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In this century a number of events could extinguish humanity. The probability of these events may be very low, but the expected value of preventing them could be high, as it represents the value of all future human lives. We review the challenges to studying human extinction risks and, by way of example, estimate the cost effectiveness of preventing extinction-level asteroid impacts.

This talk is an essay on design. In the 16th century, Michel de Montaigne invented a new genre of writing he called an essai, which in modern French translates to attempt. Since then, the best essays have been explorations by an author of a topic or question, perhaps or probably without a definitive conclusion. Certainly in a good essay there can be no theme or conclusion stated at the outset, repeated several times, and supported throughout, because a true essay takes the reader on the journey of discovery that the author has or is experiencing. This essay-on design-is based on my reflections on work I've done over the past 3 years. Some of that work has been on looking at what constitutes an "ultra large scale software system" and some on researching how to keep a software system operating in the face of internal and external errors and unexpected conditions.

TAPIR started up as a research project in June 2001. In 2002 the project is sponsored by NORDINFO and the Research Council of The Danish Ministry of Culture. TAPIR aims at investigating the potentials of applying the diversity of cognitive representations pointing to scientific full-text documents following the principle of poly-representation. Poly-representation (or multi evidence) implies to utilize the cognitively different overlapping interpretations, also over time, made by different actors participating in interactive IR. Such cognitive overlaps derive, for instance, from the authors own perceptions of their work (titles, full-text terms), from human indexing (e.g. descriptors), or from citations given to the work by other authors. The assumption is that the more cognitively different the representations simultaneously pointing to a document are, the higher is the probability that the document is relevant to a given set of criteria.

Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development.