Teaching Robots to Understand Semantic Concepts(research.googleblog.com)
research.googleblog.com
Teaching Robots to Understand Semantic Concepts
https://research.googleblog.com/2017/07/teaching-robots-to-understand-semantic.html
19 comments
That seems to be what they are aiming towards but true AI is not the same as Machine Learning.
Google is still using what we could call a very rudimentary form of AI as they describe "Unsupervised learning on very small datasets is one of the most challenging scenarios in machine learning. To make this feasible, we use deep visual features from a large network trained for image recognition on ImageNet".
Google is still using what we could call a very rudimentary form of AI as they describe "Unsupervised learning on very small datasets is one of the most challenging scenarios in machine learning. To make this feasible, we use deep visual features from a large network trained for image recognition on ImageNet".
Interesting points. What would you consider an adequate task that demonstrates a program/robot has acquired "semantic understanding"?
Understand instruction in natural language.
For example:
1.'grab that red ball'
2.'turn the handle on the door 90 degree then pull it out'.
Just like how people would do it, a video and a piece of instruction listed and a label to indicate whether this task is a success or not. Then you show a different setting and a new instruction, if the model successfully generalize and understand the semantics behind it, it should carry out the instruction successfully.
For example:
1.'grab that red ball'
2.'turn the handle on the door 90 degree then pull it out'.
Just like how people would do it, a video and a piece of instruction listed and a label to indicate whether this task is a success or not. Then you show a different setting and a new instruction, if the model successfully generalize and understand the semantics behind it, it should carry out the instruction successfully.
I tied a rope to my door handle and put a treat on the other side of the door. I tried for half an hour to get my dog to learn how to open the door. I demonstrated it many times. I guided his motions through the procedure. I put a treat on the rope to encourage him to interact with it. He moved the door chewing on the treat, but still didn't learn how to open the door on purpose. I gave up.
So yeah maybe it's not anywhere near human intelligence. But its still cool they've made a robot smarter than my dog.
So yeah maybe it's not anywhere near human intelligence. But its still cool they've made a robot smarter than my dog.
I think this says more about your lack of knowledge. See https://www.youtube.com/watch?v=QKSvu3mj-14 for more information.
EDIT: To provide a bit more information. I provide a video because I think it is much more impactful to see what happens than to read it. In this video you can see how an appropriate history of reinforcement will lead to very complex behavior in simple animals. By complex I mean behavior like "talking" and "problem solving".
Here is the 2nd part: https://www.youtube.com/watch?v=erhmslcHvaw
EDIT: To provide a bit more information. I provide a video because I think it is much more impactful to see what happens than to read it. In this video you can see how an appropriate history of reinforcement will lead to very complex behavior in simple animals. By complex I mean behavior like "talking" and "problem solving".
Here is the 2nd part: https://www.youtube.com/watch?v=erhmslcHvaw
To reference an earlier article on HN, this reads like a future of, "Robot, observe these field workers picking fruit for a week. Now practice in this field for a day. OK, now the job is yours.
What I can't tell at all from this article is whether that day is years or decades away.
What I can't tell at all from this article is whether that day is years or decades away.
Yeah, I think “easily programmable humanoid” will be up there with the smartphone, maybe even the computer in terms of impact. Imagine walking into a restaurant, telling the app what food you want, and having a robot walk it over to you. Imagine going to a retail store, giving a voice query of what you’re looking for, and have a robot bring back the best candidates. Imagine owning an Etsy shop, but having a robot automate the entire creation process. Imagine...
This is unlikely to be how the fruit pickers of the future work (though I get your point). Using a robotic arm like this is an extremely expensive way of solving the problem. That's ignoring the technical challenges like accurate localisation of fruit in small clusters, not damaging crop, dealing with occlusions from leaves, etc.
My personal opinion, having worked a little on this problem, is that it's very much like autonomous driving. Getting 90% of the job done is fairly straightforward, but getting that final 10% to make a system commercially viable will take years. Commercial growers don't (yet) have any pressure on labour - it's too readily available and too cheap.
http://www.agrobot.com/ solves this in quite a neat way. Rather than grasping the fruit, they just scoop up each berry in a cup with a blade on one side which severs the stem. This means you don't care about the precise shape of the berry either.
