Ready, Set, Algorithms! Teams Learn AI by Racing Cars
October 01 2019 - 5:59AM
Dow Jones News
By Sara Castellanos
Employees at companies including Morningstar Inc. and Liberty
Mutual Group Inc. are learning about advanced
artificial-intelligence techniques by programming and racing
miniature self-driving cars.
The DeepRacer league, developed by Amazon.com Inc.'s Amazon Web
Services cloud business, is designed to teach a branch of AI known
as reinforcement learning. In this type of machine learning,
algorithms learn the correct way to perform an action based on
trial-and-error and observations. The technique is different from a
type of AI commonly used in business called supervised learning, in
which algorithms have to be fed with labeled training data to learn
to recognize images or make predictions. Here, the cars supply
their own data: images collected with cameras.
Anyone with an Amazon Web Services account can participate in
the league. Teams or individuals can compete online in "virtual"
races or in person at events world-wide.
Teams build and train AI algorithms using Amazon SageMaker
software, deploy them to self-driving cars measuring about 10
inches, then race them around a track of roughly 17 feet by 26
feet. The fastest car wins.
"It's actually having practical applications," said James
Rhodes, chief technology officer of investment research firm
Morningstar. Thanks to the training, the company expects to have
dozens of projects based on reinforcement learning and other
machine-learning techniques in deployment by the end of 2020, he
said.
Besides training autonomous vehicles, reinforcement learning can
be used to help robots walk faster or to develop security systems
that can automatically adapt to different environments, experts
say. "[It's] a pretty complicated technology and there's a pretty
steep learning curve, " said Mike Miller, general manager of AI
devices at Amazon Web Services.
AWS developed the DeepRacer program as a way to teach software
developers about machine learning in a more engaging way than
reading scientific articles, Mr. Miller said.
The algorithms are complex because they gather data on their
own, instead of being fed millions of images to learn from, said
Peter Stone, professor of computer science at the University of
Texas at Austin, who isn't involved with DeepRacer. Programmers
write code to "reward" the algorithms when they do something right,
such as winning a race or avoiding an obstacle. In the case of
algorithm-powered cars, that could include such tasks as staying
close to the center line on the track, minimizing wide steering
angles and turning to avoid barriers and crashes.
At Morningstar, more than 450 software developers, equity
analysts and quantitative researchers have formed nearly 100 racing
teams in 10 countries since January, when Mr. Rhodes began allowing
employees to use the technology.
Morningstar has invested "north of tens of thousands of dollars"
on the miniature cars and training software to date, Mr. Rhodes
said. "It's bringing the virtual [world] into the physical space,
especially for individuals who are not necessarily computer
scientists," he said.
Earlier this year, one of the Morningstar teams came up with an
idea for a tool based on reinforcement learning that looks for
patterns in regulatory filings to more accurately identify various
information. The tool was deployed in June. Another team came up
with an idea for a tool that uses reinforcement learning to
automatically find and fix broken links to financial institutions'
websites, Mr. Rhodes said. That tool is still in development.
Insurer Liberty Mutual, meanwhile, has about 270 employees,
including software engineers and data scientists, participating in
DeepRacer.
"It's a fun way for people to get practical exposure for what
are very important algorithms in a safe environment where they're
not going to mess up any core business applications," said James
McGlennon, the company's chief information officer.
The company is already using other machine-learning techniques
to tweak prices for auto insurance based on risk factors and to
look for anomalies in operations. The goal of the DeepRacer program
is for employees to think about ways the company can use
reinforcement learning to help the business, Mr. McGlennon
said.
Reinforcement learning is one of many machine-learning methods
that will ultimately be used by companies, said Dario Gil, director
of research at International Business Machines Corp. It is
challenging to rely on that technique alone when training a
real-world autonomous vehicle, he said, because so much of the
method relies on trial and error. "There's a reason why
reinforcement learning gets stuck in the world of games," he
added.
Write to Sara Castellanos at sara.castellanos@wsj.com
(END) Dow Jones Newswires
October 01, 2019 05:44 ET (09:44 GMT)
Copyright (c) 2019 Dow Jones & Company, Inc.
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