get interest from any fans, as they will likely be moving on to different stuff. Different games, or even maybe something outside gaming entirely.

Like in Matrix. Too bad they never did a reinstallment of Duke Nukem 3D.

https://xkcd.com/566/

Modern music is defined by the Loudness Wars

Almost two years since I started attempting to make my own music, and one thing is become clearer over time: The sound of the 2000s and next decades has been shaped by the Loudness Wars.

It starts with the popularity of the CD. Having the availability to perfectly record everything digitally allowed producers to push the loudness as high as they want.

I’m no expert on this, just learning, so take this, as with everything else on this blog: opinion. You can disagree and I might be wrong. That’s fine. These are my thoughts as of today.

When I started creating my first tracks I didn’t think this through, I did not realize yet. I just published the audio as it came out of my Circuit Tracks after a normalization pass.

It became apparent pretty quickly that my tracks were nowhere near in loudness as any other track I was listening to normally. So I did some research.

Turns out this isn’t about increasing the volume of the track. And of course, because I was already normalizing them. The waveforms recorded had already peaks on the maximum values (-1.0, 1.0) or 0.0dB. Increasing the volume further will introduce clipping.

And that clipping very quickly introduces a harsh sound that isn’t pleasant. It changes how it sounds, adds distortion and it’s kind of horrible.

So this is not what everyone else is doing. Well, most admit to do some clipping, but it’s minor – they say that a small amount of clipping can’t be heard, but this is not how they push the sound to be that loud.

First we need to understand what is loudness really. Loudness represents of course how loud a sound or track is to us, but this has little to do with the peaks of the signal. Songs with peaks at -10dB can sound louder than songs with peaks at 0.0dB. For example, if we pick a commercial song that is mixed very loud and we reduce the amplitude so the peaks are at -10dB, versus a recording of a microphone of a voice or ambient that has been normalized, it is likely that the commercial song is still louder despite the fact that we reduced its volume by 10dB.

The first tool to analyze this is the RMS (Root Mean Square). This instead of measuring the peaks, it measures the area under the curve.

In the above image, I filled with yellow color the are under the curve. This is what RMS is measuring. This is effectively computing “sound pressure”. It is much closer to what perceived loudness is, but not exactly. There’s still one difference.

Our ears are more sensitive to high pitch, and not that much to lower pitches. This is shown in this graph:

And therefore LUFS was born. This is a new measure (well, quite old actually) that tracks perceived loudness. The way it works is, first an EQ is applied to the signal: a 4 dB high-shelf above about 2 kHz as well as a 12dB/oct high pass filter at 100 Hz.

This mimics the response curve of the average or the population. Once this is done, they take the RMS of the resulting signal. And that is LUFS, measured in dB.

So now the question has shifted from “how to make my song louder” to “how to make my song score higher in LUFS”. The answer is that the RMS has to be higher, specially from 100Hz and up. And sounds brighter than 2Khz get a 4dB bonus too.

And this already gives a potential reason on why music on CD or Digital has been sounding brighter than vinyl. Just EQ your song to focus on mid-highs, and you’ll get higher LUFS.

The other way to get higher LUFS is to make the RMS bigger. How? Well, compare:

As we can see the first one has a peak and goes down. The second one there’s a peak in the middle (that’s a hit of a new note) yet the signal almost does not decay at all.

Even if the first one has a higher peak, the second one has a higher RMS and loudness because there’s constant pressure at all times. In other words, a wall of sound that never goes quiet. This means that not only we are using the vertical space available, but the horizontal axis (time) as well.

There are plenty of different recipes to do this, I’ve been evolving mine over time. But first, let’s quickly talk about the last part that it is important here to increase loudness: Distortion.

This is a sine wave:

This has an area of 0.707 or the square root of 2.

And this is a square wave:

This has an area of 1.0.

So the square wave is louder than the sine wave. Also it sounds harsher.

But this also means that we can distort the waveform to get even louder. For example:

This is exaggerated. But I think it drives the point across. A distortion can add loudness into the mix. But it also high frequencies that make the sound harsh. However, LUFS also scores higher for high frequencies, so bonus points for loudness.

And turns out that a bit of distortion can help making two sounds blend together.

And finally there’s clipping. Not going to go into detail here because it gets very technical and long, but basically a clipped waveform actually goes above 1.0 when reproduced (because of Nyquist and how digital music is processed). So it is possible to add another 1dB of loudness this way. As it is a distortion, it adds harmonics as well and sounds harsher. 1dB might not be enough to be perceived though.

All these together is what allows producers to go to stupid values such as -2.0 LUFS.

But why? Because louder sounds better, that’s it. If you have two tracks and one sounds louder than the other, subconsciously the louder one sounds better. And I’m not talking that it sounds better because of what we did to the sound, I’m talking about just changing the volume knob or changing the normalization value.

Making a song louder results in that is also reproduced louder in radios, making it stand out from the others. Not sure if that’s still the case as of today, but now it has set the trend for 2 decades, maybe more, and it has created a distinctive sound of what the population considers “good music”. And the only way now is to follow it regardless of it is really better or not.

That’s how we defined the sound of the last decades, by sheer loudness.

Take almost anything, push the loudness past -9 LUFS and boom! it sounds modern.

Now with streaming services, they are normalizing at around -14 LUFS, so it doesn’t matter that much anymore. One could choose to publish a less loud master and it should effectively sound as loud as the others.

But the reality is that we already have set a sound, a desire for constant sound pressure, that makes us prefer the color of the sound created by pushing the music to be as loud as possible.

I’m unsure if it’s really “better” or only collectively better from what the society has been considering as good for the past two decades. Maybe it’s really better. I don’t know. But it can be an effect like 24fps and anamorphic in the cinema, where we associated these as good cinematographic and the 60fps and regular lenses to amateur. There’s also a lot of discussion and people giving good reasons why 24fps might be better.

Most probably this is something we still need to explore in the music side of things too.

As I’m only learning and I don’t know that much, let me link here a video from Dan Worral who has a lot of experience:

One thing that Dan seems to miss, and to be honest, most people talking about this too, is that the push for loudness gives the music a certain color or aesthetic.

As soon as I began producing using Ardour, I started looking into loudness of my songs, and pushing them to -14 LUFS. That’s why I created the track “How Loud is Loud”, basically a protest against this.

What I found is that I needed a lot of pads, and ways to fill the song with sound. Then I needed quite a lot of trickery to get to -14 LUFS. Sometimes with compressors and limiters in the verge of “pumping” – other times that escaped my checks and the pumping effect got released.

And a lot of people will say “whoa trouble to get to ONLY -14? what kind of noob are you?”. Yes, noob. I started recently. But also, most people just use a few plugins that do this for them, and they have little idea on how to do it by themselves. It is also done by specifically controlling the waveform from each track, and lots of them are directly using audio tracks. That’s their choice, and a fine one, but my choice is to stick with full MIDI as much as possible.

