This paper makes two pedagogical contributions. First, we describe two macrooperators for best-first search algorithms: the collapse macro where asubtree is deleted from memory and its best frontier value is stored in itsroot, and, the restore macro (the inverse of collapse) where thesubtree is restored to its previous structure. We show that many known searchalgorithms can be easily described by using these macros. The secondcontribution is an algorithm called Iterative Linear Best-first Search (ILBFS). ILBFS is equivalent to RBFS. While RBFS uses a recursive structure,ILBFS uses the regular structure of BFS with occasionally using the collapseand restore macros. ILBFS and RBFS are identical in the nodes that they visitand have identical properties. But, I believe that ILBFS is pedagogicallysimpler to describe and understand; it could at least serve as a pedagogicaltool for RBFS.

Most of previous work in knowledge base (KB) completion has focused on the problem of relation extraction. In this work, we focus on the task of inferring missing entity type instances in a KB, a fundamental task for KB competition yet receives little attention. Due to the novelty of this task, we construct a large-scale dataset and design an automatic evaluation methodology. Our knowledge base completion method uses information within the existing KB and external information from Wikipedia. We show that individual methods trained with a global objective that considers unobserved cells from both the entity and the type side gives consistently higher quality predictions compared to baseline methods. We also perform manual evaluation on a small subset of the data to verify the effectiveness of our knowledge base completion methods and the correctness of our proposed automatic evaluation method.

Dr. Ford is being recognized Outstanding Program

We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. Big data problems that involve integrating data from multiple sources can benefit from our approach when the datas ontologies are unknown, inaccessible or semantically trivial. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

In AI we talk about expert systems.

Oliver G. Selfridge was born in London 10 May 1926. He studied at the Massachusetts Institute of Technology from 1942-1945, returning postgraduately from 1946-1950. After 2 years at Signal Corps Laboratories at Fort Monmouth, New Jersey, he joined Lincoln Laboratories in Group 34, Communication Techniques, of which he is now Group Leader. INTRODUCTION WE are proposing here a model of a process which we claim can adaptively improve itself to handle certain pattern recognition problems Which cannot be adequately specified in advance. Such problems are usual when trying' to build a machine to Imitate any one of a very large class of human data processing techniques. A speech typewriter is a good example of something that very many people have been trying unsuccessfully to build for some time. We do not suggest that we have proposed a model which can learn to typewrite from merely hearing speech. Pandemonium does not, however, seem on paper to have the same kinds of inherent restrictions or inflexibility that many previous proposals have had. The basic motif behind our model is the Inn of parallel processing.

An unsophisticated translation of such a sentence will therefore not be a good translation. Again, contrary to Mr. Richensi opinion, I believe that the problem involved is serious. There is no simple procedure to find out which, and in what way, the words of the English language are context-dependent. And I don't think that the issue can be belittled for tae reason that contextdependent words do not occur in scientific discussions and writings. They might not be too abundant in ordinary scientific papers on matters physical or chemical, but there would surely be plenty of them in discussions of matters linguistic, for instance. This might be one reason why so far hardly anybody has tried to machine translate papers in linguistics. As soon as this is attempted, the seriousness of the problem will become immediately evident.

Marvin Lee Minsky was born in New York on 9th August, 1927. He received his B.A from Harvard in 1950 and Ph.D in Mathematics from Princeton in 1954. For the next three years he was a member of the Harvard University Society of Fellows, and in 1957-58 was staff member of the M.I.T. Lincoln Laboratories. At present he is Assistant Professor of Mathematics at M.I.T. where he is giving a course in Automata and Artificial Intelligence and is also staff member of the Research Laboratory of Electronics. SUMMARY THIS paper is an attempt to discuss and partially organize a number of ideas concerning the design or programming of machines to work on problems for which the designer does not have, in advance, practical methods of solution. Particular attention is given to processes involving pattern recognition, learning, planning ahead, and the use of analogies or?models!. Also considered is the question of designing "administrative" procedures to manage the use of these other devices.

Biology seems to be a science in its own right, or set of sciences having common aims, and so it should have its own language and explanatory concepts; yet when any specifically biological concept is suggested and used as an explanatory concept it seems to be unsatisfactory and even mystical. There are many biological concepts of this kind: Purpose, Drive, elan vital, Entelechy, Gestalten.* Physicists and engineers seem, on the other hand, to have clearly defined concepts having great power within biology.

If ability to perform complex calculations were a sufficient criterion, then even a conventional digital computor could lay claim to more intelligence than any of usand perhaps we had better let it make away with the word and be done with it.