AAAI 2012 has gone for another year and it was a great conference by all accounts, as long as you stayed out of the Toronto heat. In an upcoming post we’ll have a more a detailed overview of the papers from the Computational Sustainability track. One thing most attendees would probably agree on was that this year’s plenary speaker’s lineup was fantastic. I found a few of the plenary talks particularly inspiring for the future of AI as well for Computational Sustainability research even though the talks were not focussed on that topic:

• Judea Pearl was awarded the highest honour in Computer Science, the ACM Turing Award, for 2011 and he chose to give his ACM Turing Lecture at AAAI 2012. He provided a fascinating history of research into inference and causal learning. He argued that counterfactual reasoning is the best basis to create ‘mini-Turing tests’ since humans naturally and effortlessly carry out counterfactual reasoning all the time. Yet it is something that is still not widely used in modelling and AI systems.  Advances in causal inference and learning would of course be a huge benefit for all scientific pursuits. In sustainability sciences in particular, there is a huge amount of data collection being carried out and discovering causal relationships in this data (such as between native organisms and invaders, climate change and pollutants, policies and energy usage) are the core challenges in these fields. Algorithms and usable tools that scientists can use to more quickly test their causal hypothesis’ or discover new possible causal relationships would have a huge impact.
• Christos Papadimitriou gave the AAAI Turing Lecture, which was an inspiring and very personal talk on the contributions of Alan Turing on this 100th anniversary of his birth. Papadimitriou compared Alan Turing to Charles Darwin as a founder of a field but also as a major contributor to the understanding of biology and evolution.  He described how computer science is transforming the fields of biology and genetics. One example are some recent results from his research group applies theoretical CS analysis to resolve the conundrum of sexual recombination in genetics. The problem, as I understand it, is that asexual recombination works perfectly well in many simple organisms, optimizing fitness as the number of offspring produced. So the question is, why do more complex organisms use sexual recombination? Why do they require two individuals when one seems to work perfectly well?  Their analysis and experiments show that sexual recombination is optimal if the fitness measure isn’t the number of offspring produced but rather, the ability to breed widely. Thus, robustness of breeding is being favoured over maximizing offspring. This is something which has apparently been hard for geneticists to work out from their point of view but from a computational perspective was more easily achievable.  The message from this being that we should never underestimate what contributions computer science can make to other fields of human knowledge. Even theoretical concepts can turn out to provide a better understanding of something in the world; but we as computer scientists need to reach out and make the connections ourselves, because it is unlikely else will.
• Sebastian Thrun later spoke about the Google self-driving car project which he is a part of. It was a great update on how far Google has come in a remarkably short time towards a feasible self-driving car that can be used on a large scale. Attendees had a bit of an insider view of some of their latest results a few weeks before the media ran stories on Google making more confident announcements about how reliable their cars are compared to human drivers (spoiler: the self-driving cars are more reliable). As Thrun pointed out in his talk, there are still a small percentage of cases where the human driver needs to take over but under normal driving conditions these situations come  up on the order of every few months rather than hours or days. One obvious connection between self-driving cars and sustainability is energy efficiency. If a critical mass of cars on the road are self-driving then many options become possible such as coordinated traffic, drafting to increase fuel efficiency and more dynamic carpooling. Thrun pointed out that if you look at the utilization of roads in the USA very little of the space is actually used at any one time. Self-driving cars could tailgate much more closely and thus reduce the need to build more roads into existing natural areas. But the other lesson I took away from this is that along the way to attacking the problem of  self-driving cars they encountered challenging open problems, such as : how to combine huge amounts of data from heterogeneous sensors; how to dynamically switch datasources in real time when one system failed (eg. when the maps are out of date due to road construction); complex problems of spatial reasoning about the identity of objects showing up on the laser scanner (ie. is it a tree, another car or a person?).  Of course, all of this also needs to be done in real time with a very, very low failure rate because lives are on the line. By forcing themselves to deal with all these challenges at once in search of an ambitious goal they needed to find new solutions for visualization, learning, optimization and data management. I think one of the big Computer Science gains of Computational Sustainability research is a similar necessity of invention that arises from dealing with problems which are larger, more noisy or more heterogeneous than a simpler test domain would provide.