If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) trained a neural network to recognize materials (e.g., metal grate, plants, concrete sidewalk) being hit with a drumstick, and synthesize sounds to accompany the actions. It did well enough to fool humans into thinking the sounds were real. Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. In this paper, we propose the task of predicting what sound an object makes when struck as a way of studying physical interactions within a visual scene.
For a generation that has been exposed to the Terminator movies, visions of a robot uprising come to mind whenever news about advancements in artificial intelligence surface. Great minds such as Tesla Motors and SpaceX CEO Elon Musk, famed astrophysicist Stephen Hawking and Apple co-founder Steve Wozniak have previously expressed their concern on the possibility of a robot apocalypse. It would seem that Google, one of the companies at the forefront of artificial intelligence development, is now sharing some of these concerns, as its DeepMind unit has published a study that seeks to implement safety measures on the technology. The paper, published as a collaboration between DeepMind and the Future of Humanity Institute of Oxford University, discusses a "big red button" that will allow humans to turn off artificial intelligence in a robot and take control of it in case the robot is misbehaving or malfunctioning. And just so it is clear, the Future of Humanity Institute is named as such as it wants humanity to have a future, with Nick Bostrom, its founding director, being one of the more vocal opponents of artificial intelligence.
A general game-playing program must know the rules of the particular playing game. These rules are: (1) an algorithm indicating the winning state; (2) an algorithm enumerating legal moves. A move gives a set of changes from the present situation. There are two means of giving these rules: (1) We can write a subroutine which recognizes if we have won and another which enumerates legal moves. Such a subroutine is a black box giving to the calling program the answer: 'you win' or'you do not win', or the list of legal moves.
Rote learning.We can keep all the situations already found. With each situation we store an indication on its interest or the move which has to be played. Samuell gives an example of such an application. This can be done if there are not too many possible situations. Even in games where there are many possible situations, this method can be useful for the beginning or the end of the games.
This paper compares two sources of advice for forecasting of severe thunderstorms: an expert system (WILLARD) and government-issued severe weather outlooks. WILLARD was constructed by a meteorologist using the RuleMaster expert system building facility, which features rule induction from examples of expert decision-making. The forecast comparisons are presented in terms of statistical properties: the Probability of Detection, the False Alarm Rate, and the Critical Skill Index. Even though WILLARD was developed as a demonstration system, its forecasting accuracy on major severe weather days is comparable to government-issued forecasts for the validation period. By examining the results of the comparison, deficiencies in WILLARD were identified that can be rectified in future versions, thereby increasing WILLARD'S store of weather knowledge.
We consider a class of problems of formation type where the goal is to construct by computer a program -- in a given programming language -- that satisfies a finite number of conditions in the form of given input-output correspondences. A variety of automatic design problems, as well as problems of theory construction in empirical sciences, are of this general type. The main problems in the design of procedures for the solution of formation problems are: selection of an'appropriate' grammar for specifying the language in which candidate solutions are to be represented; formulation of evaluation procedures for ordering candidate solutions; and formulation of control strategies for generating'intelligently' a sequence of candidate solutions which converges efficiently to the desired solution. The problems of representation, evaluation, and control are closely coupled; however, dominant among them is the problem of representation. A promising approach to the choice of a grammar for the solution language is to find a model corresponding to the grammar, for which it is moderately easy to evaluate the relative merit of candidate solutions (in terms of their satisfying the given problem conditions) by analyzing their representations in the model.
A computer program has been written which can formulate hypotheses from a given set of scientific data. The data consist of the mass spectrum and the empirical formula of an organic chemical compound. The hypotheses which are produced describe molecular structures which are plausible explanations of the data. The hypotheses are generated systematically within the program's theory of chemical stability and within limiting constraints which are inferred from the data by heuristic rules. The program excludes hypotheses inconsistent with the data and lists its candidate explanatory hypotheses in order of decreasing plausibility.
Problems related to an inadequate data base of interpretation rules. The same set of production rules can suggest possible structural interpretations of 13C spectral features. Any individual 13C feature permits a great variety of st,:uctural interpretations. This paper presents an "expert system" devised to aid organic chemists in determining the structure (i.e. the arrangement of atoms and bonds) of newly isolated, naturally occurring compounds. The system exploits a data base of rules for analyzing.013
Knowledge engineering is still more of an art than a science. In the list presented here, we have tried to capture the art as it exists at the beginning of the 1980's. Many of his heuristics still apply.) 'David Barstow is a member of the Schlumberger-Doll Research Laboratory, Ridgefield, CT. This memo is also being published by SDR as Al Memo number 10. 1 In the course of building expert systems, knowledge engineers have developed intuitions about how best to proceed, heuristics to keep in mind when building a system for a particular task.
James G. Nourse, 1979 card 1 of 1 HPP 78-8 Reprinted with permission from the Journal of the America., Abstract: The configuration symmetry group, a novel specification of the symmetry of an organic chemical structure of defined constitution, is formulated. The symmetry operations in this group are represented in part by their effects on the configurations of the stereocenters in the structure. Introduction A number of ways exist for specifying the symmetry of a chemical structure, each suitable for different purposes. For most applications the familiar geometric point group is chosen .22 In some spectroscopic applications It is necessary to take internal motion into account and specify a nonrigid symmetry group.2b For applications in dynamic stereochemistry it is necessary to consider the group of all permutations of identical atoms and often several subgroups.3 Symmetry groups that include the point group and operations that invert chiral centers are useful both in constructing ...