Page 29 - EE Times Europe November 2021 final

SPECIAL REPORT: ARTIFICIAL INTELLIGENCE

Engineering Trust in

the Era of AI 2.0

By George Leopold
W ith artificial intelligence concluding its hype cycle, engineers

and researchers are learning more about what we know and
don’t know about its potential and untapped promises.
Clearly, skeptics warn, we need to look, test, and validate
before leaping into an AI-centric future. Hence, there is growing awareness
and research focused on emerging disciplines like “AI safety” that seek to
identify and ultimately anticipate the causes of unintended behavior in
machine-learning and other autonomous systems.
Some early steps will help, including a recent U.S. regulatory order
requiring mandatory reporting of crashes involving ADAS vehicles. (We note
the reference to “crashes” rather than “accidents,” a fraught term in this
context, as it is exculpatory.)
In this AI Special Report, we examine the engineering challenges and
the unintended consequences of entrusting control of our machines to
algorithms. One conclusion is that we remain a long way from using AI in
mission-critical systems that must work 99.999% of the time.
Achieving such levels of reliability, safety, and, ultimately, trust requires
relentless testing, technical standards, and what researcher Helen Toner of
Georgetown University’s Center for Security and Emerging Technology calls
“engineering discipline.”
Another issue is allocation of scarce engineering resources as the list of
companies designing AI chips soars. The latest is Tesla, which unveiled its
Dojo D1 chip for training neural networks during its recent AI Day event.
While accelerating the training of neural networks for ADAS applications is
indeed a requirement, the vertically integrated carmaker’s AI chip appears
to have been motivated by pride of ownership.
“With so many companies building AI chips, why build your own?” notes
Kevin Krewell, principal analyst with Tirias Research. The growing list of
companies working independently on applying AI to autonomous driving
amounts to a “staggering” amount of duplication and waste, Krewell adds.
Automotive applications are pushing the limits of AI technology and
may be among the first to deploy the resulting machine-learning models in
hazardous settings. Before that happens, however, those machines must be
as close to foolproof as engineers can make them.
As our colleague Egil Juliussen notes, the prevailing notion of AI implies
the technology is analogous to human intelligence. As we discuss, AI shall
remain a misnomer until engineers can imbue machines with the common
sense learned by a toddler based on real-world trial and error. ■
George Leopold is a technology writer and an EE Times contributing editor.
This article was originally published on EE Times and may be viewed at
bit.ly/3zJ7mN5r.
IMAGE: SHUTTERSTOCK