Category: Science & Tech

Where do kids hang out nowadays?

When I was growing up, online hangouts occurred on IRC, forums or altavista chatrooms. All these platforms had one thing in common: anonymity. The internet was objectively known to be a creepy place.

The first time I went online, I was around 9 years old, around the turn of the millennium. I registered a nickname on my country’s IRC network. Radark, I thought it sounded super badass. I convinced a group of people I was a 16 year old hacker. I had watched my older sibling play around with Netbus and Sub7 a few days ago, so seemed easy enough. Plus, the copied book we got sent by snail mail called “Hacking” gave me some street cred.

So off I went on one of my first online adventures, convincing a group of computer science enthusiasts or students that I was a 16 year old hacker. And it all went well for the whole time of 3 hours, until my sibling came back and told them Radark is a 9 year old kid.

Woopsy. Too bad, great thing I could just do /nick aNewName and off I go, another personality, others to fool, but more importantly, others to converse with. While growing up, I chatted to a lot of people. I did not have many friends in real life, so the internet was a great place. In particular, I could talk about anything without fearing the repercussion of my opinion not being accepted. And as easily as you could create a personality, you could erase it. And it was fine, nobody cared. Granted, you do /whois to figure out whether a nickname was registered or not. But that was pretty much as much as you would know about your conversation partner.

Nowadays, I don’t know where kids hang out anymore. I hope I am wrong, but I have the impression kids hang out mostly on Instagram or Snapchat (Facebook is for old people). And this is worrying because all these platforms have, in its core, the user-centric aspect of it. These are not primarily chatting platforms, despite being used as such. Indeed, they are broadcasting tools – the user is incentivized to share content, which ends up often being personal content (in the form of pictures, videos or others). This also means that changing/deleting profiles is actually an annoying process because you put some effort into these profiles. It also means that the barrier to randomly chatting to someone is now higher – it’s not longer whether the nickname has been registered or not, but whether the other person has shared some content themselves, has enough followers, etc.

I think in general, people don’t think much about the information that can be inferred from public social media posts. Indeed, the current state of the internet incentivizes users to not think about it: it’s cool, link your phone number to your facebook, to use your real name, share geotagged content, win points, whatever. All these academics publishing stuff on user privacy, they are tinfoil hat dudes.

Anyway, my hope is that I am completely wrong, and kids just use Discord now.


Generating Art using Robotics and AI: What is out there? (2012)

*** Access the full report here.

     Acess the introduction post here***

One of the first uses of randomization in arts is commonly attributed to Wolfgang Amadeus Mozart, in the field of music. He wrote the measures and instructions for a musical composition dice game, where pre-written measures of music were chosen according to a mapping between these and the results of throwing die; these were pasted together afterwards to create a Minuet. [1]

However, it was only in the 20th century that randomization in arts became more popular. John Cage’s work named Music of Changes in 1951 is often considered the first piece to be largely produced through random methods, where he used I Ching, a Chinese classic text commonly used as a divination system. He would “ask” the book questions about various aspects of the composition at hand and use the answers to compose.

In visual arts, it was Ellsworth Kelly, in 1957, who first featured generative methods in his pieces by using chance operations to assign colors in a grid, or colourful pictures and cheap materials cut into strips or squares to produce collages.


Ellsworth Kelly, Spectrum Colors Arranged by Chance V, 1951

Other artists, namely, Alfred Jensen and Carl Andre, traditionally abstract artists also contributed to the generative art movement during the 20th century.

