Systems, Man & Cybernetics - April 2016 - 8

Input

Level

Complexity

Sensory Data +
Human Knowledge

particularly concerned about the way
many writers spoke about NNs, as seen
in the follow ing quote.

Seductive Semantics in
Science and Engineering
∪
∪
∪
Another objective [of the paper] conSensor Data +
cerns the use of "seductive semantics";
A ~ Symbolic
ANN ⊂ APR ⊂
AI
Knowledge Tidbits (KT)
that is, words or phrases which convey,
∪
∪
∪
by being interpreted in their ordinary
Sensor Data +
(non-scientific) usage, a far more
C ~ Numeric
CNN ⊂ CPR ⊂ CI
Computation
profound and substantial meaning about
the performance of an algorithm or computational architecture than can be readFigure 1. the abCs: neural networks, pattern recognition, and
intelligence [3].
ily ascertained from the available theoretical and/or empirical evidence. Examples of seductive phrases include words
such
as:
neural,
self-organizing, machine learning,
To my knowledge, this was the first article that
adaptive, cognitive. [3]
proposed a (somewhat loose) definition of the term CI.
The phraseology and semantics of computation that
The meaning I intended for the term CI in [3] has been
I was attempting to capture and discuss were exhibited in
analyzed, ridiculed, supported, criticized, lionized, and
[3, Table 1] and repeated here as Table 1.
so on, almost ad infinitum (or should it be ad nauseam?).
Has the debate about the relationship between NNs and
See [3, Figure 1].
(human) intelligence abated in the 20+ years since this was
The abbreviations in Figure 1 are NN = neural network,
written? Hardly. Ray Kurzweil (the strong AI guy), Google's
PR = pattern recognition, I = intelligence. Earlier in [3] I
engineering director, recently affirmed his skepticism about
had posted my definition of the ABCs:
what NNs can really achieve. In October 2014, commenting
◆ A = Artificial-nonbiological (man-made)
on research being performed at the Institute of Deep
◆ B = Biological-physical + chemical + (??) = organic
Learning in Beijing, he was quoted as saying, "There's more
◆ C = Computational-mathematics + man-made machines.
to human intelligence than just finding patterns" [14].
CPR, for example, stands here, with no pun intended,
Does the term AI have a wider approval rating than
for computational pattern recognition. And so on. I meant
NNs? Well, Richard Feynman had this to say about
for the inclusion symbols (both horizontally and
it in 1985:
vertically) in Figure 1 to be taken quite literally. In
There is a great deal of work to try to develop smarter
particular, I show CI as a SUBSET of AI. I believed this to
machines...This often goes under the name of artifibe the case in 1992, and I still believe it today. You can see
cial intelligence, but I don't like that name. Perhaps
from the abstract above that my main focus was on
the unintelligent machines can do even better than the
computational neural networks (CNNs) a nd their
intelligent ones. [15]
relationship to AI, and more generally, to BI. I was
BPR ⊂

BI

B ~ Organic

Complexity

BNN ⊂

Table 1. Defining the ABCs [3].

8

BNN

Hardware: The BRAIN processes your sensory inputs

Sensory data processing; how does it work?

ANN

CNN (+) KTs process sensor inputs and KTs in the style
of the brain

Intermediate level processing. More than adaptivity, fault
tolerance, and so forth. A human is always in the loop.

CNN

Biologically inspired models process sensor inputs in the
style of the brain

Low-level sensor data processing.

BPR

Search for structure in sensory data

We are really good at it: how does it work?

APR

CPR (+) KTs

Intermediate level data processing that utilizes KTs (more
than sensor data).

CPR

Search for structure in numerical data

This includes almost all NN procedures.

BI

Software: The MIND

Cognition, memory, action: how does it work?

AI

CI (+) KTs

Intermediate level cognition in the style of the mind.

CI

Low-level information analysis

Low-level cognition in the style of the mind.

IEEE SyStEmS, man, & CybErnEtICS magazInE A pri l 2016



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