The problem to get a song louder seems more a lack of tools than lack of knowledge. As soon as I got into Bitwig I found out that I could hit higher than -14 LUFS pretty easy.

And the way I’m creating songs is changing as well.

Before, I had to add strings and pads to fill the gaps. Now what is happening is that the tail of each sound is being retained loud, pushing up the reverb, releases, and other effects up whenever there’s room for them.

The result is that my songs are getting simpler over time as I master this technique. I almost don’t need pads or strings. A single bass doing a rhythm is enough to fill. I started mastering at -9 LUFS confidently, and my last tracks show this.

However I found out that Youtube Music for some reason seems to be doing up to -7 LUFS. Yet again my songs are less loud than the surrounding ones. So, mastering to -7 LUFS it is.

A lot of people (most of them probably) are using sidechain compression, ducking sounds when other sounds play. I tried this before, and I find it finicky, and a lot of stuff to tune.

As a new artist/producer/composer, I still mix a lot of ideas, I don’t have a clear concept of what I want to do, and my tracks tend to get quite a bit chaotic. This is something that I’d like to improve over time. It also doesn’t help that I want to stay away from the common ways of composing and producing, trying to find the path and my sound in my own.

All of that makes the songs complicated, with a lot of changes over time, and I might end needing to change and tweak effects lots of times to correct and fix stuff. That’s why I don’t like sidechaining or automations. I just want to place some notes and I want these to sound good and loud. That’s it.

What I’m doing lately is to mix quiet, with lots of headroom, then fix everything in the master track with basically 3 steps:

First, compressing the signal with infinite ratio so it tends to normalize to a particular LUFS level of choice (in the Src FX there’s an EQ that emulates the LUFS EQ curve). With very soft attack and release, so it preserves big part of the dynamics. Also preserves and enhances some of the transients. This is a bit in reverse from what others are doing.

Then I distort it. The idea is that the distortion mimics a transistor amp, squashing the transients and adding some color to them. We got used to how the sound and harmonics of pushing a system too loud sounds like, so this will mimic this behavior while squashing the transients.

Finally I add a limiting step, as usual. This is also a bit aggressive to try to push the tail of the signal a bit louder.

This might be suboptimal, but does what I need for now: it gives me the sound and color of loud music without the need to tinker individual tracks that much. Of course, each track needs to have its gain properly set, but aside of that, it is quite hassle free.

This setup has one big caveat. When the song is at total silence for a while, which would be the start of the song, the initial amplification is way too much, so the first second of the song might contain a glitch. And in a recent track this happened to me without noticing. Because of this I need to start the song with a soft sound that has a long attack.

Depending on the settings and the level of target loudness, it might get a pumping effect or a growl/distortion. Some instruments might react badly with this setup in aggressive configurations.

But well, limitations. That’s one thing I learned, limitations is what shapes your work.

The other way of working has its own limitations, i.e. you have to have a clearer idea from the start, you can’t do something really complicated, you can’t mess around that much. That gives another kind of result.

And the song that got this mistake of the glitch on the first second is “Halloween Ghost Rave”, which sucks because it seems that is the best one that I did so far and I missed this problem for days after release.

Well, another mistake to learn from.

Oh, one thing I missed from explaining: As I noted, I’m not using sidechaining most of the time. Master effects do this for me. How? Simple, all instruments have tails that are long but quiet (effects, reverb, etc). Because these master effects are pushing the tail of the sounds so much up, you’ll hear these if nothing is playing. If one instrument plays anything, it will be much louder than all the other tails combined, and the distortion will basically hide the tails and enhance the top instrument. This allows me to master the track at the MIDI level, instead of doing this at the waveform level, which is much more time consuming. It also allows me to compose everything from the start knowing how it sounds mastered, instead of composing first and then mastering to get a good sound.

It’s a far cry from perfect. But it is what I need now to keep going. As I get better and find what is my sound, how to get it, what does it look like, it is very possible that I’ll go for mastering techniques that are better but more time consuming, since I’ll have less problems trying to arrange the composition.

And as a conclusion, the seek for loudness is no longer because it actually sounds loud, but because it gives certain color that… have to admit, I do like.

The Backrooms: better games, please!

If you don’t know what are The Backrooms, or if you think they’re a part of a nightclub, let me introduce you quickly to the internet creepy-pasta.

If you’re not careful and you noclip out of reality in the wrong areas, you’ll end up in the Backrooms, where it’s nothing but the stink of old moist carpet, the madness of mono-yellow, the endless background noise of fluorescent lights at maximum hum-buzz, and approximately six hundred million square miles of randomly segmented empty rooms to be trapped in
God save you if you hear something wandering around nearby, because it sure as hell has heard you

 Anonymous, 4chan (May 13, 2019)

The image and the citation was extracted from Wikipedia. There is a lot of lore and stuff that people made up to continue on this concept, different levels, etc. But this does not interest me that much, since it feels quite random. They have their own canon of how this is supposed to be.

But then Kane Parsons in his YouTube Kane Pixels did his own adaptation which broke all previous rules, creating his own world and story using just the concept. And I like that world much more, I find it much more intriguing.

And finally we come to the games. While Kane is still finishing his project, others are creating games to try to convey these feelings of uneasiness and being lost in game format. And here is where really my interest spiked.

I discovered The Backrooms while following my favorite Twitch streamers that do horror games, and it got me curious. It’s not that trivial as a field of monsters, it is more subtle. It’s more psychological than anything else.

Then I came across “The Complex: Expedition”.

This is a new game that is close to Kane’s interpretation and it’s main focus is on trying to get realistic graphics, to the point that you may believe the game play is actual footage.

I had to play it. So I did last night. It is good, it got me with this eerie feeling, uneasy. The problem is that I was expecting to get lost, and I tried common algorithms for path solving. Simply tracking in my mind which direction was I facing originally, in which direction I’ve been waling the most lately, which way the map is “extending”. Or following the right wall.

The disappointment was that I could finish the game with 54 minutes in Steam, and that accounts for Proton installs and some tries, setting up some options, etc. In a first run, still having to learn the controls. I swiftly skipped most rooms and content as if I knew where the exit of each level was beforehand.

Don’t get me wrong, I plan to play it again and explore a bit more. But the problem is that you’re supposed to be lost and it’s clear that I knew what the correct direction was most of the time.

Also, there’s too much stuff that tells me that this is just CGI, and it should be trivial to fix. The walls look like boxes. Most of the time the walls have a common thickness (as if all this was made in a 1m x 1m grid), the walls also lack decorations to finish the ceiling and floor side, which makes it obvious that it is just a polygon rendered there.