However, it was with Frieder Nake, a mathematician, that computer art became more well defined. He began to write computer programs to generate drawings. He used Zuze Z64 Graphomat, a digital plotter from 1961 developed by computer pioneer Konrad Zuse, to do drawings as of 1963. [2]

In 1973, the software AARON was born in University of Stanford developed by artist Harold Cohen and it is now a project that has been ongoing for many decades. AARON was one of the first machines to be made with the intention to programme a creative agent that produces art and collaborates with its creator. However, despite being an autonomous generator of drawings, it cannot learn new styles nor objects. Each new functionality and style has to be hand-coded. [3] For instance, when generating the drawing of a person, it goes back to memory and finds the components needed to generate a person (e.g. arm, torso, etc.), however, the randomness is introduced to decide the position of each component, for instance, at what angle the elbow is going to be at. \cite{aaron}


AARON, In Zana’s Room, 2013

In 2006, in the Computational Creativity Research Group at Imperial College London the Painting Fool was born. The objective is to create a software that will be taken seriously as a creative agent.

Shortly after, in 2009, eDavid was started. This is a project from University of Konstanz, where researchers are creating a robot that physically paints.

eDavid project page

In 2012, Vangobot was built as a collaboration between an artist and a programmer. It’s a robot that given an image and instructions paints on a canvas, being able to accomplish different styles. [4] BNJMN was also created in 2012, as a project from the Basel Academy of Art and Design, and it is a “mobile sensory image production mechanism” [5]. It roams in search of paper and produces abstract art.




[2] R. Bowlin (2010), Z64 Graphomat and Frieder Nake. Available:

[3] P. McCorduck, Aarons Code, W.H.Freeman & Co Ltd, 1991

[4] L. Kelly, D. Marx, (2012), The vangobot project, Available:


Generating Art using Robotics and AI – Introduction & Context

*** Access the full report here. ***

When talking about art generated by robots, there are several interpretations ranging from a very basic system that prints art to a fully automated creative agent. What’s more, between these two extremes, there have been a lot of different contributions and projects involving both the art and the scientific community.

Within the arts community, generative art refers to any art practices where the artist uses a system (this could be a set of rules, a computer program, a machine, or any other procedural invention) that has a degree of autonomy that contributes to or results in art.[1]

A common misconception is to think generative art is only using computer programmming in art. In reality, the key concept of this type of art is the procedural invention. This means that generative art includes the usage of chemical reactions, living plants, condensation/crystallization processes, melting substances, or any other physical process that would take place autonomously. The keypoint here is that the artist chooses to give up some degree of control to an external system and thus, the artwork will then result from more than the artists’ input and intuitive decisions.

For Philip Galenter the motivation for randomization in art, and with this, the usage of randomization and generative techniques derives from complexity science and complexity theory. The idea is that complex systems have generally a certain degree of randomness, thus, to create truly complex art, it makes sense to include some randomness in the process.[2]

However, not all types of randomness imply complexity in the context that would be used in art, where complexity is often associated to being interesting and communicating higher order information. Thus, there is a need to find a way to quantify complexity with a measure that is appropriate in the context of art.

The usage of Shannon’s entrophy was quickly abandoned as it did not fit the artistic definition of complex, as highly random processes do not necessarily generate the most complex or interesting pieces.[2]

From an intuitive point of view, working artists understand that an audience will quickly be tired of both a highly ordered and a highly disordered aesthetic experience because both lack structural complexity worthy of their continued attention. The intuition that structure and complexity increase somewhere between the extremes of order and disorder fits in with the idea of effective complexity, introduced below.

What seemed to be more relevant in the context of art was Murray Gell-Mann’s idea of effective complexity. In effective complex systems, the ones which are highly ordered or disordered are given a low score, indicating simplicity, and systems that are somewhere in between are given a high score, indicating complexity.

To measure effective complexity Gell-Mann proposes to split a given system into two algorithmic terms: the first algorithm capturing structure and the second algorithm capturing random deviation. The effective complexity would then be proportional to the size of the optimally compressed program for the first algorithm that captures structure. The random aspects are forgotten and the aspects that exhibit structure are compressed (abstracted and generalized). Structural aspects that resist compression are then experienced as being complex. Thus, highly ordered systems from nature such as crystals, or highly disordered systems such as atmospheric gases, yield low measures of effective complexity, however, the adaptive systems found in nature, the living things biology have high effective complexity.[3]

One of the objectives of generative artists, as mentioned above, is to introduce new elements to their art and use techniques that are originated in science, but another one that is mentioned in [2] is that generative art is seen as a way to bring artists and scientists to collaborate more.

