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Preparing the Network for AI and Machine Learning - insideBIGDATA

#artificialintelligence

Other organizations can leverage business data to drive data-informed project management, allowing business leaders to more accurately determine how long certain operations may take and will cost. The fundamentals of these technologies are rooted in data-driven algorithms that enable machines to develop learned responses or predictive capabilities. As a result, with AI and machine learning comes data--big data--that requires resources to be allocated, not only specialists like programmers, but additional on-premises resources such as storage, server CPUs, networking bandwidth, and cloud-hosted storage services. As businesses look to develop their digital transformation strategies and create unique competitive advantage, AI and machine learning are increasingly considered the keys to unlocking the value of an organization's accumulated data.



Report 83-20.pdf

Classics (Collection 2)

Jeffrey S. Rosenschein and Vineet Singh Heuristic Programming Project Computer Science Department Stanford University Stanford, CA 94305 Abstract Meta-level control, in an Artificial Intelligence system, can provide increased capabilities This improvement, however, is achieved at the cost of the meta-level effort itself. This paper outlines a formalization of the costs involved in choosing between independent problem-solving methods: the cost of meta-level control is explicitly included. It is often desirable for Artificial Intelligence systems to make use of explicit knowledge about what they know; this tneta-level knowledge allows a program to direct its own activities in an informed and efficient manner [I] [21. The use of meta-level knowledge by a system to control its own actions is called'new-level confrol. If we are to gain efficiene; thi-migh the use of meta-level effort, \'.e must be sure that %'.hat is aved at the base level is not canceled by what is expended at the rnota-level.


Report 82-37.pdf

Classics (Collection 2)

Heuristic Programming Project Report No. HPP 82-37 May 1982 COMPUTER-BASED CLINICAL DECISION AIDS: SOME PRACTICAL CONSIDERATIONS Edward H. Shortliffe, MD, PhD Division of General Internal Medicine Department of Medicine Stanford University School of Medicine Stanford, California 94305 To be presented at AMIA Congress Hyatt on Union Square San Francisco, California 2-5 May 1982 * Dr. Shortliffe is recipient of Research Career Development Award LM00048 from the National Library of Medicine. AMIA Congress 82 E.H. Shortliffe AKTRACT Medical decision making research has tended to emphasize the generation of optimal decisions, an issue which is central to the development of clinically useful consultation programs. This paper stresses the need to consider other theoretical and practical issues that are pertinent if consultation systems are to be accepted by physicians. Since adequate decision making performance remains an essential component of acceptable systems, the paper suggests c-iteria for selecting clinical problems that may be amenable to short-term implementation using state-of-the-art techniques. Introducticn At the beginning of a third decade of research into the development of computer-based diagnostic aids, it is appropriate for medical computer scientists to assess the strides that have been taken, the barriers that remain, and the optimal strategies for furthering the field in the years ahead.


Report 81 09 Stanford KSL

Classics (Collection 2)

Edward H. Shortliffe, Jul 1981 HP? -81 -9 EVALUATINq EXPERT SYSTEMS Edward H. Shortliffe Heuristic Programming Project Departments of Medicine and Computer Science Stanford University Stanford, California 94305 July 1981 This paper is the author's contribution to Chapter 6. in the volume EXPERT SYSTEMS, edited by R. Hayes-Roth, D. Lenat, and D. Waterman:4 The full article is entitled "Evaluation of expert systems: issues and case studies", and is authored by J. Gaschnig, P. Klahr, H. Pople, E. Shortliffe. The volume is the result of a Workshop on Expert Systems held in San Diego in August 1980 and sponsored by the Rand Corporation, ARPA, and the NSF. Parts of Chapters 7 & 8. Reprinted with permission. Issues in the Evaluation of Expert Systems EVALUATING EXPERT SYSTEMS 1 Issues in the Evaluation of Expert Systems 4e have been discussing the reasons for doing evaluations of expert systems, or for having reservations about getting involved in the evaluation process, but we have not addressed the nature of the evaluation process itself. In this section we define ma,ly of the parameters that determine an appropriate design for an evaluation experiment.


Report 80-30.pdf

Classics (Collection 2)

Reprinted by permission of the Canadian Society for Computational Studies of Intelligence. Reprinted frcm: Proceedings of the CSCSI/SCEIO Conference 14-16 May 1980 University of Victoria Victoria, British Columbia pp. California 94305 ABSTFACT Computer systems for use by physicians have had limited impact on clinical medicine. The mYCIN System is used to illustrate tne ways In which Our research group has attempted to respond to the design criteria cited. My goal is to present design criteria which may encourage the use of computer programs oy physicians, and to show that Al offers some particularly pertinent methods for responding to the design criteria outlined.


Report 80-14.pdf

Classics (Collection 2)

Despite diverse research efforts, and a literature on computer-aided diagnosis that has numbered at least 1,000 references in the last 20 years, clinical consultation programs have seldom been used other than in experimental environments. The reasons for attempting to develop such systems are self-evident. Growth in medical knowledge has far surpassed the ability of the single practitioner to master it all, and the computer's superior information poacessing capacity thereby offers a natural appeal. Furthermore, the reasoning processes of medical experts are poorly understood; attempts to model expert decision making necer.sarily New insights that result may also allow us more adequately to teach medical students and house staff the techniques for reaching good decisions, rather than merely to offer a collection of facts that they must independently learn to use coherently.


Report 79-20.pdf

Classics (Collection 2)

Reprinted, with permission from HPP 79-20 Proceedings of the IEEE, Vol.67, No.9, pp.1207-1224, September 1979. These include I) clinical algorithms. It is noted that no one method is best for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge engineering research particularly attractive. Since that time a variety of techniques have been applied, accounting for at least 800 references in the clinical ard computing literature [1121.


Report 78-27.pdf

Classics (Collection 2)

These include I) clinical algorithms, 2) clinical databanks that include analytic functions, 3) mathematical models of physical processes, 4) pattern recognition, 5) Bayesian statistics, 6) decision analysis, and 7) symbolic reasoning or artificial intelligence. Because the techniques used in the various systems cannot be examined exhaustively, the case studies in each category are used as a basis for studying general strengths and limitations. It is noted that no one method is best for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge engineering research particularly attractive. We stress that consid-Manuscript received December 13, 1978: revised February 20, 1979.


Submitted to MEDINF0.77

Classics (Collection 2)

Rule Based Medical Decision Making by Computer Bruce G. Buchanan, Randall Davis, Victor Yu, and Stanley Cohen(1) Stanford University Stanford, California, U.S.A. A new approach to medical decision making by computers is summarized. Individual inference rules-- both definitional and judgmental --are incorporated in a symbolic reasoning program, named MYCIN, that is designed to provide assistance to physicians regarding appropriate therapy for infectious diseases. Because of the structure of the knowledge base, the program can explain its reasoning and can easily integrate new items of information about the domain. Results of a formal study show that the program provides good advice most of the time, as judged by ten experts in the field. 1 BACKGROUND A number of recent studies indicate a major need to improve the quality of antimicrobial therapy.