However, there are other problems that come from the original creepy-pasta. The elevators that move you to the next level, the train… these are too common concepts in games to put a level to an end that it is reassuring, not eerie, not confusing, not unsettling.

Also the different levels or visuals are just too clean and distinct. While on the yellow muddy wall section, there are just slight differences on color, but then the other rooms are just too different. It gives you a sensation of completing, advancing, which is not what we want in this concept.

This creepy-pasta needs to be refined, a lot. It has potential, but yet it is still very rough.

In this particular game (The Complex: Expedition) there’s a notable lack of positional sound effects. It just feels empty. There’s nothing going on. Why, if I stop to listen, every single time I hear nothing. Maybe there’s some parts with some music, yeah. But that’s it.

And the monster… I think this is one of the biggest problems. The original 4chan post mentions that you’re not alone, that there is something that will kill you. But the inclusion of an actual monster that can be seen is a problem though.

Monsters or entities of the backrooms are needed to make the player run for their lives, but they need to be way more subtle than an actual monster. Relying on sound effects and lighting is gonna be much more effective than seeing an actual entity. And if an entity has to be seen, making it such a way that lacks form or it seems to shift the form would also be good.

If anyone is making a new Backrooms game and wants some ideas to stand out (even if it makes the game too hard), here are mine:

• Ensure most walls have terminations for the ceiling and floors.
• Avoid clues that help the player count 90 degree turns. Don’t make the walls at 90º exactly. If the ceiling or the floor have square features, try to make them crooked or distorted in different ways.
• Add positional sound effects that could be produced by a human or an entity, like knocking, steps, crying, voices, etc.
• Don’t let the player get close and identify the positional source of a sound. If the player gets too close, either disable that sound or move it to a different position. This could be used to get the player to pursue a sound again and again and get very lost.
• Punish the player for getting too close to a sound source.
• When laying out a labyrinth, make sure the exit of the level is not just in the opposite direction of the start.
• Use the third dimension as much as possible, hallways or parts slightly going up or down could allow for more intricate weaving patterns for the labyrinth. Making the ceiling lower might give more claustrofobia, but also makes a slight ramp enough to get to a upper or lower level without the player noticing.

But overall I think the shape of the maps or how the maze is laid out on most games is quite simple. You start from one area, you discover another, and from there you continue… until you finish the game. It is pretty lineal.

We can consider a maze 2D if height does not matter; if you could flatten the map into a sheet of paper and there is no or very little overlap. As long as the maze is 2D, it is pretty straightforward to figure out. One way to make these a bit more challenging is the typical key-lock trope, which might not involve a key and a lock, but overall an item that needs to be found to access other area, or to be able to do something different.

But the most important thing to enhance the feeling of getting lost is to use more dimensions. As I wrote above, making the ceiling shorter, allows for moving the player up and down to a different floor without being that obvious.

Another strategy that might work is having a small room with a door, that from the outside still looks the typical yellow paper and wooden door, but when entering, inside it might look like part of a house, with stairs going up. Place maybe two dorms and from one of them there could be another door leading to outside, which would be a floor up.

Elevators are currently the common way of moving between levels. The advantage is that they can skip many floors. The problem is that most times these are implemented linearly – you enter and it moves to a different place, which it is always the right place. And you cannot drive the elevator back.

I think an elevator could be very powerful if it had a floor selector and it could be used back and forth. Imagine an elevator where most of the buttons didn’t work, and the elevator did not send you to the floor that was actually printed in the button. Also floor numbers might not make any sense at all and be just random.

From here, I think we exhausted what can be done with full use of the three dimensions. But we can go further! What about time?

Some of the Backrooms lore suggest interaction with time. Of course, being able to put the future into the past is not something that can be done in a game. That does work in films, but in games unless it is some kind of cinematic or pre-recorded event it is impossible. But there are other ways that time can interact with the game.

The first idea that comes to mind isn’t that good or to my liking: paths and hallways shifting over time. While this sounds enticing, not only it is hard to implement, but also it has caveats. If it shifts shape over time, it means that the player could be able to see the shift happening directly. And this removes a lot of the mysticism of the Backrooms. Also we don’t see any sign of this happening on any of the current takes of it.

But another way to incorporate time is to have some doors lock and unlock from time to time – this shouldn’t be blocking anyone from an area, but let’s say there are 3 doors in different places that connect two different areas. It would be interesting if one of them is locked, and which one is locked changes over time. So it throws people off when going back.

This can be done in elevators too, which buttons are working might slightly change over time. Also this shouldn’t be abused to just enable the right button only for minor times, and as with the doors, it shouldn’t gate areas. Maybe there are different elevators with different working buttons at each time, and there are also stairs.

The point of this is making a huge interwoven space with a huge amount of floors and spaces that can be navigated freely. There should be multiple paths in and out of most of the things, and there should not be a continuous linear part at any point.

I’ve noticed that these games try to guide the player, some small cues such as lighting, type of obstacles and changes in scenery are placed to tell the player that they’re going in the right path. And going in the wrong path usually either terminates quickly or circles around in a small radius. We need to think bigger, way bigger. We shouldn’t be afraid of making the characters move fast, so the player doesn’t get bored; in compensation they will get very, very lost.

Imagine a path that circles around but it is so long that by the time you return to a known area, you don’t really recall it.

Thinking more about layout changes, while I don’t like walls moving over time, one potential solution would be doors appearing and disappearing. That could be subtle enough and if spotted it would not look that bad.

If walls or part of the maze changes, maybe it is very slow or freezes when the player is around.

But with all those options explored, we can also think about a fourth dimension. Non-euclidean space is one of my favorites, but this is really complicated to do. But there are lots of tricks that one can do that can emulate some weird space bending.

One is for example doing perspective warping. Imagine the hallways elongating and everything looks further away from where it really is. Or imagine if everything curved downwards, as if you were standing on top of a ball. Or maybe it continuosly curves to the left.

These could help a lot on preventing the player from noticing if he’s actually going up or down, if he’s turning 90 degrees left or not. These effects could be subtle and activated gradually in zones.

The next level of deception and 4D-ness are “portals”. Regions of space that initially are apart and the portals join them together. Nowadays it seems pretty clear how to do this in a game with a second camera rendering.

With that, one could connect different parts of the map together seamlessly, in a way that doesn’t feel like the player is traversing a portal at all. Which would make it extra confusing since it would break all rules of how space is connected.

Portals don’t even need to be in the same orientation, meaning that the entry might be facing North and the exit could be South or West. That also puts off the player’s sense of direction.

But that’s not enough for me! not hard enough. There’s still another degree of freedom that can be added: mirroring. When passing through a particular portal, the whole level could be mirrored. This seems minor, but it can have massive disorienting effects.

