> We use AI where semantic understanding and flexibility are useful, such as interpreting less structured information or understanding what in an unfamiliar scene is relevant to a procedure. Once the system knows what physical interaction it needs to perform, we prefer explicit geometry, planning, optimization, and control where possible...
> A few centimeters of error may not matter much when navigating down a hallway, but it matters if a sensor is supposed to remain normal to a curved surface.
This is a great specific example of where the creativity (read: randomness) of AI runs into a wall. Do you see this changing over time as models improve, or do you expect that safety-critical / highly specific tasks will always require a more explicit set of instructions?
That creativity/randomness presents itself in different ways depending on the application of the AI. In robotics, that randomness means variance in the motion of the robots physical body and arm(s). I think over time as labs get better at understanding and quantifying the uncertainty of model outputs there could absolutely be a place for full end-to-end AI in safety-critical environments. But today, we believe that is not the best approach for these applications.
Congrats on the launch. "Successfully executing a trajectory doesn't mean the inspection worked" is a great frame, closing the loop on the measurement itself is the hard part.
Congrats on launching. This should get more attention
> We use AI where semantic understanding and flexibility are useful, such as interpreting less structured information or understanding what in an unfamiliar scene is relevant to a procedure. Once the system knows what physical interaction it needs to perform, we prefer explicit geometry, planning, optimization, and control where possible...
> A few centimeters of error may not matter much when navigating down a hallway, but it matters if a sensor is supposed to remain normal to a curved surface.
This is a great specific example of where the creativity (read: randomness) of AI runs into a wall. Do you see this changing over time as models improve, or do you expect that safety-critical / highly specific tasks will always require a more explicit set of instructions?
That creativity/randomness presents itself in different ways depending on the application of the AI. In robotics, that randomness means variance in the motion of the robots physical body and arm(s). I think over time as labs get better at understanding and quantifying the uncertainty of model outputs there could absolutely be a place for full end-to-end AI in safety-critical environments. But today, we believe that is not the best approach for these applications.
Congrats on the launch. "Successfully executing a trajectory doesn't mean the inspection worked" is a great frame, closing the loop on the measurement itself is the hard part.
Thanks, analyzing the data collection in real time plays a major part in assuring we actually collect the data we set out to.
Is "Salem" a tongue-in-cheek nod to being a small, quirky competitor to Boston Dynamics (Salem, MA : Boston, MA :: Salem Robotics : Boston Dynamics)?
If so, as someone who lives close to Salem, I like it :)
Good luck to you guys.
Salem Statics
First thing I thought of too, I love it
Haha it wasn't, but we might steal that for our story going forward.
is this based in Salem MA? If so, nice