This previous-generation machine still chugs along, and you'll save a bunch of cash. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. If you need a lightweight laptop with some gaming chops, last year's model of the Asus TUF Gaming A14 (8/10, WIRED Recommends) is currently marked down to just $900 at Walmart . This budget-friendly laptop was already a good choice at its original price, and is even more appealing when it's discounted to under $1,000.

Stellantis is the world's fifth largest automaker with a stable of more than a dozen North American and European brands including Jeep, Ram, Dodge, Maserati and Fiat. The company has set extremely ambitious goals to drastically reduce its carbon footprint by the end of the decade, as part of Dare Forward 2030, with 100 percent of its European sales and half of its US sales to be of the fully-electric variety. As part of that effort, Stellantis has already teased us with an EV concept from Chrysler, came right out and showed us next year's electric Jeep and, on Thursday, unveiled the Ram 1500 Revolution BEV Concept. The Ram 1500 BEV will be available in the 2024 model year alongside the as-of-yet-unnamed Jeep (with which it also shares a STLA EV frame). The Concept shown off Thursday will serve as a design template for the upcoming production vehicle.

His proofs are ingenious, cleverly argued, quite convincing to many of his contemporaries, and utterly wrong. The Simon Newcomb Award is given annually for the silliest published argument attacking AI. Our subject may be unique in the virulence and frequency with which it is attacked, both in the popular media and among the cultured intelligentsia. Recent articles have argued that the very idea of AI reflects a cancer in the heart of our culture and have proven (yet again) that it is impossible. While many of these attacks are cited widely, most of them are ridiculous to anyone with an appropriate technical education.

Analogy has a rich history in Western civilization. Over the centuries, it has become reified in that analogical reasoning has sometimes been regarded as a fundamental cognitive process. In addition, it has become identified with a particular expressive format. The limitations of the modern view are illustrated by monster analogies, which show that analogy need not be regarded as something having a single form, format, or semantics. Analogy clearly does depend on the human ability to create and use well-defined or analytic formats for laying out propositions that express or imply meanings and perceptions.

Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures We propose that there exist different problem-solving types, i e, uses of knowledge, and corresponding to each is a separate substructure specializing in that1 type of problem-solving Each substructure is in turn further decomposed into a hierarchy of specialists which differ from each other not in the type of problem-solving, but in the conceptual content of their knowledge; e g, one of them may specialize in "heart disease," while another may do so in "liver," though both of them are doing the same type of problem-solving Thus ultimately all the knowledge in the system is distributed among problem-solvers which know how to use that knowledge This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds In our framework there is no distinction between knowledge bases and problem-solvers: each knowledge source is a problem-solver We have so far had occasion to deal with three generic problem-solving types in expert clinical reasoning: diagnosis (classification), data retrieval and organization, and reasoning about consequences of actions In a novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them This is a revised and extended version of an invited talk entitled, "Decomposition of Domain Knowledge Into Knowledge Sources: The MDX Approach," delivered at the IV National Conference of the Canadian Society for Computational Studies of Intelligence, May 17-19, 1982, Saskatchewan For the past few years our research group has been investigating the issues of problem-solving as well as knowledge organization and representation in medical decision making. In parallel with this investigation we have also been building and extending a cluster of systems for various aspects of medical reasoning. MDX, which is a diagnostic system, i.e., its role is to arrive RADEX is a Though in a sense RADEX and PATREC can both be viewed as "intelligent" data base specialists, RADEX has some additional features of interest due to the perceptual nature of some of its knowledge. However, for the purpose of this paper, it is not necessary to go into RADEX in much detail, and we can view PATREC as prototypical of this class of auxiliary systems. Our aim in this paper is to outline a point of view about how a domain gets naturally decomposed into substructures each of which specializes in one type of problem-solving.