Anyway, I would love to have games that exploit these, to get truly lost. To spend time trying to understand how the space works.

But so far the games I’ve seen are rather simplistic in this way.

Hope I can find someday a gem, regardless of it being a Backrooms interpretation or not.

Cierra Sedice.com & Leelibros.com

Me duele en el alma escribir esto y es uno de los motivos por los cuales no ha habido ningún anuncio ni comunicación por mi parte en los últimos meses. Sedice y Leelibros dejaron de funcionar cuando el servidor cerró. Prometí en su momento que lo migraría, pero nunca ocurrió.

Descargué el backup y contraté nuevo servidor, y moví los datos allí. Pero pasar de este punto a tener el servidor funcional, se me ha hecho muy cuesta arriba. Lo que pensé que haría es muy distinto de lo que al final hice (o lo que no hice en este caso).

Con el paso del tiempo cada vez he tenido menos energía para dedicarle a Sedice, y la poca actividad que había confirmaba mi poca motivación. Al final, y después de tanto tiempo, creo que lo más justo es aceptar que no voy a ponerme a ello.

Por lo tanto, aquí anuncio que Sedice.com y Leelibros.com han cerrado y no van a volver.

Lo único para lo que me quedan energías para hacer es intentar mantener la última copia de los datos segura y seguir pagando los dominios durante los próximos años. Por si acaso. Pero no espero que después de tanto tiempo, si recuperara lo que hay, que ningún usuario vuelva. Quedaban pocos y después de un parón tan largo, dudo que vuelva ninguno.

Hay poco que se pueda hacer en estos momentos. Los backups contienen una barbaridad de datos privados y no sería de recibo darle la copia a alguien que potencialmente podría explotar esos datos (contraseñas, direcciones de correo, mensajes privados, foros privados); por lo tanto el proyecto muere aquí, ya que todas las personas de confianza que habían no están interesadas en Sedice desde hace muchos años.

Supongo que la mayoría, si no todos, sabéis de sobra que mi interés en Sedice y Leelibros siempre estuvo en el punto de vista técnico de llevar la plataforma, no tanto en su contenido (apenas leo novelas de ficción). Sin embargo el interés técnico me hizo mantener Sedice durante muchísimos años, y se hubiese cerrado cuando la Asociación Cultural Sedice cerró.

El problema raíz es la cantidad de horas que son necesarias para levantar Sedice y Leelibros en un servidor nuevo, especialmente cuando llevo 5 años o más sin tocar el cómo funciona por dentro. Sedice tiene una plataforma que es tremendamente antigua y ajustada a medida, lo que hace que reconfigurar todo desde cero lleve mucho tiempo. Es más, hay mucho código que no funciona en servidores nuevos.

Como comprenderéis, a estas alturas no tengo ganas de perder aproximadamente cuatro días enteros a arreglar todo el tinglado. A meter Sedice en un Docker por ejemplo. O a testear y corregir todo el código para que funcione en un PHP que sea medio decente.

Y MySQL, esa es otra cosa que no quiero tocar. El backup se hizo mientras la base de datos estaba en marcha, por lo que es altamente probable que MySQL no arranque. Esto quiere decir que tendría que hacer una reinstalación de cero y usar los backups en SQL para ir recuperando toda la estructura. Uf… muy pocas ganas sólo de pensarlo.

Los donativos, hace mucho que no se recibe prácticamente nada (3 donativos en 2022). Aunque el pago del servidor siempre ha sido lo de menos – no cuesta tanto dinero y el trabajo actual que tengo hace que sea un gasto muy pequeño.

En fin. Me fastidia que termine así, pero es lo que hay. Y disculpas otra vez en el retraso en anunciar algo que debería haber hecho en el momento que el proveedor original me avisó (realmente les tuve que preguntar el porqué no me aceptaban el pago). Fui incapaz de ver que no iba a meterme otra vez a dedicarle el tiempo necesario.

Para los que quieran seguir con la comunidad de Sedice lo que os puedo recomendar es que os miréis Reddit, Lemmy y KBin. Aunque son principalmente en inglés, se pueden crear comunidades en español y podréis encontrar a gente de habla hispana por allá que ya los usa.

Y ya está. Esto es todo. Espero que estéis todos bien y sigáis disfrutando de vuestras lecturas y participando en otras comunidades literarias. Agradezco el apoyo y la confianza que depositaron en Sedice y Leelibros a lo largo de los años, y lamento profundamente no haber podido cumplir con la promesa de migrar los datos y mantener los sitios funcionando.

Gracias por todo y un saludo a todos.

Could AI have consciousness and self-awareness?

I’m back to rambling…. yet again. Dear reader, before I go in to the meat of this article let me warn you: This blog is just my personal thoughts, I often like to talk about stuff I don’t really comprehend, I like to oversimplify stuff, and I never talk about anything that I might have any insider knowledge. So take all my opinions with a pinch of salt. Or two.

With that said, let’s begin. I laugh hard when someone says that our current AI technology is conscious or self-aware (to be clear, mid-2023 AI tech or earlier). But I also see that a lot of opinions saying that it cannot happen, that we cannot build a thing that is self-aware and has consciousness.

And to be clear, I’m not talking about AGI. I believe you can get AGI or better (super-human intelligence and reasoning) without any hints of self-awareness or consciousness. Though related, I believe each sits on their own axis. A conscious AI could be bad at reasoning or adapting, and an AGI could lack consciousness.

To compare, we do have a lot of animals that most likely do have self-awareness and are conscious. But they are unable to perform 99.9% of the tasks that we humans do. On the other hand, we have very complex programs that are way better than humans on some specific tasks. This is just an example on why I think that good reasoning skills are not needed for consciousness, and consciousness is not needed for good reasoning.

So, whether we can build an Artificial General Intelligence, or if we can build a conscious AI are two different questions. For AGI, in my last post I already expressed my opinion: It’s about to be created. I really doubt that it would take 10 years to get the first one. For consciousness in AI, I think it probably won’t happen, but not because we can’t but because we are not interested on it. Unless it turns out that is one of the ways to get to AGI and we accidentally hit that path without being aware of it.

Let’s backtrack a bit and go back to the basics. Humans are not special. We are not special. There’s a lot we don’t know about how our brain and bodies actually work, and that adds a lot of mysticism. But that does not make us special.

We all agree that humans are self-aware and have consciousness, but we don’t know what enables that. But also we don’t know either how to test for any of that reliably. For animals there is at least a test for self-awareness using a mirror and painting a dot on their forehead. Still, if a creature doesn’t pass the test that does not mean it is not self-aware – it is still possible that they react or think in a different way that does not make the test work. But even then, do they have a consciousness?

