Monday, November 29, 2021

g-f(2)691 THE BIG PICTURE OF THE DIGITAL AGE (11/29/2021), MIT News, Artificial intelligence that understands object relationships


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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital age, Artificial Intelligence, Robotics  (11/2/2021)  g-f(2)426 


OPPORTUNITY, MIT News

AI that understands object relationships 


  • A new machine-learning model could enable robots to understand interactions in the world in the way humans do.
  • MIT researchers have developed a model that understands the underlying relationships between objects in a scene. Their model represents individual relationships one at a time, then combines these representations to describe the overall scene. This enables the model to generate more accurate images from text descriptions, even when the scene includes several objects that are arranged in different relationships with one another.
  • This work could be applied in situations where industrial robots must perform intricate, multistep manipulation tasks, like stacking items in a warehouse or assembling appliances. It also moves the field one step closer to enabling machines that can learn from and interact with their environments more like humans do.
  • This research is supported, in part, by Raytheon BBN Technologies Corp., Mitsubishi Electric Research Laboratory, the National Science Foundation, the Office of Naval Research, and the IBM Thomas J. Watson Research Center.


Genioux knowledge fact condensed as an image


References


Artificial intelligence that understands object relationships, Adam Zewe, November 29, 2021, MIT News.



Extra-condensed knowledge


Lessons learned, MIT News

  • When humans look at a scene, they see objects and the relationships between them. On top of your desk, there might be a laptop that is sitting to the left of a phone, which is in front of a computer monitor.
  • Many deep learning models struggle to see the world this way because they don’t understand the entangled relationships between individual objects. Without knowledge of these relationships, a robot designed to help someone in a kitchen would have difficulty following a command like “pick up the spatula that is to the left of the stove and place it on top of the cutting board.”
  • “When I look at a table, I can’t say that there is an object at XYZ location. Our minds don’t work like that. In our minds, when we understand a scene, we really understand it based on the relationships between the objects. We think that by building a system that can understand the relationships between objects, we could use that system to more effectively manipulate and change our environments,” says Yilun Du, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper.


Condensed knowledge




Lessons learned, MIT News


  • Understanding complex scenes. The researchers compared their model to other deep learning methods that were given text descriptions and tasked with generating images that displayed the corresponding objects and their relationships. In each instance, their model outperformed the baselines.
  • While these early results are encouraging, the researchers would like to see how their model performs on real-world images that are more complex, with noisy backgrounds and objects that are blocking one another.
  • They are also interested in eventually incorporating their model into robotics systems, enabling a robot to infer object relationships from videos and then apply this knowledge to manipulate objects in the world.


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  • Category 2: The Big Picture of the Digital Age
  • [genioux fact deduced or extracted from MIT News]
  • This is a “genioux fact fast solution.”
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  • Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
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    • Inherited from sources + Supported by the knowledge of one or more experts.


References


“genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)691, Fernando Machuca, November 29, 2021, blog.geniouxfacts.com, geniouxfacts.com, Genioux.com Corporation.


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