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An author's article published by consulting firm Gartner in September 2020 highlights that artificial intelligence is exceeding its hype, a peak of disproportionate expectations, meaning it is starting to deliver results in line with its potential and delivering business value. ... According to the author, AI is becoming a reality thanks to the democratization and industrialization of AI platforms.
Is this a
tangible reality? An international report from Capgemini in May 2020 with more
than a thousand companies represented the evolution of AI penetration in
business and across various sectors, with downright unsatisfactory results. The
vast majority of companies surveyed (72%) are so-called 'struggling
organisations': they started pilot testing before 2019, but have not yet be
able to implement the application in real production.
My
colleague Jonah Ehazarra has already pointed out some of the possible reasons
for this uneven deployment of AI solutions in the industry. If we analyze them
from a purely scientific and technical point of view, or from the point of view
of implementation and decision making as a product, there is always a certain
gap in the perception of promising trends or technologies.
The
well-known death valley of technology is the desert that separates the
undeniable and impressive scientific achievements of this one-handed robot from
the actual implementation of a solution based on similar algorithms for
learning reinforcement in a factory. for continuous learning of an artificial
intelligence model with production data in real time without human intervention
and control during the process.
Is this a
clear indication that AI will remain in this uncertainty of promising but never
efficient enough technologies? Anyone who considers the impossibility of
deploying an AI model insufficient, which only distracts and learns how to
predict a quality error in the production of a metal part, for example by
stamping, would answer in the affirmative: yes, AI remained in another hype
technology whose effective penetration into industrial reality did not deliver
the promised greatness. This belief is supported by voices that qualify as very
limited progress, even worthless, all those small successes not linked to
achieving the expectations of science fiction.
How not to
feel overshadowed by JARVIS; "Just a very intelligent system", an
artificial intelligence created by Marvel's character Anthony Stark (Iron Man).
It is such an advanced system that, thanks to ultra-fast digital scanning of a
city model, it is able to classify the target of the presented elements without
any control, to derive and combine molecular binding rules to produce protons
and neutrons on the skeleton. to structure. model and estimate the possibility
of creating a new chemical element in less than a minute. JARVIS It is
ubiquitous, integrating all kinds of data sources, presenting an immersive
interaction interface, interpreting gesture commands flawlessly and processing
natural language, learning continuously and autonomously.
This is
indeed an unrealistic vision for AI today. In the Gartner hype cycle (shown in
the image), the so-called general artificial intelligence that we might
associate with this vision is still at the beginning of an innovative trigger
with an estimated prospect of more than 10 years to overcome the high level.
cycle points and achieving a possible stable regime.
However,
some of us think that this claim is no barrier to arguing at the same time that
the AI we know and use opens up a spectrum of new horizons in today's
industry that would otherwise be unimaginable and unattainable. Data-driven
techniques, based on current machine learning algorithms, have a cross-cutting
and transformative impact on understanding problems in production processes. It
is a fact that the development of AI prediction models makes it possible to
predict and minimize risks automatically and in continuous adaptation.
Deploying
and integrating a soft sensor (IA models with real-time prediction) into a
digital solution that predicts the quality score of a continuous process is a
breakthrough with measurable and immediate benefits. The same measure was
previously performed in the lab and applied to a random sample taken once a
day, the result of which was delayed for several hours: this could mean giving
up half a day's products if the quality falls below the required threshold. ...