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.
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The human body is complex and there are lots 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.