Why This Virtual Human Is Being Injured by Scientists

The human body is complex. It’s got 206 bones, over 600 muscles, and
every day I get older, I discover a new joint I didn’t know could hurt so bad. Learning to control the darn thing takes years
of practice and refinement, and some of us still don’t quite have the hang of it. But artificial intelligence is getting better
all the time, and using consumer-grade computer hardware, scientists in South Korea were able
to train a neural network to control a simulated human body. The work could shape the future of physical
therapy, surgery, and robotics. They started by building a simplified human
body. The researchers figured they didn’t need
all of those 600 plus muscles, so they did away with the superfluous ones that control
things like facial expressions and kept just the 346 that contribute to how joints move. They rigged these muscles over a skeletal
tree that had eight revolute joints like knees and elbows and 14 ball-and-socket joints like
hips and shoulders. To save on the computational load, they also gave him simplified feet with two blocky toes and 31 fewer muscles to simulate. Then they started training him, teaching an
algorithm to control their skeleton through a variety of tasks, some as simple as walking,
while others were more complex, like cartwheeling or lifting weights. Now, this is about the time alarm bells started
going off in my head. They’re training AI to control a buff emotionless
skeleton that lifts weights. I can practically hear that thing saying,
“I’ll be back”. Even more impressive, or worrying depending
on how many times you’ve watched the Terminator movies, is how fast the AI learned to coordinate
these muscles. Researchers started by feeding it motion capture
data of humans doing the desired task like walking. Researchers have taught AI to make a biped
model walk without a reference point in the past, but the results weren’t always…
humanlike. Anyway, it’s faster to train the AI with
these references. Now, depending on how complicated the action was,
the AI could learn to mimic it in anywhere from 12 to 36 hours. And this wasn’t some supercomputer that
was crunching the numbers either, but a PC using a higher-end CPU and graphics card from
2017. Once the AI had the movement down, researchers
could start changing the parameters to see how it responded. They made the weights it was lifting heavier,
and watched as their model started using different muscle groups. They told it to jump higher, and their guy
responded by using their arms in more dynamic ways to balance. They pelted it with simulated balls and watched
him shake them off until toppling over, looking like me in high school gym class on dodgeball day. Or in my adult dodgeball league right now. Go fighting Unicorns. Anyway, whatever the researchers could literally
throw at it, the AI adapted. Finally, they started tweaking the skeleton
and muscles to simulate various ailments, like tightened calf muscles that made the
character walk on its tip toes, or a prosthetic limb that made the character learn a whole
new gait. They also simulated surgeries to correct for
those ailments, and watched how their model adapted post operation. This is where the value of research like this
is really apparent. Building a bipedal model controlled by AI
can teach us how people will walk with a new prosthetic limb design, or can help inform
doctors what surgeries will do their patients the most good. And someday, if the AI is robust enough and
robotics are advanced enough, it can make a bipedal robot blend in among humans. I just hope it also learns to forgive us for
when we simulated injuring it… I’m not as worried about AI in the future
as I am worried about human hackers right now. I used to assume my info was safe online until
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of ways it can all go wrong, like your bones turning hollow. For more on that, check out Sick’s video
on osteoporosis here. Do you see a benefits to teaching machines
to walk or are you worried about a T-800 situation? Let us know in the comments, make sure to subscribe while you’re down there, and I’ll see you next time on Seeker.