Meaning starts with the concept, which is in somebody’s mind, and only there: it is ineffable. ... An operational definition puts communicable meaning into a concept. Adjectives like good, reliable, uniform, round, tired, safe, unsafe, unemployed have no communicable meaning until they are expressed in operational terms of sampling, test, and criterion.
Testable Thinking
Transforming vague concepts into testable frameworks.
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In each of these cases, whether writing the value of pi or shooting at a target, you achieve accuracy when the accumulation of results is close to the desired value, which in these examples is either the true value of the constant or the center of the target. Precision, by contrast, is attained when the accumulated results are similar to one another, when the shooting attempt is achieved many times with exactly the same outcome—even though that outcome may not necessarily reflect the true value of the desired end. In summary, accuracy is true to the intention; precision is true to itself.
To avoid philosophical discussions about what ‘mind’ or ‘thought’ or ‘free will’ were supposed to be, he favoured the idea of judging a machine’s mental capacity simply by comparing its performance with that of a human being. It was an operational definition of ‘thinking’, rather as Einstein had insisted on operational definitions of time and space in order to liberate his theory from a priori assumptions.
A scientific hypothesis is tested by comparing its conclusions with the reality of the world as it is. Yet, no matter how many examples of agreement one may collect, they do not prove the truth of the hypothesis, for it may be argued that one has not tested it in the single case where the theory may fail to agree with reality. On the other hand, just one instance of disagreement between the hypothesis and reality is sufficient to make the hypothesis incontrovertibly false.
Other thought experiments are less rigorous but often just as effective: little stories designed to provoke a heartfelt, table-thumping intuition—“Yes, of course, it has to be so!”—about whatever thesis is being defended. I have called these intuition pumps. ... They are the philosophers’ version of Aesop’s fables, which have been recognized as wonderful thinking tools since before there were philosophers.
The clarification chain is just a short series of connections that should bring us from thinking of something as an intangible to thinking of it as tangible. First, we recognize that if X is something that we care about, then X, by definition, must be detectable in some way. ... If we can observe it in some amount, then it must be measurable.
What Fermi understood is that by breaking down the question, we can better separate the knowable and the unknowable. ... But we have brought our guessing process out into the light of day where we can inspect it. And the net result tends to be a more accurate estimate than whatever number happened to pop out of the black box when we first read the question.
Humans must have some compact representation of the information needed in their brains, as well as an effective procedure to interpret each question properly and extract the right answer from the stored representation. To pass the mini-Turing test, therefore, we need to equip machines with a similarly efficient representation and answer-extraction algorithm. Such a representation not only exists but has childlike simplicity: a causal diagram.