Before I move on to my daily research routine I will first attempt to reflect on the conference. Hopefully this way I can reduce the chances of forgetting it ever happened. I won’t go into much depth but only some of the things I remember most clearly.
ECAL felt like an extended-family-of-the-CCNR week-long meeting. And I think that’s a good thing. The most exciting organisational differences between this ECAL and the previous one, as well as at least the two last Alifes, had to be the panel discussions at the end of each day. Although the discussions were not always insightful (see point towards the end on information theory and dynamical systems), they usually reflected the state of something that’s happening in the Alife community. Another good thing about this conference was the stronger congruence throughout each day’s theme as embodied by the keynotes, talks and posters presented in each.
There were several talks that I particularly enjoyed. Randall Beer’s and Janet Wile’s keynotes were the most exciting for me. I liked them the most because of the clearness of their motivations, the relevance of their scientific questions, the concreteness of the frameworks they propose, and the in-depth and systematic approach that they usually take, among other things. What I particularly liked about Randy’s was his outlook for future directions of research. I also found inspiring his emphasis on the idea that the use of dynamical systems analysis was not impossible to achieve, but on the contrary quite tractable, as long – of course – as education on the language and the tools were available to the community. I very much liked how Janet Wiles was so aware of her role as a supervisor of grad students. She kept bringing the topic of ‘approaches to grad students’ up any chance she had, unfortunately I felt most of the times it was glossed over by the rest and usually put to one side.
There were other good talks. I enjoyed Ezequiel’s keynote because of its exploratory nature and the effort to tie the sort of work that evolutionary robotics people do (and not very unlike mine) together with important philosophical strands. Even though Jun Tani and Takashi Ikegami didn’t give keynotes, from their interventions a similar (if not more exploratory still) approach was always present. This was also refreshing. Particularly, because they were so honest about mostly doing it because it was fun for them. Interestingly enough, although I didn’t get most of Brian Goodwin‘s points, his way of addressing us was very inspiring too.
Although I’m not usually amused by engineering feats, I was genuinely impressed with the work of Rudolf Bannasch. Undoubtedly, I was better able to appreciate the extent of his contributions precisely because he made it so clear throughout his talk that his motivation was purely in engineering. This is not typical in Alife. What is more characteristic is that certain disguised-in-science-but-engineering-at-heart works leaves a lot of people confused and makes me in particular obsess throughout the duration of their exposition about which scientific question is the one we are tackling. Also, one usually hears about how understanding of biological organisms is going to impact how we as engineers design machines, but it is not everyday that you see biologically-inspired machines out there in the world. His talk made it seem otherwise which was great. In some respects, I guess Dario Floreano’s talk also helped give that feeling a little bit.
As usual, it is the workshops that stand out the most for me. Unfortunately they all happen in parallel on the same day, so you can only attend to one or two. I attended Rachel Wood’s (and others) ‘dynamics of development’ workshop. Among the participants were Linda Smith, Jane Wiles, Jun Tani, Luc Bertouhze, Ezequiel Di Paolo. Lots of time for discussions and comments. I was fortunate to have been given the chance to comment on Josh Bongard’s latest internal self-modeling robot work and how it might go beyond the metaphor to answer questions relevant to developmental psychologists. I took the opportunity to refresh skepticism about the usefulness of sense-model-plan-act approaches in Robotics when they are used as scientific tools. Overall, I was mostly surprised with the readiness of a good proportion of the participants – in particular people whose work is very much along the lines of a situated, embodied and dynamical systems perspective – to take for granted that we have (or eventually develop) internal models of ourselves that we can ‘use’ to simulate things in.
Worth echoing here are two points that Randy brought up during this workshop. The first is one we are all very familiar with. Basically being very aware of not confusing engineering and science. Particularly of not falling into the trap of going to the scientific meetings and talking about the engineering and viceversa. The second point worth mentioning is regarding the relevance of physical or simulated models. Although I was familiar with the discussion, I hadn’t seen it from the perspective that Randy pointed out until then. His point was that the use of physical robots need not be, by default, more assumptions free nor more realistic, nor better in any a priori way than simulations. He gave a good example of how sometimes simulations can be more biologically realistic than physical models, and how this depended on the proof of how the relevant biomechanical properties compared to the real organism being modeled and not simply on the physicality of the robot – as choices of materials, dimensions, etc. could be very much inappropriate. This is how I interpreted and recall those two points now anyways.
