The idea of an artificial intelligence (AI) uprising may sound like the plot of a science-fiction film, but the notion is a topic of a new study that finds it is possible and we would not be able to stop it. A team of international scientists designed a theoretical containment algorithm that ensures a super-intelligent system could not harm people under any circumstance, by simulating the AI and blocking it from wrecking havoc on humanity. However, the analysis shows current algorithms do not have the ability to halt AI, because commanding the system to not destroy the world would inadvertently halt the algorithm's own operations. Iyad Rahwan, Director of the Center for Humans and Machines, said: 'If this happened, you would not know whether the containment algorithm is still analyzing the threat, or whether it has stopped to contain the harmful AI.' 'In effect, this makes the containment algorithm unusable.' AI has been fascinating humans for years, as we are in awe by machines that control cars, compose symphonies or beat the world's best chess player at their own game.

Superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. In light of recent advances in machine intelligence, a number of scientists, philosophers and technologists have revived the discussion about the potentially catastrophic risks entailed by such an entity. In this article, we trace the origins and development of the neo-fear of superintelligence, and some of the major proposals for its containment. We argue that total containment is, in principle, impossible, due to fundamental limits inherent to computing itself. Assuming that a superintelligence will contain a program that includes all the programs that can be executed by a universal Turing machine on input potentially as complex as the state of the world, strict containment requires simulations of such a program, something theoretically (and practically) impossible. "Machines take me by surprise with great frequency. This is largely because I do not do sufficient calculation to decide what to expect them to do." Alan Turing (1950), Computing Machinery and Intelligence, Mind, 59, 433-460

Querying knowledge bases is one of the most important and fundamental tasks in knowledge representation. Although much of the work on querying knowledge bases is focused on conjunctive queries, there is often the need to use a simple form of recursion, such as the one provided by regular path queries (RPQ), which ask for paths defined by a given regular language. Conjunctive RPQs (CRPQs) can then be understood as the generalization of conjunctive queries with this form of recursion. CRPQs are part of SPARQL, the W3C standard for querying RDF data, including well known knowledge bases such as DBpedia and Wikidata. In particular, RPQs are quite popular for querying Wikidata. They are used in over 24% of the queries (and over 38% of the unique queries), according to recent studies (Malyshev et al., 2018; Bonifati et al., 2019). More generally, CRPQs are basic building blocks for querying graph-structured databases (Barceló, 2013). As knowledge bases become larger, reasoning about queries (e.g. for optimization) becomes increasingly important.

The Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. In light of recent advances in machine intelligence, a number of scientists, philosophers and technologists have revived the discussion about the potential catastrophic risks entailed by such an entity. In this article, we trace the origins and development of the neo-fear of superintelligence, and some of the major proposals for its containment. We argue that such containment is, in principle, impossible, due to fundamental limits inherent to computing itself. Assuming that a superintelligence will contain a program that includes all the programs that can be executed by a universal Turing machine on input potentially as complex as the state of the world, strict containment requires simulations of such a program, something theoretically (and practically) infeasible.

Static analysis is a core task in query optimization and knowledge base verification. We study static analysis techniques for SPARQL, the standard language for querying Semantic Web data. Specifically, we investigate the query containment problem and query-update independence analysis. We are interested in developing techniques through reductions to the validity problem in logic.

The rectangle-packing problem consists of finding an enclosing rectangle of smallest area that can contain a given set of rectangles without overlap. Our new benchmark includes rectangles of successively higher precision, challenging the previous state-of-the-art, which enumerates all locations for placing rectangles, as well as all bounding box widths and heights up to the optimal box. We instead limit the rectangles’ coordinates and bounding box dimensions to the set of subset sums of the rectangles’ dimensions. We also dynamically prune values by learning from infeasible subtrees and constrain the problem by replacing rectangles and empty space with larger rectangles. Compared to the previous state-of-the-art, we test 4,500 times fewer bounding boxes on the high-precision benchmark and solve N =9 over two orders of magnitude faster. We also present all optimal solutions up to N =15, which requires 39 bits of precision to solve. Finally, on the open problem of whether or not one can pack a particular infinite series of rectangles into the unit square, we pack the first 50,000 such rectangles witha greedy heuristic and conjecture that the entire infinite series can fit..