Jan 28, 2023·edited Jan 28, 2023Liked by Rohit Krishnan
It appears no-one is seriously working on sensors, particularly at scale. Your hands have thousands of temperature, texture, and pressure sensors, and synthesizing information from the data these provide is crucial for the ability to manipulate the variety of objects found in unstructured or semistructured environments. (If you've experienced numbness from cold or carpal tunnel, you will know this.)
The easy stuff, vision, has been done--possibly, well enough. Time to move on to the real challenges.
We are indeed surrounded by specialised "robots", but the true paradigm shift is if/when we engineer ones with high mobility *and* ability to generalise to new tasks. I think the latter trait will take more time to get right that the former, but eventually we would be able to free up people's time just as other household items were the unsung heroes of productivity increase in past decades. Alphabet seems to be on this pursuit with Everyday Robots, one of their X spinoffs. And it looks like the transformer paradigm that has enabled systems like ChatGPT might be the key to help robots significantly improve their ability to learn new tasks.
Seem that these are same f the same problems as with children. Hard to train and expansive. :)
Seriously, the solutions may have some similarity: stretch out the training and payment periods, buying more capabilities as the unit learns to use its existing capabilities. Maybe in 16-18 years is would be capable of driving a car.
Thank you! I think we're mitigating and solving some of the concerns you expressed in your article. Nice to see there is recognition of these critical AI production problems!
Love this post and the excerpt below. Have you seen what we do at SparkAI?
"And turns out that this is incredibly hard. There is no way you can handle all edge cases when the training is pretty specific, and rooted in biomechanics. To be successful, they have to work relatively autonomously, navigate its surroundings, make autonomous decisions, and be able to actually handle things like a baby blanket or a hot cup of coffee."
Where does the company Figure.ai, well, figure, into all of this? Do they have a solid plan, or are they just hype? I don't know enough to know either way.
It appears no-one is seriously working on sensors, particularly at scale. Your hands have thousands of temperature, texture, and pressure sensors, and synthesizing information from the data these provide is crucial for the ability to manipulate the variety of objects found in unstructured or semistructured environments. (If you've experienced numbness from cold or carpal tunnel, you will know this.)
The easy stuff, vision, has been done--possibly, well enough. Time to move on to the real challenges.
We are indeed surrounded by specialised "robots", but the true paradigm shift is if/when we engineer ones with high mobility *and* ability to generalise to new tasks. I think the latter trait will take more time to get right that the former, but eventually we would be able to free up people's time just as other household items were the unsung heroes of productivity increase in past decades. Alphabet seems to be on this pursuit with Everyday Robots, one of their X spinoffs. And it looks like the transformer paradigm that has enabled systems like ChatGPT might be the key to help robots significantly improve their ability to learn new tasks.
Seem that these are same f the same problems as with children. Hard to train and expansive. :)
Seriously, the solutions may have some similarity: stretch out the training and payment periods, buying more capabilities as the unit learns to use its existing capabilities. Maybe in 16-18 years is would be capable of driving a car.
Thank you! I think we're mitigating and solving some of the concerns you expressed in your article. Nice to see there is recognition of these critical AI production problems!
Love this post and the excerpt below. Have you seen what we do at SparkAI?
"And turns out that this is incredibly hard. There is no way you can handle all edge cases when the training is pretty specific, and rooted in biomechanics. To be successful, they have to work relatively autonomously, navigate its surroundings, make autonomous decisions, and be able to actually handle things like a baby blanket or a hot cup of coffee."
See SOTA robots here https://ai.googleblog.com/2022/12/rt-1-robotics-transformer-for-real.html?m=1
I also wrote about the problem here https://sergey.substack.com/p/general-purpose-robots it mostly holds up
bots are ubiquitous, they just aren't the kind you're looking for
Where does the company Figure.ai, well, figure, into all of this? Do they have a solid plan, or are they just hype? I don't know enough to know either way.