L’intelligence artificielle : vers un futur révolutionnaire ? | unmondemoderne © Roberto Rizzato Il y a de cela quelques centaines de milliers d’années, bien avant que l’électricité ou encore l’automobile ne soit inventé, l’homme se contentait de peu pour vivre. Aujourd’hui dans les pays dit développés, si nous avons soif par exemple, nous n’avons plus à parcourir des kilomètres pour récupérer de l’eau, il nous suffit tout simplement de faire quelques pas, de tendre un verre puis de tourner un robinet. Le progrès, l’innovation pour ainsi dire, joue bien un rôle prépondérant dans l’évolution de notre société parce qu’avant tout elle nous facilite la vie. En nous intéressant aux nouvelles technologies, à l’exemple du téléphone portable plus connu sous le nom de Smartphone, il est amusant de constater qu’en relativement peu de temps, nous avons très vite adopté cet objet dans notre quotidien de tel sorte qu’il fait désormais partie intégrante de notre mode vie, au point que nous en soyons véritablement dépendant. WordPress: J’aime chargement…
Machine learning Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] Machine learning explores the construction and study of algorithms that can learn from and make predictions on data.[2] Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions,[3]:2 rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Overview[edit] Tom M. Types of problems and tasks[edit] History and relationships to other fields[edit] Theory[edit]
HTML HTML or HyperText Markup Language is the standard markup language used to create web pages. HTML is written in the form of HTML elements consisting of tags enclosed in angle brackets (like <html>). HTML tags most commonly come in pairs like <h1>and </h1>, although some tags represent empty elements and so are unpaired, for example <img>. The first tag in a pair is the start tag, and the second tag is the end tag (they are also called opening tags and closing tags). The purpose of a web browser is to read HTML documents and compose them into visible or audible web pages. Web browsers can also refer to Cascading Style Sheets (CSS) to define the look and layout of text and other material. History[edit] The historic logo made by the W3C Development[edit] In 1980, physicist Tim Berners-Lee, who was a contractor at CERN, proposed and prototyped ENQUIRE, a system for CERN researchers to use and share documents. Further development under the auspices of the IETF was stalled by competing interests.
An improved general E-unification method Bachmair, 1987 L. BachmairProof Methods for Equational Theories dissertation, U. of Illinois, Urbana-Champaign (1987) Bachmair et al., 1986 L. Proc. Bachmair et al., 1987 L. Proceedings of CREAS (1987) Dershowitz and Jounnaud, 1991 N. Handbook of Theoretical Computer Science, North-Holland, Amsterdam (1991), pp. 243-320 Dougherty and Johann, 1990 D. M.E. Fay, 1979 M. Proc. Fages and Huet, 1986 F. Theoretical Computer Science, 43 (1986), pp. 189-200 Gallier and Snyder, 1989 J.H. Theoretical Computer Science, 67 (1989), pp. 203-260 Goguen and Meseguer, 1981 J.A. ACM SIG-PLAN Notices (1981) Goguen and Meseguer, 1985 Houston Journal of Mathematics (1985), pp. 307-334 Herbrand, 1971 J. W. Hullot, 1980 J. Proc. Kirchner, 1984 C. Proc. Kirchner, 1985 C. Thèse d'Etat, Université de Nancy I (1985) Kirchner, 1986 C. Proc. Lankford, 1975 D. Tech. Martelli and Montanari, 1982 A. ACM Transactions on Programming Languages and Systems, 4 (1982), pp. 258-282 Martelli et al., 1986 A. Proc. Plotkin, 1972 G. B. Robinson and Wos, 1969
Joël de Rosnay “Intégrer la Complexité est la Clé du Progrès” Patrice van Eersel, Jean-Louis Servan-Schreiber Scientifique et communicant jusqu’au bout des doigts, Joël de Rosnay a enseigné au prestigieux Massachusetts Institute of Technology de Boston (MIT), avant de rejoindre l’institut Pasteur, à Paris. Il s’est passionné, de longue date, pour les nouvelles technologies, la systémique, la prospective. Bien connu pour ses capacités de pédagogue, il a le mérite de savoir intégrer la problématique technologique dans la grande saga de l’évolution, notamment depuis l’émergence de la cybernétique et d’Internet, dont il est l’un des meilleurs connaisseurs en France – il a notamment cofondé le site citoyen AgoraVox. Auteur de nombreux ouvrages de vulgarisation et de prospective, il préside aujourd’hui la société de conseil Biotics International et il est conseiller du président de la Cité des sciences et de l’industrie de la Villette. Clés : Vous aimez vous définir comme un « optipessimiste »… En quoi consiste cette nouvelle culture ? Vaste question que je présentai en trois volets, trois regards.
