By definition, the future is unexpected. We cannot know for sure what’s going to happen tomorrow, in two days, or even two minutes.
Buddhism teaches us that it is reasonable to expect that unexpected things will happen, and sometimes, some of those things will be undesirable.
So why do we see so many box packing patents based on advanced planning, uniform parcels, and packing manifests?
These systems hinge on nothing going wrong, and when mistakes do inevitably happen, the AI models they use are so large that the errors often can’t be found.
At Liberty Robotics, we’ve taken a more thoughtful approach to AI. Our Pick on First Sight Technology combines AI with statistical analysis to pinpoint exactly where and how to best apply that AI.
This guided approach not only prevents errors but also allows us to process novel objects and situations we’ve never seen before, just like you and I have to do every day in life.
So how do we maintain the robust reliability of our system without an over-reliance on pre-planning? There are 3 things that we do:
1. Information Theory: We get enhanced data through the merging of appearance and depth data, using information theoretic surprisal. See our patent at www.ppubs.uspto.gov/20230120703, if you’re curious.
2. Look Ahead Algorithms: When deciding where to place parcels, we use specialized algorithms that map out possible futures. Now, this may seem like an obvious thing to do, but most other companies don’t. They don’t because they can’t.
This is a non-polynomial hard problem. The short version of what that means is the deeper you look into the problem, the more time you need to derive a workable solution. If there are 3 boxes to place in an empty container, there are approximately 256,000,000,000,000 solutions as to where to place them. (For you math folks, if N = 3, then 2^(3*16) = 2^48.)
Read the article in full here.
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