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Optimization tips and tricks on Azure SQL Server for Machine Learning Services

#artificialintelligence

By using memory-optimized tables, resume features are stored in main memory and disk IO could be significantly reduced. If the database engine server detects more than 8 physical cores per NUMA node or socket, it will automatically create soft-NUMA nodes that ideally contain 8 cores. We then further created 4 SQL resource pools and 4 external resource pools [7] to specify the CPU affinity of using the same set of CPUs in each node. We can create resource governance for R services on SQL Server [8] by routing those scoring batches into different workload groups (Figure.


LEARNING IN RANDOM NETS

Classics (Collection 2)

Reprinted from Information Theory, Fourth London Symposium published by Butterworths, 88 Kingsway, London, W.C.2. MARVIN MINSKY and OLIVER G. SELFRIDGE Lincoln Laboratory*, Massachusetts Institute of Technology INTRODUCTION THE general nature of the problem is that an organism must learn to make the'right', or appropriate, response to its inputs. Typically, the inputs are large amounts of data, so that the machine must learn to recognize the similarities between different inputs which call for the same response, contrasted with the distinctions that call for different responses. The particular machines we are concerned with are random nets. A random net is a large set of similar and simply-acting elements whose attributes and interactive connections may be randomly established.


Report 84-20.pdf

Classics (Collection 2)

Reprinted by permission of the author. Published in the Proceedings of a Symposium on Computers in Medicine, Annual Meeting, California Medical Association, Anaheim, CA., February 1984. Edward H. Shortlitre, M.D., Ph.D. Division of General Internal Medicine, Department of Medicine Stanford University School of Medicine Stanford, California 94305 Alt;iough computing technology is playing an increasingly important role in medicine, systems designed to advise physicians on diagnosis or therapy selection have remained largely experimental to date. Despite diverse research efforts, and a literature on computer-aided diagnosis that has numbered over 1500 references in the last 20 years, clinical consultation programs have failed to achieve wide acceptance. The reasons for attempting to develop such systems are self-evident.


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 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.


Report 77-39.pdf

Classics (Collection 2)

Department of Electrical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, 24601. 1 /Introduction Giving a machine the ability to learn, adapt, organize or repair itself are among the oldest and most ambitious goals of computer science. In the early days of computing, these goals were central to the new discipline called cybernetics [126], [2]. Over the past two decades, progress toward these goals has come from a variety of fields - notably computer science, psychology, adaptive control theory, pattern recognition, and philosophy. Substantial progress has been made in developing techniques for machine learning in highly restricted environments. Each of these programs, however, is tailored to its particular task, taking advantage of particular assumptions and characteristics associated with its domain.


Report 77-25.pdf

Classics (Collection 2)

Atm 11177 Stanford Heuristic Programming Project August 1977 Memo HPP-77-25 Computer Science Department Report No. STAN-CS-77-62I THE ART OF ARTIFICIAL INTELLIGENCE: 1. THEMES AND CASE STUDIES OF KNOWLEDGE ENGINEERING by E. A. Feigenbaum COMPUTER SCIENCE DEPARTMENT School of Humanities and Sciences STANFORD UNIVERSITY THE ART OF ARTIFICIAL INTELLIGENCE: I. Themes and Case Studies of Knowledge Engineering STAN-CS-77-621 Heuristic Programming Project Memo 77-25 Edward A. Feigenbaum Department of Computer Science Stanford University Stanford, California ABSTRACT The knowledge engineer practices the art of bringing the principles and tools of Al research to bear on difficult applications problems requiring experts' knowledge for their solution. The technical issues of acquiring this knowledge, representing it, and using it appropriately to construct and explain lines-of-reasoning, are important problems in the design of knowledge-based systems. Various systems that have achieved expert level performance in scientific and medical inference illuminates the art of knowledge engineering and its parent science, Artificial Intelligence. The views and conclusions in this document are those of the author and should not be interpreted as necessarily representing the official policies, either express or implied, of the Defense Advanced Research Projects Agency of the United States Government. This research has received support from the lollowing agencies: Defense Advanced Research Projects Agency, DAHC 15-73-C-0435; National Institutes of Health, 5R24-RR00612, RR-00785; National Science Foundation, MCS 76-11649, DCR 74-23461; The Bureau of Health Sciences Research and Evaluation, HS-01544.


Report 77 14 Stanford KSL

Classics (Collection 2)

A Model For Learning Systems STAN-CS-77-605 Heuristic Programming Project Memo 77-14 Reid G. Smith, Tom M. Mitchell Richard A. Chestek and Bruce G. Buchanan ABSTRACT A model for learnina systems is presented, and representative Al, pattern recognition, and control systems are discussed in terms of its framework. The model details the functional components felt to be essential for any learning system, independent of the techniques used for its construction, and the specific environment In which it operates. These components are performance element, instance selector, critic, learning element, blackboard, and world model. Consideration of learning system design leads naturally to the concept of a layered system, each layer operating at a different level of abstraction. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either express or implied, of the Defense Advanced Research ...


Using Artificial Neural Networks to Predict the Quality and Performance of Oil-Field Cements

AI Magazine

Inherent batch-to-batch variability, aging, and contamination are major factors contributing to variability in oil-field cement-slurry performance. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure. Our approach involves predicting cement compositions, particle-size distributions, and thickening-time curves from the diffuse reflectance infrared Fourier transform spectrum of neat cement powders. Our research shows that many key cement properties are captured within the Fourier transform infrared spectra of cement powders and can be predicted from these spectra using suitable neural network techniques.