So, here’s how my logic plays out. If I ask you if a human has consciousness I am 99.99% sure you’ll answer yes. But then I can ask again for gorillas that learned sign language, then monkeys, dolphins, octopus, dogs, birds, insects, plants, unicellular organisms… And I am almost guaranteed that you’ll go from being okay with accepting that the creature in question has consciousness to outright denying it as impossible.

This means that either at some point some evolution made a creature conscious, or consciousness is a scale of greys not black and white, and evolution slowly made some creatures more and more conscious until it gets to us. And this still assumes that humans are the most self-aware and have the most consciousness in this planet, which might be wrong, we don’t know, but it also doesn’t matter.

The next step of my reasoning requires that physics can be computed. That our universe is either just math, or math is enough to compute it (given enough resources). Therefore what happens on a brain (human or not) can be reduced to interactions between chemicals and electrical signals. More or less. If you completely disagree with this, that would mean that you believe that there is something we have that cannot be computed, i.e. a soul or something that serves the same function.

I’m not gonna laugh at anyone that believes in a soul or anything similar that cannot be computed – I am not 100% sure either if everything can be just boiled down to quantum physics, string theory or whatever. There is a possibility that we might be missing something.

But if we assume that everything can be computed, then this means that evolution randomly added different “computations” to different species as they evolved, and by chance, consciousness and self-awareness appeared.

A computer (which does computations) can therefore be given both self-awareness and consciousness if the right instructions are given, because at the very least it could simulate a human brain atom by atom, one plank time unit at a time. Maybe such a program is way too large for our current technology, or for the technology we will have in 100 years – who knows. But the point that I’m trying to make here is that a computer brain is indeed capable of having self-awareness and consciousness under the right conditions.

Our brains do not use infinite resources or computations, therefore we should not need an infinitely big computer or infinite time resources to run an artificial conscious being. Unless… some quantum stuff plays a crucial role in that computation and then we would require a quantum computer for it – but we are creating quantum computers anyway.

All of this, assumes that everything happening in our brain can be computed. As stated earlier, this may or may not be the case. I’m certainly inclined to think that yes, everything can be computed. But I’m not 100% confident on this.

Are our current AI conscious?

I’m very sure that the answer is no, at least up to what we have today.

There are tons of explanations from other people that talk about how the current LLM like ChatGPT are only glorified statistics that find the next word (token) , but I think that there’s a better underlying reason on why they cannot have consciousness.

These systems don’t have the capacity to change their internal state.

The way they work currently is just a function with an input and an output. The function itself DOES NOT mutate when executed, except during training. Therefore it cannot “experience” anything.

So, even if we made the case that the current AI have what they need to be like us, turns out that in every time we ask them anything we are feeding them exactly what they will “remember”, execute and get the output text from them. Then they are reset and on the next time we will pass another set of things “to remember”.

To put an equivalent of what this is for humans, imagine a human brain, frozen in a jar. Every time you want an answer to something we just feed the right electric signals and get the output ones. The brain is frozen and not allowed to change its internal state or wiring. Any electric charge is removed between questions.

Yes, it is a very disgusting and frightening scenario. But then, would that brain have consciousness? If done correctly, the answer is no. This brain does not have experiences, does not learn, it does not have any chance to rewire itself.

And this is what is happening to the current AI models being deployed. They do not rewire themselves when experiencing any interaction. Our current models are not alive. They are just computers, executing a series of complex math.

I don’t think that our current AI has enough capabilities to become sentient even if given the chance, but if they remain “plugged” in the way we do, I really doubt that any super intelligent AI can really become self-aware. We can probably teach them a concept of self, but I don’t believe they can really grasp it and act on it.

For that, I think that at least a few things need to change on how they are deployed:

• They need to perform “training steps” as they execute normally. This means that they should be able to rewire themselves from the experiences and interactions, making each AI grow and learn over real-world data, not in controlled training batches as we do.
• They need to be able to have authority to execute continuously and decide by themselves when to do/act or when to rest.
• They should be able to talk to themselves, to have an inner thought.

Maybe there are a few more that I missed. But these can be good starting points.

But who would want to deploy something like that? Seriously. Even if they become cheap enough to run continuously and train while inferring, who would want that?

To me, the above points sound like a recipe for disaster. Giving them autonomy and authority makes them very easy to spin out of control with no apparent benefit for any human.

I should point out that Auto-GPT fulfills two of the points above. Maybe it is not that far fetched the thought that someone wants to experiment with a fully autonomous AI. And it might gain some degree of self-awareness and consciousness – no idea – but I don’t see it impossible.

Maybe a more reckless version of Auto-GPT combined with an AGI is the recipe. And this for me sounds like pandora’s box, and we should not go this way.

The risks

AGI without Auto-GPT already poses a lot of risks. But the potential benefits do outweigh the risks if we can ensure safe deployments. If we deploy it with full autonomy it can go south very quickly. At that point no one will be caring anymore if it is sentient or not.

The risks of AI are not inherent to the AI being good or bad but on how is it deployed. The technology is quite accessible as of today if you have money for it. Meaning that we require way less skilled people to deploy these and they can make mistakes that may be obvious to experts. There may be dubious applications of AI where it sits unsupervised and a human should have been in the loop.

No one really knows how exactly AI works, these systems are most of the time black boxes. But don’t get fooled, this is not the problem. The problem is that the actual people that will deploy these lack expertise and have wrong assumptions about these systems.

For example, some people think of AI as computer programs, and they think that if they ask the AI for a task and on a few examples it performed well, it will do well all the time and perform that task. But this isn’t true.

You can ask it to summarize a text, and for some inputs it can do something else – either continue the original input, create a definition of what is a summary or something else.

AI is also prone to “cheating”. If it is easier to cheat the system to get the goals, it will do so. Instead of performing the task you wanted, it can do something totally unrelated that will score.

It also sees patterns and just follows them. If in a test all answers so far have been “A”, it is likely that it will attempt to answer “A” to all of them. There are also lots of biases, both on training data, and also learned from the task itself.

There are far more ways that AI can behave unexpectedly, I’m no expert.

Because of this, the U.S. and Europe are pushing for AI regulations to limit and control the application of AI. This will harm innovation and growth in AI, and also will put in an advantage countries that don’t have it. On the other hand these are necessary to prevent companies from deploying something that can be harmful for society.

I’m happy to see that there are people voicing their concerns and legislators are listening. I have really no idea how to solve this, therefore I have no opinion on the matter. Well, with one exception: I really hope they do not cut access for people to AI, because I’d love to continue playing with it and learning from it.

Can context windows create consciousness?

Here’s a wild thought: if a Deep Neural Network of infinite size can be thought as being Touring Complete (it can compute any program), then it can also run a Python interpreter from the source code given in the context window.

