Computer Blindness

In December we visited a lecture about interaction between arts and sciences, namely between (primarily) visual arts and (again, primarily) multimedia studies (Russian page). The lecture was given by Asya Ablogina, who turned out to be a nice girl. Although she lacked any technical background, she was doing well. Surprisingly, there is a lot of artwork that exploits technical support in a witty manner, which I was unaware of, so the lousy stuff exhibited in the Moscow Museum of Modern Art is not everything one can do in this field!

The most interesting part was Asya's exposition of some masterpieces. Most of them were presented during 2009 Science as Suspense exhibit in Moscow (Russian). Nicolas Reeves is a famous Canadian architect, also known for his work on modelling biological systems. He used the output of biologically-inspired computer algorithms (such as genetic algorithms) to draw pictures. In Moscow he presented a project called Marching Floating Cubes: massive but light cubes float in the air. Their movements are controlled by tiny fans, although any little gust of wind can affect the movement. Each cube is equipped with an on-board computer, which helps to avoid collisions. The implemented algorithms are simple but stochastic, hence the behaviour is unpredictable. They are said to move like animal creatures. Here is the video:

A similar project was developed by Paul Granjon. He also tries to gift robots with animal behaviour. In the video below, robots are sexed, i.e. they are able to locate robots of the opposite sex and eventually end up in coitus. Another Paul's robot (the Smartbot, photo) creeps over the restricted space and always grumbles (just like Marvin!). I also enjoy the way Paul speaks:

The real idol in the sci-art community is Stelarc (see also his homepage, although it takes balls to get through the welcome page :). His talent is recognized by both scientists and artists (it is enough to mention he's an Honorary Professor of Arts and Robotics at Carnegie Mellon). He's best known for his experiments on his own body. For example, during a performance he allowed to control his body remotely over the Internet by muscle stimulation. Probably his favourite project is Prosthetic Head, which is in fact a 3D model of his own head. The head is learned from Stelarc's behaviour and speech, so it is able to communicate with people using colloquial information as well as non-verbal cues. It is interesting to try it in practice, but here is just a non-interactive demo video:

Stelarc is probably the only person on Earth who has three ears. In 2007 surgeons implanted an ear into his arm! It is not functioning now (just a piece of skin), but he plans to install an audio receiver into the ear and broadcast everything he hears over the Internet. I definitely recommend to look through his projects, there are decent ones.

Asya also presented her own works. She focus on different photographic techniques such as overexposure. Here are photos from her project Canvas of the Road compiled in a video. There is a play of words in Russian: the single word for canvas and road surface. On these photos car traces look like painter's strokes:

Another Asya's project is a short film series called Habitat. She showed only one episode with the title Habitat: MSU Main Building. The film was about a girl who is a PhD student in Moscow State University and described her place: a standard 8 sq.m. dorm room. The narrative was full of expressive epithets and metaphors about that awful room: the girl felt like in a cage, she also didn't like shared bathroom, etc. It's funny, because I live in a similar PhD student single room, and I'm quite happy with it, since I've been living 5 years in a shared room before. :)

The second lecturer, Vladimir Vishnyakov, presented his project called The Museum of Revived Photography. He used the photos of the XIX century to create image-based animation. The idea is technically simple: first segment people from the background, then use inpainting techniques to restore the background behind them, and animate the movements, but Vladimir referred to a programmer he works with as a real genius. In fact, all the stuff I post here varies from easy to moderately complex (except of Stelarc's projects), so you can do something similar too. The main problem is to come up with the concept. Come on then! ;)

Recently I realized that object class detection and semantic segmentation are the two different ways to solve the recognition task. Although the approaches look very similar, methods vary significantly on the higher level (and sometimes on the lower level too). Let me first state the problem formulations.

Semantic segmentation (or pixel classification) associates one of the pre-defined class labels to each pixel. The input image is divided into the regions, which correspond to the objects of the scene or "stuff" (in terms of Heitz and Koller (2008)). In the simplest case pixels are classified w.r.t. their local features, such as colour and/or texture features (Shotton et al., 2006). Markov Random Fields could be used to incorporate inter-pixel relations.

In theory, the two problems are almost equivalent. Object detection reduces easily to semantic segmentation. If we have a segmentation output, we just need to retain object classes (or discard the "stuff" classes) and take MBRs of regions. The contrary is more difficult. Actually, all the stuff turns into the background class. All the found objects within the rectangles should be segmented, but it is a solvable issue since foreground extraction techniques like GrabCut could be applied. So, there are technical difficulties which could be overcome and the two problems could be considered equivalent, however, in practice the approaches are different.

Today I attended Mikhail Burtsev's lecture on evolutionary approach to AI. The lecture was hosted by Moscow Polytechnic Museum's lecture hall. First of all, the organization was great, and the quality of presentation was high. Unsurprisingly, the hall was almost totally occupied by the listeners. I recommend you to attend their lectures.

Mikhail talked a lot about the history and foundations of AI, which was not new to me. He did not bore the audience with the psychology-related stuff, but featured two approaches to AI, that have been being developed simultaneously all the way since 1950s: artificial neural networks and symbolic computational AI. ANNs attempt to model human brain functioning by means of artificial neurons and axons. The problem here is if we build even human-scale artificial brain (which was actually done in Allen Institute for Brain Science in 2005), it will be isolated from the world, and could not be trained like real human brain. Symbolic AI uses formal logic to solve the problems. It also suffers certain problems, like the lack of uncertainty. So, vanilla AI is in the period of stagnation now (it dates bake to the late 1980s, when Rodney Brooks started to criticize it).

Mikhail's point was the one that most researchers attempt to build a straight away model of human intelligence. He suggested to look at the evolution process. We need to start with understanding how the neural system of an amoeba operates, and then track the evolution of it to find out how a human being's mind works. Evolutionary algorithms might be useful there.

I cannot agree with that point. Last 50 years witnessed the failure of fundamental AI research. Their common problem was they tried to model some cognitive mechanisms instead of tackling actual problems, while those problems were being solved by different techniques. Consider the autonomous robot control problem (e.g. a robotic vacuum cleaner). It can be formulated in terms of reinforcement learning and solved by the methods not connected to the vanilla AI (dynamic programming-based, I suppose). Mikhail does not depart from the problem, again. So I don't really believe in success of the method. I am also sceptic to the computational methods inspired by biology. They could be a good starting point, but they used to be supplanted by mathematically founded ones.

Another issue raised in the discussion after the lecture was AI safety, which is probably the most popular philosophical question connected to AI. As far as I understood, there are two main concerns: the AI could become self-replicated (or self-improved) uncontrollably, and it could become self-motivated. If the AI is self-replicated, it is not so dangerous unless it is self-motivated. If it is motivated by a human, it is bad just like nuclear weapon, which is scary only if an evil party possesses it (so, nothing brand new here). If the AI will be motivated to gain the world domination, it could be harmful. But where such self-motivation can come from? In my opinion, the only option is the biological obsession to propagate (imagine some joint AI system with bio-motivator), but such a disaster has already been overcome once. Do you remember that rabbits in Australia? I hope, an uncontrolled AI won't be a bigger problem.