Ford’s Use of AI an Example of Shaping of Innovation in MIT Future of Work Session

Attendees of MIT’s Future of Work Congress 2020 learned how
Ford Motor Co. is shaping innovation from its use of AI in
manufacturing. (Credit: Getty Images)

The Ford Motor Co. has made a substantial investment in AI, from
investing $1 billion in Argo AI in 2017 to advance its self-driving
car efforts, to developing centers of excellence to focus on
machine learning and AI, where engineers determine the AI tools and
methods that can be dispersed throughout the company.

The use of AI for predictive maintenance, anticipating when a
part may fail before it does, is proving productive for
manufacturing at Ford, according
to Jeanne Magoulick, Advanced
Manufacturing Manager, Ford Motor Co.
. She spoke as a
member of a panel on Shaping Technology Innovation at MIT’s
recent AI and the Work of the Future Congress 2020 held

“We are excited about predictive
maintenance,” Magoulick said. “It will make us more efficient.
We can identify when a machine is trending out of control and may
need maintenance, so we can schedule at the next available window.
It’s the next level of predictive maintenance from what we do

It also helps in the ordering of needed replacement parts. “If
we know the part is going bad, rather than holding the cash in our
inventory, we can order it on demand,” she said.  

AI is also being applied to vision systems, making for more
powerful abilities to conduct inspections during manufacturing.
”We can find defects anywhere, including seeing paint
scratches,” Magoulick said.   

In addition, AI is being applied to further automate the auto
manufacturing process, with research into where to apply the
innovation ongoing. “We are using machine learning to try to
reduce our cycle times,” she said. “We recently reviewed a use
case for transmission assembly, which reduced the cycle time
slightly the first time through.”  

In addition, Ford is experimenting with the use of natural
language for voice commands to communicate with machines on the
shop floor. “It’s Siri for manufacturing,” she said.   

Additional areas of research include studying audio to detect
quality defects, “using AI to assess what is a good and what is a
bad digital audio signature,” she said. Also, Ford is
experimenting with collaborative robots on the shop floor, she

Domain Expertise Comes from Those Doing the Work

Asked by session moderator David Mindell, Co-chair, MIT
Task Force on the Work of the Future,
 and MIT Professor of
Aeronautics, where the domain expertise comes from, Julie
Shah, MIT Associate Professor, Department of Aeronautics and
 said it is primarily from the people doing
the work today. “The domain expertise is with people on the shop
floor doing the job today, learned through years of apprenticeship
in some cases,” said Shah. “It might look easy in some cases on
first look but it can be challenging to program.” 

She added, “Being able to learn from observation and
demonstration is best done directly from someone doing the task on
the shop floor, to see the key factors in doing the job

Panelist Daron Acemoglu, MIT Professor of
 in response to a question from Dr. Mindell on
whether AI will make better engineers, stressed the need for AI
engineers to have a “concrete understanding” of the social
implications of decisions they will make. He also stressed the
importance of government policy.  

“Government priorities are signals,” he said. “If the
government gives up on the agenda of creating better technology,
it’s natural for researchers to do that too.”  

He is concerned that AI researchers maintain autonomy from the
corporate world, and that big tech companies fund much of AI
research in their own AI labs. “They have their own agendas,”
he said. “If those companies set the tone for leading AI labs,
how can we expect the AI research to do anything but parrot the
priorities of those companies. It’s a difficult lesson. We are
not really establishing our autonomy. We are saying good research
means we are more integrated with Google, Amazon and IBM. Autonomy
is critical in this area.”  

Rus Sees “Problem-Driven” Research With Industry as

He was challenged on this point a bit by
panelist Daniela Rus, Director MIT CSAIL, and MIT Professor
of Electrical Engineering and Computer Science, 
who said
she has had some good experiences collaborating with researchers in
private industry.   

“I think there is a fair bit of autonomy and a number of
programs that support problem-driven research,” she said.
“Maybe there is not enough funding, and in some sense, where the
government is lacking, the companies are stepping in.”  

She added, “I find working with companies can be enriching and
empowering,” mentioning a collaboration with Toyota Research
Institute about five years ago to advance the science and AI and
robotics research. Outlining her thoughts, she said, “When I
think about how the university and industry research labs connect
together, I have a mental model where the industry development lab
works on products for today, the industry research lab works on
problems of tomorrow, and the university research role is to think
about the day after tomorrow, connecting to how those advances
matter. The applications allow us to root our ideas into things the
world cares about.” 

Mindel asked if we should worry about AI taking over too many
functions of humans. Prof. Acemoglu said, “There is a choice.
There is no iron-clad rule on what humans can do and what
technology can do. They are both fluid. It depends on what we

Prof. Shah agreed with the sentiment. “The machines are still
performing very narrowly-defined tasks,” she said. “Deep
learning is a functional approximator, like algebra and calculus.
It’s how we take those tools and use them for a purpose, and how
we define success for those systems that matters. We might be
trying to replace some aspect of what a human is doing today, but
none of these systems operate truly independently. So asking what
is the way we can have these technologies achieve our larger goals
is the critical question.”  

Rus ended the session on an optimistic note. “In the
scientific community, we advance the science and engineering or
intelligence and in doing so, we accomplish many things. We get a
better handle on life, and we develop a broader range of machine
capabilities. I am excited about using the latest advances in AI,
machine learning and robotics to make certain jobs easier, to make
life easier,” she said.  

Technology has allowed people to come closer together during the
pandemic, she said, “Despite the fact that the world is in the
middle of a pandemic. And technology has allowed us to develop a
vaccine more quickly, and that is helping us address the

Originally published by
AI Trends Staff | December 10, 2020
AI Trends

Read the 2020
report from the MIT Task Force on the Future of Work