Editor: Having worked on real-time expert systems for several years, I read with interest the survey article "Real-Time Knowledge-Based Systems" by Thomas Laffey, et al. in the recent edition of AI Magazine. Indeed, the work of myself and my colleagues was mentioned, but the description of YES/L1 in the article was incorrect in several ways. Laffey and his coauthors incorrectly describe YES/L1 as being "implemented in OPS5 and MacLisp " They seem to be confusing YES/Ll, the shell, with YES/MVS was written in OPS5. YES/L1 is a compiled, data-driven language and an environment for developing interactive and real-time expert systems. The YES/L1 language is an integration of procedural and rule-based techniques.

This article is a sequel to an article titled "A New Direction in AI--Toward a Computational Theory of Perceptions," which appeared in the Spring 2001 issue of AI Magazine (volume 22, No. 1, 73-84). The concept of precisiated natural language (PNL) was briefly introduced in that article, and PNL was employed as a basis for computation with perceptions. In what follows, the conceptual structure of PNL is described in greater detail, and PNL's role in knowledge representation, deduction, and concept definition is outlined and illustrated by examples. What should be understood is that PNL is in its initial stages of development and that the exposition that follows is an outline of the basic ideas that underlie PNL rather than a definitive theory. A natural language is basically a system for describing perceptions.

One of the major problems in developing so-called intelligent computer-aided design (CAD) systems (ten Hagen and Tomiyama 1987) is the representation of design knowledge, which is a two-part process: the representation of design objects and the representation of design processes. We believe that intelligent CAD systems will be fully realized only when these two types of representation are integrated. Progress has been made in the representation of design objects, as can be seen, for example, in geometric modeling; however, almost no significant results have been seen in the representation of design processes, which implies that we need a design theory to formalize them. According to Finger and Dixon (1989), design process models can be categorized into a descriptive model that explains how design is done, a cognitive model that explains the designer's behavior, a prescriptive model that shows how design must be done, and a computable model that expresses a method by which a computer can accomplish a task. A design theory for intelligent CAD is not useful when it is merely descriptive or cognitive; it must also be computable.

In particular, we focus on issues of ontology integration and the related problem of semantic mapping, that is, the mapping of ontologies and taxonomies to reference ontologies to preserve semantics. Along the way, we discuss what typically constitutes an ontology architecture. By its very nature, B2B e-commerce must try to interlink buyers and sellers from multiple companies with disparate product-description terminologies and meanings, thus serving as a paradigmatic case for the use of ontologies to support corporate applications. Commercial organizations are seeking to codify web services using such formalizations as the universal description, discovery, and integration (UDDI) specification. There are efforts to standardize intelligent agent technology, such as the Foundation for Intelligent Physical Agents (FIPA). These efforts at standardization must use ontologies if emerging internet applications are to be powered by semantics, the meaning behind advanced applications and their enterprise-level and community-level transactions. In this article, we discuss some issues that arise when ontologies are used to support corporate application domains such as electronic commerce (e-commerce) and some technical problems in deploying ontologies for realworld use. In particular, we focus on issues of ontology integration and the related problem of semantic mapping, that is, the mapping of ontologies and taxonomies to reference ontologies to preserve semantics. Along the way, we discuss what typically constitutes an ontology architecture and provide a short summary of ontology development tools. By its very nature, B2B e-commerce must try to interlink buyers and sellers from multiple companies with disparate product-description terminologies and meanings, thus serving as a paradigmatic case for the use of ontologies to support corporate applications. The "vocabularies" for ontologies, as discussed in the introduction to this special issue, are distinct at different levels.

MetLife processes over 260,000 life insurance applications a year. Underwriting of these applications is labor intensive. Automation is difficult because the applications include many free-form text fields. The application contains questions that can be answered by structured data fields (yes-no or pick lists) as well as questions that require free-form textual answers. Currently, MetLife's Individual Business Personal Insurance unit employs over 120 underwriters and processes in excess of 260,000 life insurance applications a year.