He was a musical innovator who revolutionised the world of jazz. Now the life and career of Miles Davis is being celebrated in a new film directed by, and starring, Oscar-nominated Don Cheadle. Mark Savage took the Hollywood actor to a record shop to find out how Davis's creative genius inspired the film.

Summary: The premise of this new Key Object architecture is that search is broken, at least as it applies to complex merchandise like computers, printers, and cameras. An innovative and workable solution is described. The question remains, is the pain sufficient to justify a switch? As we are all fond of saying, innovation follows pain points. Are we missing something in our uber-critical search capabilities that needs to be resolved?

In July 2013, an up-and-coming artist had an exhibition at the Galerie Oberkampf in Paris. It lasted for a week, was attended by the public, received press coverage, and featured works produced over a number of years, including some created on the spot in the gallery. Altogether, it was a fairly typical art-world event. The only unusual feature was that the artist in question was a computer program known as "The Painting Fool." Even that was not such a novelty.

Data science is the result of a new paradigm taking place in IT. The question was raised recently, and here I explain how and why data science is part of this new paradigm, and not recycled material. Many data science techniques are very different, if not the opposite of old techniques that were designed to be implemented on abacus, rather than computers. These new tools are often model-free. Indeed, old techniques such as logistic regression and classification trees don't even belong to data science, more stable techniques are used in data science.

We address the problem of generating all possible palindromes from a corpus of Ngrams. Palindromes are texts that read the same both ways. Short palindromes ("race car") usually carry precise, significant meanings. Long palindromes are often less meaningful, but even harder to generate. The palindrome generation problem has never been addressed, to our knowledge, from a strictly combinatorial point of view. The main difficulty is that generating palindromes require the simultaneous consideration of two inter-related levels in a sequence: the "character" and the "word" levels. Although the problem seems very combinatorial, we propose an elegant yet non-trivial graph structure that can be used to generate all possible palindromes from a given corpus of Ngrams, with a linear complexity. We illustrate our approach with short and long palindromes obtained from the Google Ngram corpus. We show how we can control the semantics, to some extent, by using arbitrary text corpora to bias the probabilities of certain sets of words. More generally this work addresses the issue of modelling human virtuosity from a combinatorial viewpoint, as a means to understand human creativity.

This paper presents an interdisciplinary project which aims at cross-fertilizing dance with artificial intelligence. It utilizes AI as an approach to explore and unveil new territories of possible dance movements. Statistical analyzes of recorded human dance movements provide the foundation for a system that learns poses from human dancers, extends them with novel variations and creates new movement sequences. The system provides dancers with a tool for exploring possible movements and finding inspiration from motion sequences generated automatically in real time in the form of an improvising avatar. Experiences of bringing the avatar to the studio as a virtual dance partner indicate the usefulness of the software as a tool for kinetic exploration. In addition to these artistic results, the experiments also raise questions about how AI generally relates to artistic agency and creativity. Is the improvising avatar truly creative, or is it merely some kind of extension of the dancer or the AI researcher? By analyzing the developed platform as a framework for exploration of conceptual movement spaces, and by considering the interaction between dancer, researcher and software, some possible interpretations of this particular kind of creative process can be offered.

Social media data mining and analytics has stimulated a wide array of computational research. Traditionally, individual researchers are responsible for acquiring and managing their own datasets. However, the temporal nature of social data, the challenges involved in correctly preparing a dataset, the sheer scale of many datasets, and the proprietary nature of many data sources can make extending and comparing computational methods difficult and often impossible. In light of this, because replicability is a fundamental pillar of the scientific process and because method comparison is essential to characterizing computational advancements, we require an alternative to the traditional model of researcher-owned datasets. In this paper we propose FREESR, a framework that gives researchers the ability to develop and test method performance without requiring direct access to “shared” datasets. As a case study and first community resource, we have implemented the FREESR paradigm around the task of Tweet geolocation. The implementation showcases the clear suitability of this framework for the social media research context. Beyond the implementation, we see the FREESR paradigm as being an important step towards making study reproducibility and method comparison more principled and ubiquitous in the social media research community.

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.

Editors' note The essay by Alan Turing, which we reproduce here, was written in September 1947, when the world's first stored-program digital computers, to a significant degree his own conceptual creation, were about to become operational. The paper was submitted in 1948 to the National Physical Laboratory, where Turing was then employed, as a report on his year's sabbatical leave which he had spent at Cambridge. During the same period Turing achieved his demonstration of the unsolvability of the word problem for semi-groups with cancellation. A condensed version is to appear in the Collected Works of A.M.Turing which is forthcoming under Dr Gandy's editorship. We also thank Mr Michael Woodger, who incidentally helped Turing finish it by drawing the original diagrams, for an unforgettable account of the furore created by Turing at N.P.L. with his prognostications of intelligent machinery: 'Turing is going to infest the countryside' some declared'with a robot which will live on twigs and scrap iron!' The anticipation of the notion of a sub-routine on page 21 and of the device of doing machine problem-solving via theorem-proving algorithms (p. Abstract The possible ways in which machinery might be made to show intelligent behaviour are discussed. The analogy with the human brain is used as a guiding principle. It is pointed out that the potentialities of the human intelligence can only be realized if suitable education is provided. The investigation mainly centres round an analogous teaching process applied to machines. The idea of an unorganized machine is defined, and it is suggested that the infant human cortex is of this nature. Simple examples of such machines are given, and their education by means of rewards and punishments is discussed. I propose to investigate the question as to whether it is possible for machinery to show intelligent behaviour. It is usually assumed without argument that it is not possible. Common catch phrases such as'acting like a machine', 'purely mechanical behaviour' reveal this common attitude. It is not difficult to see why such an attitude should have arisen. Some of the reasons are: (a) An unwillingness to admit the possibility that mankind can have any rivals in intellectual power. This occurs as much amongst intellectual people as amongst others: they have more to lose. Those who admit the possibility all agree that its realization would be very disagreeable.

In particular the technique of heuristic programming is under detailed investigation as a means to the end of applying largescale rently digital computers to the solution of a difficult class of problems cur considered to be beyond their capabilities; namely those problems that seem to require the agent of human intelligence and ingenuity for their solution. It is difficult to characterize such problems further, except, perhaps, plex to remark rather vaguely that they generally involve com vironment.