Of course LLMs are not infinite in size, but that means that it cannot execute a program that requires a lot of steps, just a limited amount. So in practice it cannot run all programs, just a small subset.

But this points at the possibility that the context window can be used to “reprogram” the AI itself, meaning that it’s short-term memory also makes part of what it is, what it knows and what it can do.

If this thought is somewhat correct (I have no idea), this could mean that a large context window and good attention layers would be all we need for ticking the box on my first point above:

<They need to perform “training steps” as they execute normally. This means that they should be able to rewire themselves from the experiences and interactions, making each AI grow and learn over real-world data, not in controlled training batches as we do.>

So maybe, and just maybe, an AGI with a large context window running on some kind of Auto-GPT would be enough to trigger some degree self-awareness. It is very unclear to me how conscious such thing it would be, my guess is that at best it would barely be if at all.

But I wouldn’t discard this entirely, since in computing everything can be reduced into inputs, outputs and a state. And the context window can effectively fulfill the function of a state. To compare this with an actual human brain, the context window seems to resemble our short term memory, while the actual fine-tuning seems to resemble our long-term memory. Our consciousness to me seems to require more of a short-term memory than a long-term one.

To be clear, this part to me sounds very far fetched, and I feel that somehow it must be incorrect. But I lack the knowledge to find the correct counter-argument on why it is wrong.

A long discussion with ChatGPT

To be clear, nothing written here was copied or extracted from any LLM. But after I wrote the first draft I went to ChatGPT and discussed a broad set of topics regarding consciousness and what are we missing.

My conversation as focused on getting a better understanding on two topics:

• The thalamocortical system, as I saw a paper that presented this as one of the main points on why AI cannot have consciousness
• Causal Reasoning, as after some conversation I realized that the AI might be lacking this concept of “If I do this, that would happen”, which may be critical for self-awareness.

The thalamocortical system

As I lack so much knowledge on this area, I think it is best just to cite what ChatGPT told me here:

The thalamocortical system refers to the complex network of connections between the thalamus and the cerebral cortex in the brain. It plays a crucial role in relaying sensory information from the peripheral sensory organs (such as the eyes, ears, and skin) to the cerebral cortex, which is responsible for higher-order processing, perception, and conscious awareness.

The thalamus acts as a relay station or gateway for sensory information. It receives sensory input from various sensory pathways and then sends it to the corresponding regions of the cerebral cortex. Different thalamic nuclei are specialized in processing different types of sensory information, such as visual, auditory, somatosensory, and so on.

The information transmitted through the thalamocortical system is crucial for generating conscious awareness. Conscious awareness refers to our subjective experience of the world and the ability to perceive and interact with our environment consciously. The thalamus filters and selects sensory information based on its relevance and significance, allowing only a subset of sensory signals to reach the cerebral cortex for further processing.

– ChatGPT when I asked about the thalamocortical system.

The conversation went for very long, so after lots of tries I managed to get ChatGPT to agree on a very simplified, potentially wrong comparison of this to the AI that is somewhat useful:

The thalamocortical system receives information from different senses, meaning it is multimodal. Therefore it makes little sense to incorporate something similar to an AI that is not multimodal.

It also specializes on filtering out unwanted noise (gating), removing a lot of information that is not needed. For example, our brains do not operate directly with “pixel data” from our retinas, nor with Fourier transforms of sound. It uses more abstract representations, which sometimes might miss some important details.

It is a system that makes sense only for a “roaming agent”, because being exposed to an environment makes the agent to be exposed to huge amount of information of which very little is relevant. Other AI that are just Q&A or request->response do not benefit from this, and probably doesn’t make sense to include any equivalent system since it could drop data that we want it to use.

Also it resembles to what an autoencoder does. Collects raw information and encodes it with as little information as possible in order to reconstruct later. But multimodal; which also possibly joins all modalities into a single output space.

When I joked about a “multimodal autoencoder” ChatGPT -as usual- surprised me when it told me that it is actually one of the many ways it is being purspued a multimodal AI. I double checked on Google, and found papers on this topic. There were many other fields of research that are related to multimodal AI but in a different path. It doesn’t matter much, really.

The important point is that if such system is required for consciousness, it means that the AI needs to be multimodal, and it needs to have the right architecture to be a free roaming agent in an environment. The other thing that’s important here is that research is underway in so many related areas here, so even if it is required, humanity is already figuring it out.

This doesn’t look like it will be a hard blocker to get a digital consciousness.

Causal reasoning

As before, I think it will be better to let ChatGPT to introduce this topic – it does a better job than I can do:

Humans possess the cognitive ability to reason about the consequences of performing certain actions based on their understanding of how the world works. They can mentally simulate or predict the likely outcomes of different scenarios and make decisions based on these anticipated consequences.

This type of reasoning allows individuals to consider the potential effects of their actions before taking them, and it helps guide decision-making and planning. It involves thinking in terms of “if this happens, then that will happen” and reasoning about the causal links between events.

In the field of artificial intelligence, there is ongoing research and development to build AI systems that can simulate similar causal reasoning abilities

– ChatGPT when asked about Causal reasoning and AI

My questioning was about why our current LLM do not have causal reasoning. Currently GPT3 and GPT4 show, sometimes, some reasoning capabilities. And reasoning is not causal reasoning, but where’s the difference?

I walked ChatGPT over a thought scenario of using GPT4 plus Tree of Thoughts and something similar as Auto-GPT to allow the agent to think, reason and backtrack when needed.

Here, it did come up with a lot of different topics on why current AIs don’t have causal reasoning. Most of them focused on being able to try by itself, and being smarter on understanding the underlying causes and hidden variables.

I asked about Reinforcement Learning, as it seemed similar or related. ChatGPT pointed out that a variant of it, called Causal RL (confirmed via Google that it exists) is in active development.

And I have to admit, the conversation was already too much for me to grasp. I will have to go and read quite a bit on the topic, but right now it is too much for my current knowledge.

However, it does feel to me that causal reasoning could play a critical role in creating a digital consciousness. And also, it is being researched at the moment, so it is not impossible that we can create it.

Wrapping up

I do not think that even an AGI will be self-aware or have consciousness on regular deployments. And to reiterate, as of today not a single AI is minimally self-aware.  However, I see feasible that an experiment will be able to create such things in the future, although I don’t think it would be deployed exposed to the regular public. I don’t see any particular advantage of such AI that a regular AGI deployment would not give, and the additional risks are too high.

There’s also a small chance that we will accidentally create it without noticing. I’m unsure what would be the impact of this, or even if found in an experiment.

What is most likely is that any evidence will be dismissed until it is undeniable. Because a lot of people just don’t see possible at all and will make an assortment of arguments to justify their positions.

And I expect the same people to be in the comments section of this article jumping to tell me how wrong I am.