My personal opinion, having worked a little on this problem, is that it's very much like autonomous driving. Getting 90% of the job done is fairly straightforward, but getting that final 10% to make a system commercially viable will take years. Commercial growers don't (yet) have any pressure on labour - it's too readily available and too cheap.
http://www.agrobot.com/ solves this in quite a neat way. Rather than grasping the fruit, they just scoop up each berry in a cup with a blade on one side which severs the stem. This means you don't care about the precise shape of the berry either.
> whether that day is years or decades away
Yes, it's both. Depends on the task.
Afaik we can already program industrial robots by showing them what to do. Robot records movement in its actuators, then keeps replaying over and over.
And we already have machine learning agents that can observe your behavior and learn which news stories are "good" news stories and which aren't. (algo newsfeeds). You could use them to observe a magazine editor and after a few issues, you'd have a robo editor.
Yes, it's both. Depends on the task.
Afaik we can already program industrial robots by showing them what to do. Robot records movement in its actuators, then keeps replaying over and over.
And we already have machine learning agents that can observe your behavior and learn which news stories are "good" news stories and which aren't. (algo newsfeeds). You could use them to observe a magazine editor and after a few issues, you'd have a robo editor.
That's not "showing them what to do" it is more along the lines of "hand-holding".
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https://xkcd.com/1425/
It reall, really depends on the job. And in a job, maybe some subtasks can be automated.
Perhaps the picking cannot be automated, but the transportation can. Or instead of computer vision, have a person click the fruit on a screen so the robot know where to pick.
It reall, really depends on the job. And in a job, maybe some subtasks can be automated.
Perhaps the picking cannot be automated, but the transportation can. Or instead of computer vision, have a person click the fruit on a screen so the robot know where to pick.
Crazy to think that something that was considered "virtually impossible" (image recognition) a few years ago is relatively easy today.
Summary--
Semantics part: Seems like the idea is we can "transfer" knowledge from prior labeled samples so that we don't need to do as much new work labeling sample images with semantic labels.
Grasping part: "Emulating human movements with self-supervision and imitation." High-level imitation based on visual frame differences avoids needing to manually control actuators. Not sure how this works exactly
Two-stream model: ventral network asks What class, dorsal network asks Is this how we should grasp this object. The benefit is that we can make use of all the automatically generated (robot-generated) grasping data without having a human supervise all that automated grasping, e.g. "This process is a successful way to pickup this object, and also this object is an apple." The ventral network ties back this the grasping data (without object labels) to object labels, which allows for semantic control of the trained robot e.g. "Pickup that apple".
Semantics part: Seems like the idea is we can "transfer" knowledge from prior labeled samples so that we don't need to do as much new work labeling sample images with semantic labels.
Grasping part: "Emulating human movements with self-supervision and imitation." High-level imitation based on visual frame differences avoids needing to manually control actuators. Not sure how this works exactly
Two-stream model: ventral network asks What class, dorsal network asks Is this how we should grasp this object. The benefit is that we can make use of all the automatically generated (robot-generated) grasping data without having a human supervise all that automated grasping, e.g. "This process is a successful way to pickup this object, and also this object is an apple." The ventral network ties back this the grasping data (without object labels) to object labels, which allows for semantic control of the trained robot e.g. "Pickup that apple".
I don't want to discount the value of this research. It's absolutely necessary to do this sort of basic proof-of-concept testing of these ideas. But the claim being made implicitly here is way beyond what's actually going on. The software understands nothing, and the "semantics" extend to simple image-matching of objects, but there's no deeper meaning associated with the labels, so I think calling that "semantics" is a major stretch.
This approach is not going to teach a robot how to pick fruit, or serve food, or clean floors anytime soon. In the best case where this is even a workable approach, research like this is just the first of millions more tiny steps along the path. Anyway I think it's naive to assume that a good way to approach automation is to write software to let robots learn by watching humans do the desired task. As cool as that sounds, chances are that approach would ultimately be a massively inefficient way to solve the problem. It'd be like trying to invent the automobile by building a steam-powered horse robot that can tow carriages. The critical purpose is being overlooked in favor of a cool-looking but totally impractical toy demo.