It is noted that, despite the existence of a trend towards technology-based art, the fundamental philosophies of arts, humanities and science in the 20th are very distinct. It is hoped that generative art, due in part to its technology based nature, to be an interesting area where both artists and scientists can exhibit and work in tandem.[2]

What I would say is the corresponding to generative art within the scientific community is the sub-field of Artificial Intelligence called Computational Creativity. In Computational Creativity, one of the main interests is the answer to the following question: “under what circumstances (if any) is it appropriate to describe the behaviour of a computational system as creative?

There are three main motivations for the study of Computational Creativity[4]:

  1. To provide a computational perspective on human creativity, in order to help us to understand it (cognitive science);
  2. To enable machines to be creative, in order to enhance our lives in some way (engineering);
  3. To produce tools which enhance human creativity (aids for creative individuals).

The theory Computational creativity theory (CCT) is seen as the analogue in Computational Creativity research to Computational Learning Theory (CLT) in the field of Machine Learning. In Computational Creativity research, the question that is asked is “What does it mean to say a computer has created something?”, whereas in Machine Learning, what is largely studied is how to induce a concept definition (or set of rules) to fit data which has been given in advance, and to use this learning to classify new examples with respect to the learned concept. Machine learning enabled the formalisation of acts of learning within Computational Learning Theory. Dr. Simon Colton formalizes the idea of CCT: the aim of this theory is to provide a rigorous, computationally detailed and plausible description of how creation can be achieved.[5]

Some more technical problems are also part of this sub-field of research, such as how to teach a physical robot to paint, how to formalize a creative process or how to represent various aspects of art (let this be video, images, colours) in a computer. By treating these problems, this research field becomes multidisciplinary, involving concepts from Mathematics, Computer Science and Mechanical Engineering, to name a few.

[1] P. Galanter (2003), What is Generative Art?, Available:

[2] P. Galanter (2003), Complexism and evolutionary art in The Art of Artificial Evolution, Springer, New York, 2008, pp. 311 – 330

[3] M. Gell-Mann, What is complexity? in Complexity, John Whiley and Sons, Vol 1 No 1, 1995

[4] S. Colton, On Impact and Evaluation in Computational Creativity: A Discussion of the Turing Test and an Alternative Proposal, In Proceedings of the AISB symposium on AI and Philosophy, 2011

[5] S. Colton, J. Charnley and A. Pease, Computational Creativity Theory: The FACE and IDEA models, In Proceedings of the International Conference on Computational Creativity, 2011

Impostor syndrome or accurate perception.

I don’t think I’m good enough to do the things I want to do and this makes me sad. But if I am still allowed to do them, why should it matter? I have the inner belief I’m completely mediocre, that these things go way beyond my skills, but if some others still allow me to play with these things – by hiring or working with me, then why should I feel ‘hopeless’ (for lack of a better word)? Is it an ego thing?  Like how one should work to be recognized and ought to be successful, destined to be fucking amazing and thus, being mediocre is equivalent to being a loser, piece of shit. Or is it an honesty thing? As in, tricking people and wasting their time fully conscious of this fact. Or is it the fear that at some point, others will too realize my ineptitude and by then, I would have wasted too much time doing things I am not qualified for? Even so, what would be the awful consequence of that – except the perception of again, being a loser? Perhaps is that I could have become good at something I am suited for and by then, I might have lost the chance to?
But more importantly, is my love for these topics enough to make me satisfied, despite the high chances that I will never achieve much (as if that were the ultimate goal, for some reason)?
I flicker between the self perception of a naive fool and an idealistic one. And yes, tried googling for answers. No results were found.