I also took part in the ‘dynamics of learning and neuromodulation’ workshop, of course. That went really really well. Lots of people turned up. In fact some people told me it was one of the busiest workshops there. There was lots of discussion too. Some of it was unfortunately spent clarifying the two different levels of defining learning: mechanistic and behavioral. I understand that it is something that takes a while to assimilate, but I would have thought by now (and in the context of that workshop) that everybody would have been aware of the difference. Some people would inevitable (no matter how many times we reminded them) kept slipping back into a ‘learning as mechanism’ perspective. When that wasn’t the case however, an interesting discussion of what was behavioral learning dominated the discussion – with particularly relevant insights coming from Randy, Anil Seth, Chrisantha and Seth Bullock. This was particularly relevant for me as most of the issues that were discussed are very relevant for my thesis.
There were many good talks and posters as well. But it will take me longer to realise which are relevant for me and which aren’t.
Lisboa was great of course. I’m sure I don’t need to say much there. Undoubtedly, Barrio Alto served as the secondary venue for the conference.
One of the panel discussions opened up a debate about information theory and dynamical systems theory which was unfortunately not too insightful (it has been followed in the conference blog). For two reasons mainly. First, most people seemed to equate dynamical systems theory with particular dynamical neural models and information theory with computation. This obviously leads to people talking across each other about a lot of senseless stuff. Several people pointed this out during the discussion but a few minutes later the discussion would slip into loaded notions of the terms. Second, because people would sometimes argue that one was ‘a better tool’ than the other. This doesn’t make sense, on his own anyway. Better at what? Both of them are tools that can be used to analyse dynamical (and non-dynamical) agent/environment systems. What I think some people may have meant is that there can be certain types of question that one tool will be able to answer better than the other (and of course, some questions which might be better answered by using both or neither tools). Dynamicists, as they are sometimes called, think that certain aspects of interactions (such as relative timing, morphologies, biomechanics, etc.) are important for what they understand as cognition. Therefore, dynamical systems tools are most of the times the more appropriate tool to address the sorts of questions that they have about their systems of interest. On the other hand, informationists (or however these people are called), have very different types of questions about their systems. This is sometimes because of the nature of the systems which they study (such as throwing a dice with clearly labeled sides and discrete events in time), but ultimately the type of system under study should not force one to study it with one tool or the other. It is their type of questions that are different. For example, they might be interested in how much mutual information an agent shares with its environment. It is questions of this nature that they think are relevant for their understanding of cognition, for which information theory is a mighty powerful tool. In summary, I don’t think there is a debate worth having about which is a better tool. Particularly, in the absence of the context of which question and which system the user of the tool is trying to answer. It is more interesting to debate which questions we are asking about our agent/environment systems and the tools will simply follow.
Finally, a couple of comments regarding the blog and wiki that were set up. Ideally they should have been arranged with some more anticipation. More importantly, however, is that they be made more general ‘artificial life community’ blogs/wikis and not just ECAL2007 ones (e.g. http://artificiallife.wordpress.com/ ?). That’s only a minor matter of changing domain name and title I guess. Perhaps it should be done for the wiki. On a different but related idea, would anybody (else besides me) find useful a user-submitted voted-on and commented-on paper repository in the form of an ‘artificial life community digg-style’ site? Just a thought.
ps. as the astute reader will have noticed (I always wanted to say that phrase) I am making an effort to not so easily refer to the brain or the mind as the place where things ‘happen’ when we ‘think/learn/remember’ things (e.g. the title of the post). I’m sure you will have found it utterly silly and downright annoying. I don’t blame you. But I think it should be relevant to have a situated, embodied and dynamical systems perspective not just in our research but throughout our lives, down to even our most meaningless day-to-day expressions. Although I understand they are not meant to be accurate, I reckon the accumulation of these phrases make it harder to turn cognition into a less GOFAI-oriented science. I’m sure, however, that for the larger proportion of the three people reading this blog it will not come as a surprise that their body is relevant to what they learn and remember.