Science Systematic endeavour to gain knowledge Science is a systematic discipline that builds and organises knowledge in the form of testable hypotheses and predictions about the universe.[1][2] Modern science is typically divided into two or three major branches:[3] the natural sciences (e.g., physics, chemistry, and biology), which study the physical world; and the social sciences (e.g., economics, psychology, and sociology), which study individuals and societies.[4][5] Applied sciences are disciplines that use scientific knowledge for practical purposes, such as engineering and medicine.[6][7][8] While sometimes referred to as the formal sciences, the study of logic, mathematics, and theoretical computer science (which study formal systems governed by axioms and rules)[9][10] are typically regarded as separate because they rely on deductive reasoning instead of the scientific method or empirical evidence as their main methodology.[11][12][13][14] Etymology History Early history Middle Ages Awards
Category:Image processing Image processing is the application of signal processing techniques to the domain of images — two-dimensional signals such as photographs or video. Image processing does typically involve filtering an image using various types of filters. Related categories: computer vision and imaging. Subcategories This category has the following 13 subcategories, out of 13 total. Pages in category "Image processing" The following 200 pages are in this category, out of 213 total. (previous 200) (next 200)(previous 200) (next 200) Algorithm Flow chart of an algorithm (Euclid's algorithm) for calculating the greatest common divisor (g.c.d.) of two numbers a and b in locations named A and B. The algorithm proceeds by successive subtractions in two loops: IF the test B ≥ A yields "yes" (or true) (more accurately the numberb in location B is greater than or equal to the numbera in location A) THEN, the algorithm specifies B ← B − A (meaning the number b − a replaces the old b). Similarly, IF A > B, THEN A ← A − B. The process terminates when (the contents of) B is 0, yielding the g.c.d. in A. In mathematics and computer science, an algorithm ( i/ˈælɡərɪðəm/ AL-gə-ri-dhəm) is a step-by-step procedure for calculations. Informal definition[edit] While there is no generally accepted formal definition of "algorithm," an informal definition could be "a set of rules that precisely defines a sequence of operations Boolos & Jeffrey (1974, 1999) offer an informal meaning of the word in the following quotation: Formalization[edit]
Artificial intelligence AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence is still among the field's long-term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. History[edit] Research[edit] Goals[edit] Planning[edit] Logic-based
Philosophy Philosophy is the study of general and fundamental problems, such as those connected with reality, existence, knowledge, values, reason, mind, and language.[1][2] Philosophy is distinguished from other ways of addressing such problems by its critical, generally systematic approach and its reliance on rational argument.[3] In more casual speech, by extension, "philosophy" can refer to "the most basic beliefs, concepts, and attitudes of an individual or group".[4] The word "philosophy" comes from the Ancient Greek φιλοσοφία (philosophia), which literally means "love of wisdom".[5][6][7] The introduction of the terms "philosopher" and "philosophy" has been ascribed to the Greek thinker Pythagoras.[8] Areas of inquiry Philosophy is divided into many sub-fields. Epistemology Epistemology is concerned with the nature and scope of knowledge,[11] such as the relationships between truth, belief, and theories of justification. Rationalism is the emphasis on reasoning as a source of knowledge. Logic
Data (computing) In an alternate usage, binary files (which are not human-readable) are sometimes called "data" as distinguished from human-readable "text".[4] The total amount of digital data in 2007 was estimated to be 281 billion gigabytes (= 281 exabytes).[5][6] At its heart, a single datum is a value stored at a specific location. To store data bytes in a file, they have to be serialized in a "file format". Typically, programs are stored in special file types, different from those used for other data. Keys in data provide the context for values. Computer main memory or RAM is arranged as an array of "sets of electronic on/off switches" or locations beginning at 0. Data has some inherent features when it is sorted on a key. Retrieving a small subset of data from a much larger set implies searching though the data sequentially. The advent of databases introduced a further layer of abstraction for persistent data storage.
E-unification Previous: Equational term rewrite systems Next: Quasi-identity logic Up: Supplementary Text Topics Just as unification plays a crucial role in the study of term rewrite systems (see Chapter III of LMCS), one has E-unification for work with ETRS's. Indeed the equational theorem prover EQP that William McCune used to verify the Robbin's Conjecture (discussed at the end of Chapter III of LMCS) uses AC-unification. In the following E will denote a set of equations. PROOF.\ (Exercise.) One can no longer assume that an E-unifiable pair has a most general E-unifier -- this depends on the choice of E. There is an E-unifier µ of s and t which is more general than any E-unifier of s and t. This leads to the following unification types: Using this we can give the unification types for sets of equations E: E is if every E-unifiable s,t is unitary E is if every E-unifiable s,t is unitary or finitary, and some E-unifiable s,t is finitary. Given a term s in (which is actually a coset of terms). PROOF. .
Netflix Netflix, Inc. is a provider of on-demand Internet streaming media available to viewers in North and South America, the Caribbean, and parts of Europe (Denmark, Finland, Ireland, The Netherlands, Norway, Sweden, United Kingdom, France, Switzerland, Austria, Belgium, and Germany),[4] and of flat rate DVD-by-mail in the United States, where mailed DVDs are sent via Permit Reply Mail. The company was established in 1997 and is headquartered in Los Gatos, California. It started its subscription-based service in 1999. By 2009, Netflix was offering a collection of 100,000 titles on DVD and had surpassed 10 million subscribers.[5] As of September 2014, Netflix has subscribers in over 40 countries, with intentions of expanding their services in unreached countries (such as New Zealand).[6] History[edit] Netflix headquarters in Los Gatos. In 2000, Netflix was offered for acquisition to Blockbuster for $50 million, however Blockbuster declined the offer. On December 24, 2012, at around 1:00 p.m.