letters to the editor
AWARDING ACM’S 2017 A.M. Turing Award to John Hennessy and David Pat- terson was richly deserved and long overdue, as de-
scribed by Neil Savage in his news sto-
ry “Rewarded for RISC” (June 2018).
RISC was a big step forward. In their
acceptance speech, Patterson also
graciously acknowledged the contemporary and independent invention
of the RISC concepts by John Cocke,
another Turing laureate, at IBM, as
described by Radin. 1 Unfortunately,
Cocke, who was the principal inventor but rarely published, was not included as an author, and it would
have been good if Savage had mentioned his contribution.
It is noteworthy that RISC architectures depend on and emerged
from optimizing compilers. So far as
I can tell, all the RISC inventors had
strong backgrounds in both architecture and compilers.
Reference
1. Radin, G. The 801 minicomputer. IBM Journal of
Research & Development (1983), 237–246.
Fred Brooks, Chapel Hill, NC, USA
No Inconsistencies in Fundamental
First-Order Theories in Logic
Referring to Martin E. Hellman’s Turing Lecture article “Cybersecurity, Nuclear Security, Alan Turing, and Illogical
Logic” (Dec. 2017), Carl Hewitt’s letter
to the editor “Final Knowledge with
Hennessy and Patterson
on the Roots of RISC
DOI: 10.1145/3273019
It is noteworthy that
RISC architectures
depend on and
emerged from
optimizing compilers.
Certainty Is Unobtainable” (Feb. 2018)
included a number of misleading statements, the most important that: “
Meanwhile, Gödel’s results were based on
first-order logic, but every moderately
powerful first-order theory is inconsistent. Consequently, computer science
is changing to use higher-order logic.”
Computer science is based on logic,
mostly first-order logic, and programmers make their coding decisions using logic every day. The most important
results of logic (such as Kurt Gödel’s
Incompleteness Theorems) are taught
in theory courses and are the fundamentals on which computer science
and software engineering are based. No
inconsistencies have ever been found in
any of the standard first-order theories
used in logic, ranging from moderately
powerful to very powerful, and none are
believed to be inconsistent.
Harvey Friedman, Columbus, OH, USA,
and Victor Marek, Lexington, KY, USA
Author Responds:
Powerful first-order theories of intelligent
information systems are inconsistent
because these systems are not compact,
thus violating a fundamental principle
of first-order theories. Meanwhile, the
properties of self-proof of inferential
completeness and formal consistency in
higher-order mathematical theories are
the opposite of incompleteness and the
self-unprovability of consistency Gödel
showed for first-order theories. Differing
properties between higher-order and
first-order theories are reconciled by
Gödel’s “I’mUnprovable” proposition’s
nonexistence in higher-order theories.
First-order theories are not foundational
to computer science, which indeed relies
on the opposite of Gödel’s results.
Carl Hewitt, Palo Alto, CA, USA
More Accurate Text Analysis
for Better Patient Outcomes
David Gefen et al.’s article “Identifying
Patterns in Medical Records through
Computer Vision
February 4 – May 10, 2019
Organizing Committee:
Y. Amit, University of Chicago
R. Basri, Weizmann Institute
A. Berg, University of NC
T. Berg, University of NC
P. Felzenszwalb, Brown Univ.
B. Fux Svaiter, IMPA
S. Geman, Brown University
B. Gidas, Brown University
D. Jacobs, University of MD
O. Veksler, Univ of W. Ontario
Program Description:
Computer vision is an
inter-disciplinary topic
crossing boundaries
between computer science,
statistics, mathematics,
engineering, and cognitive
science. Research in
computer vision involves
the development and
evaluation of computational methods for image
analysis.
The focus of the program
will be on problems that
involve modeling, machine
learning and optimization.
The program will also
bridge a gap between
theoretical approaches and
practical algorithms,
involving researchers with
a variety of backgrounds.
Associated Workshops:
• Theory and Practice
in Machine Learning
and Computer Vision
(February 18 - 22, 2019)
• Image Description for
Consumer and
Overhead Imagery
(February 25 - 26, 2019)
• Computational Imaging
(March 18 - 22, 2019)
• Optimization Methods in
Computer Vision and
Image Processing
(April 29 - May 3, 2019)
icerm.brown.edu
Brown University
121 S. Main Street, 11th floor
Providence, RI 02903
info@icerm.brown.edu
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ComputerVision.CACM Ad.indd 1 8/3/18 2: 55 PM