Over the last three years, few topics in enterprise technology (or, for that matter, in the
larger culture) have taken on more significance than artificial intelligence. It would be
virtually impossible to avoid bumping into articles about AI, even if you tried. While the
range of AI-related topics worth addressing is vast, let us narrow our scope to the inter-
ests of our enterprise technology audience, and to developments over the two years or
so since we devoted an entire issue of The Doppler to AI.
First, a very short reminder about what we mean when we talk about AI in the context
of business. Virtually all AI that is useful in business is “narrow”: AI trained to perform a
specifically targeted task, such as translating between two languages, or detecting a
particular type of cancer in X-rays. In fact, the more narrowly targeted the focus of an AI
model, the more accurate it tends to be. Narrow AI contrasts with artificial general intel-
ligence (AGI), whose goal is to create a general, human-like intelligence that is broadly
applicable to a virtually unlimited range of cognitive tasks. While AGI is a subject of deep
interest and vast potential, it is nearly irrelevant for business in the near- to mid-term,
due to its relative immaturity.
Let us further point out that the terms “AI” and “machine learning (ML)” are defined dif-
ferently by different people. Due in part to this confusion, these terms are often used
interchangeably in business contexts. Rather than dive into this debate, in this article we
will refer to AI and ML synonymously and collectively, while recognizing there are diver-
gent opinions.
WIth that out of the way, what has changed in the world of business AI in the last couple
of years, and what lessons should we be learning as a result?
Accuracy
By any measure, AI technologies have gotten considerably better performing and more
accurate in the last few years. Because of AI’s direct value to business, large numbers of
researchers at leading universities and commercial firms are constantly innovating and
iterating, developing new algorithms, frameworks, techniques and toolsets. Because
increased accuracy and performance can translate into significant prestige and profits,
these researchers are provided with plenty of resources. The net result is a remarkable
rate of progress, as benchmarks for every conceivable way of measuring capabilities are
continually tested. One of the best known examples of this is the ImageNet Competition,
in which a massive standardized set of images has been used over approximately a sev-
en-year period as the basis for measuring the accuracy of AI-based image classifica-
tions. During that time, error rates dropped from approximately 28% to less than 3%, sur-
passing human performance. As rates for all manner of models improve, that translates
into more and more business offerings which can be practically applied to solve prob-
lems, enhance experiences or create products.
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