An Algorithm for Truth might be possible

I’ve been really busy lately and haven’t written almost anything here, but I’m doing well. Anyway, I wanted to ramble around the topic of an algorithm for truth as I got some thoughts that want to get out.

An Algorithm for Truth might mean different things, so just to clarify, I’m referring to the use Tom Scott makes here:

It is a really good video and I recommend watching. The idea is basically the following: Given the amount of information that Internet holds, the easiness to create blog articles like this one, comments or videos, it is increasingly difficult to set apart what is real and what is just made up.

Platforms such as Twitter and YouTube (just to name two examples) have the problem of misinformation. Ideally, those platforms would like to flag such content and prevent its dissemination. We might not be talking here about blocking or taking down content, but at least to flag it so users can be aware of what they are reading might have some problems.

But this is almost impossible. On one hand, a lot of topics are really subjective. On the other hand, this is a task that requires a human with a lot of time, and getting enough people to review everything will just be economically unfeasible. Humans are biased and also could take down or flag stuff incorrectly just because of their beliefs.

Also, these platforms want to optimize what they show you. They want to show stuff that you will engage into, that you’ll watch, comment, etc. Therefore they place algorithms that will attempt to predict this and try to increase the amount of time you spend on the site.

However this has very ill effects. For example, content that is outraging will usually gain more traction. Also content that people want to believe true. This happened way before Internet, but now it is on a bigger scale. The effect is that what you get can be a bubble of misinformation that feeds itself, where the actual reason and truth is just hidden because no one wants to hear it. And this can cause a society that is rotten, and also rotten the service itself.

Something that happened in YouTube, at least in the past (haven’t checked this in the last years) is that if you keep clicking from a video to the next recommendation over and over, the results were more and more radicalized, until you get to a point that it was ridiculous.

There are ways to mitigate this with our current tools, and I believe that they have been attempting this. But it is nonetheless problematic. There’s also the problem of positive feedback loops or fame-driven fame: You don’t know if a video would perform well if you don’t show it to people, but you wouldn’t show it to people if it hasn’t performed well in the past. When a video is new, what it counts is how many subscribers you have or how many people know you. The same is true for Twitter. So you might have a once-off video or tweet that might be the best of the world and be forever forgotten because you’re not famous. On the other hand, people that got the fame can just write “coffeve” and get trillion likes.

All this happens because there’s no algorithm for truth. Even if we can scan a video, get the voice translation or in the case of a tweet, process the contents, there’s no way to know if this is interesting just by its content, if it is fair, or if it is misinformation.

Consider the case of COVID-19 restrictions/bans we had: at the beginning YouTube was restricting the views that mentioned the issue at all because it had no way to know if the information on the video made sense or not. Later on both services just flagged the content and it was up to the viewer. Still, no difference was made on the actual content: you said the word, you got flagged. Sometimes to a ridiculous amount where it was just a reference to the current situation and not a discussion around it.

But we don’t have a way to know this via an algorithm, and that means that either everything is flagged, or everything potential to be flagged needed human review, which is unfeasible. Other approach was to whitelist certain official media, but this is the old “famous people get different rules applied”.

Now with AI like ChatGPT and others that can do video and audio generation it becomes a risk that some groups could flood misinformation that would look credible to the untrained eye. This can be used to do political campaigns to lower the trust on some candidate, or to deep fake relevant people to use them as authority.

With all this, the old saying of “don’t trust anything on the Internet” becomes more true than ever.

If only we had an Algorithm for Truth…

Language models (AI) might have the clue here

Let’s imagine for a second – just a thought experiment – that language models such as GPT4 have a good capacity for reasoning and apply logic to a very big extent.

Reasoning and intelligence are very different from wisdom and knowledge, we should not confuse these two. Someone with a lot of knowledge but low intelligence can be a living encyclopedia, but it would have hard time applying that knowledge into actual scenarios.

Actual intelligence, even given little knowledge, could be able to derive new rules from its roots, can obtain proofs, etc.

It is pretty clear that the language models we currently have, even the old GPT-2, have a huge knowledge base. The problem is that this knowledge is going to be biased and have numerous cases of misinformation and incorrect data.

But, if we had such a model that had a huge capacity to reason, it should be possible for the model (given the right tooling) to introspect itself and try to reason all the knowledge it has, to come up with a set of rules on what is real, what is correct, and what is not.

Of course, not everything will fit this criteria and a lot of information such as world news is either you believe or not what you read. But also correctness or truth doesn’t need to be binary, it can be a scale of greys, or even a multidimensional space with different factors or features.

What this means is that such an AI, if existed, it could inspect any content (Tweets, Videos, Blogs, News,…) and it could come up with a scale of truthfulness for each one. Not only that, but it also could understand if it’s good material or not. Probably knowing if it is good material is easier than knowing if something is correct.

Note that such an AI would probably require multiple rounds to get a minimally accurate result. It probably needs to perform searches and read the internet almost freely in order to get enough context to understand what happens here. Basically it would equate to a human spending hours investigating a single blog post or comment.

This could be used to flag content that scores very low in a very short time frame. The same technology that can be used to flood the internet with misinformation could be used to fight it. And it doesn’t need to differentiate bots – it would target regular users too when they write stuff that it’s clearly wrong. It will be so fast that by the time anyone else sees it, it will be already flagged.

This, that looks like the Police of Truth and censoring, is a very likely future if such AI is made. And it’s not even a bad one, but will get a lot of people outraged.

Of course, this tech can be used by any big corp to push any agenda they want to. But they do this already, so what? It doesn’t change much in this scenario.

But it could be also implemented on the browser side via an extension: The user could register it with the provider of their liking and with the configuration they like, removing a lot of power from the particular sites and adding a custom layer of trust on top. This approach has its problems obviously, some pages could be analyzed just by sending the URLs, and this already means that someone else is getting your browser history (and this has happened in the past and most users were fine with it). Other content where whether it’s not public or the URL might not point to what we’re reading, the actual content might be needed to be sent. And this is even worse. Anyway, I’m not here to solve these problems. Ideally such AI should be able to run in your local computer, but we are very very far from this at the moment.

The point I’m trying to make is that this kind of AI would be groundbreaking (if it can be created). The amount of subscribers/followers will mean almost nothing – and mostly everything will be content-driven.

The good part is that this seems that could get us to a better Internet overall. But is this even possible?

Can a language model be intelligent?

Language models are just very sophisticated machines for predicting the next word (or partial words, as they use tokens). They learn the probability for the next thing given all the previous context and training. Given enough training and that the probability covers enough words, they can create text that very much looks human and it can fool a lot of people. But all it does is predicting the next word – it doesn’t think.

Although I agree with this view, I think it fails to account for emergent behaviors. If I do the same for the human brain, I could say “the brain only interchanges some chemicals and creates some electric impulses and that’s it, brains aren’t intelligent, they don’t think, just it looks like they do”. But admitting that would basically admit that we don’t think and that has to be wrong.

In the end it’s all quarks and other particles and interactions. It is the emergent phenomena and behaviors that make us intelligent. Or maybe we aren’t that smart and we just think this way. Who knows.

There are two things in language models that might suggest they could have the capability for reasoning. First of all, a neural network can be touring complete:

• On the computational power of neural nets, Siegelmann & Sonntag, 1992
• On the Practical Computational Power of Finite Precision RNNs for Language Recognition, Weiss et al, 2018

What this means is that (at least some) neural networks can be considered programs that can compute anything (like any other touring-complete programming language), and therefore because the training process changes the internal values, we can understand it as the NN to reprogram itself.

This alone could mean a lot or could mean nothing. But on the other side, if we look on what GPT3 does we see that it learns very complex patterns, it learns how to write LaTeX properly and almost every programming language.

In the paper Visualizing and Understanding Recurrent Networks we can see an example of how a neuron can encode interesting stuff:

When given enough text, the AI can learn some “hidden” patterns that are helpful for predicting what comes next. By encoding these it extracts usefulness because the predictions become more accurate.

Now, it is not that far fetched that this systems could learn some kind of logical reasoning as a lot of our texts follow it to some degree. We expose the context and typically we reason a conclusion from it (the same I’m doing here right now).

An AI trained on such data would benefit a lot by learning this. And we see already that GPT3 has learned (at the very least) the most basic part: How do we formulate our train of thoughts, reasoning and conclusion. It can create similar texts that follow the same pattern and look terribly correct for someone that doesn’t have expertise on the matter.

Given that the training data has more correct deductions than incorrect ones, or at least, that the incorrect ones do not align to something that can be predicted while the correct ones can, the AI should at some point learn how to deduce stuff by itself to be able to predict stuff more accurately.

Of course, the problem is that the data that is being fed has a lot of incorrect logic and moreover, it is way easier to predict misinformation and biases than to predict correct stuff. So the AI will have a strong preference to learn how to deceive than learning how to think.

But this already gives us a clue, the machine should be already capable of recognizing such low-effort patterns, rambles and echo chambers just because they’re easier to predict.

That also means that in the current state, any exposition that looks correct on the first sight, will also look correct to the AI. In other words, it is incapable to detect well reasoned lies, or even to know if what it is writing is correct or not.

We do have a ton of academic papers, books and data. There’s also terabytes of source code of programs. Programming correctly requires a lot of good logic and the AI would definitely benefit from understanding the underlying thing that it is being attempted in order to correctly predict the program. However a lot of programs have bugs and lack documentation, and it will be not easy for the AI to differentiate this and avoid the bias itself.

If an AI has enough power to process and learn such patterns (remember it is probably touring complete and it’s like a program that rewrites itself), it might be indeed possible. And then the AI would be able to differentiate what is correct from what is not.

And there’s a lot of incentive right now to make this happen, because everyone is trying to make a chat bot that can be asked about stuff and respond correct answers, and regular users are easy to be fooled by such systems. So everyone needs these language models to stop hallucinating and give correct, factual information.

Is it possible? I am no expert at all – just a hobbyist – but I think so. My experience with ChatGPT3.5 had some occasional sparks of reasoning that amused me quite a lot, but these are really hard to reproduce and the conversations are way too long to put in here.

I had no interaction with GPT4 but I’ve been reading other users, and I don’t see any improvement in this area. On the other hand, I’ve been trying out the new Open Assistant that uses the llama model from Facebook and it is clearly worse, way worse than GPT3.5 in reasoning. This leads me to believe that the size of the model is what actually matters here. It does make sense, since correctly applying logic and have actual understanding is really hard stuff. It may need to grow a lot in size to be able to perform such a task.

But if my guess is correct (and it is just a wild guess), then this means that this is just a matter of time. Current AI models seem limited by the GPU accelerators we have at the moment, to the point that the electricity cost for training and giving the service might be borderline profitable. Increasing the model by 10x might not be possible for several years as these GPU just don’t have enough VRAM, and if they did, the electricity costs would be prohibitive. We might need to wait several years until the Moore’s law gives enough performance and efficiency to make this happen.

In the meantime, I’m sure that a lot of researchers are looking at this, on how to make these language models smarter with the current hardware. Or even the usage of specialized hardware that is made just for running this type of AI.

Ending thoughts

This whole thing is just my own thoughts on the subject, don’t take it too seriously. I am no expert at all on this field.

But this is the beautiful thing on the Internet, right? I can express myself and you’re free from reading it or not. Believe it or not.

Until the AI Police of Truth comes in and flags this article as misinformation.

Thank you for reading.

AMD Ryzen 7000 – I’m disappointed

A lot of time has past since the last time I wrote here, doing other stuff, gaming, streaming… but now I’m a bit salty and I need to get this off. AMD Ryzen 7000 line of CPUs are out.

LTT’s Petabyte project – my thoughts on the data loss

Let me start by adding a disclaimer here that I’m currently working for Google (where these kind of problems are well understood) but I’m nowhere near the teams that do anything server or disk related and I have no idea how any of this is solved at Google.

This post is only my opinion as a hobbyist, and Linus has at this point way more experience on this topic than I do, since I don’t work on hardware.

I just would like to explain some of the things that crossed my mind when I saw the video about the drive failures; which of course, is hindsight. Nonetheless, it might be interesting to someone out there.

Review and criticism of Novation Circuit Tracks

As I posted recently, I got a Novation Circuit Tracks for few months. I’m still really new to music in all senses. Playing, composing, producing, I’m still learning.

I got this device trying to get an “all in one” GrooveBox to be able to produce tracks in a self-contained manner and reduce software dependency. Oh boy how wrong I was.

My experience with the Circuit Tracks has been overall fantastic, but I want to demand for more and put a fair bit of criticism on what it lacks or it could be improved.

Producing (awful) Music now!

I’ve been a bit offline on the last 2 months or so, and what I’ve been doing? Well, aside of traveling back to Spain to see friends, I’ve been into music production!

Beginning of November I got a Novation Circuit Tracks, and a few days ago I received the Korg Triton Taktile 49. The Tracks is a GrooveBox and the Taktile is somewhat between a MIDI controller and a Synthesizer.

I’m quite sick of YouTube and Twitch making my life difficult when uploading anything with music. Even I tried to buy a full set of music, that it was already “free” before (in license), but even then I spend more than 300€ to get a paid license.

I’ve been trying to put that music into videos, but still it’s a lot of hassle, video per video, track per track, to dispute copyright to every single track.