A team of researchers at USC is helping artificial intelligence to do something that humans have always had an easier time with: imagining the unseen.
The design technique could lead to fairer A.I., new medicines, and increased autonomous vehicle safety.
The artificial-intelligence system, developed by computer science professor Laurent Itti and PhD students Yunhao Ge, Sami Abu-El-Haija, and Gan Xin, in effect, uses the attributes that it "knows" to then think up a never-before-seen object.
Humans can separate their learned knowledge by attributes — for instance, shape, pose, position, color — and then recombine those factors to imagine a new object.
The team was inspired by a human's visual generalization capabilities. The USC researchers wanted to simulate that kind of human imagination in machines, said Ge, the study’s lead author.
"After humans see images of red boats and blue cars, they can decompose and recombine the learned knowledge to imagine novel images of red cars," Ge told Tech Briefs in the short Q&A below.
The USC team simulated this same process using neural networks.
The "imaginative" system outputs an idea that's a combination of learned knowledge. A simplistic example, shown in the above image, demonstrates how the machine — trained on five separate letters, each with their own color and background — yielded an idea that combines all of the data: a brown lowercase "g" on gold background.
The paper, titled Zero-Shot Synthesis with Group-Supervised Learning , was published in the 2021 International Conference on Learning Representations on May 7 of this year.
How to 'Disentangle' a Machine-Vision Snag
Machines are most commonly trained on sample features, like pixels, without considering the object’s attributes.
In the new study, the USC researchers attempted to overcome this limitation using a concept called disentanglement. The approach takes a group of sample images — rather than one sample at a time as traditional algorithms have done — and mines the similarity between them to achieve an idea called “controllable disentangled representation learning.”
Next, the knowledge is recombined to achieve “controllable novel image synthesis,” or what you might call imagination. It's a bit like robots on the big screen.
“Take the Transformers movie” said Ge in an earlier press release . “It can take the shape of Megatron car, the color and pose of a yellow Bumblebee car, and the background of New York’s Times Square. The result will be a Bumblebee-colored Megatron car driving in Times Square, even if this sample was not witnessed during the training session.”
In the interview with Tech Briefs below, Ge explains how disentanglement widens the opportunity for applications.
Tech Briefs: Traditional A.I. is trained on samples and image data, right? How do you train a machine to be “imaginative?” Does a system have to be trained on “learned” components or attributes that can be swapped in and out?
Yunhao Ge: To train a machine to be “imaginative,” we have an assumption that humans can “factorize” the learned knowledge and freely “combine” them to imagine a new unseen scenario for “imagination” For example, after humans see images of red boats and blue cars, they can decompose and recombine the learned knowledge to imagine novel images of red cars.
Based on this assumption, we propose a new learning paradigm, Group-Supervised Learning, which takes a group of samples as input and learns the similarity among them. Controllable disentangled representation learning simulates humans’ “knowledge factorization and recombination ability” and achieves zero-shot synthesis, which simulates the “imagination.”
In our paper, samples with attributes labels are examples of basic elements used to synthesize new unseen scenarios. In different tasks, the meaning of attributes may change. “Swap in and out” is one way of trying to simulate the recombination ability; you can use different ways to achieve this simulation under our group supervised learning paradigm.
Tech Briefs: What other specific applications do you envision for this kind of system? Which applications call for the most “imagination?”
- Discover new drugs. To combine some learned functions from existing drugs together and synthesize or discover new drugs with desired functions.
- For fairness decisions, based on our controllable disentangled ability, we can factorize the undesired factors out and avoid the system consider them during decision making. For instance, race and gender should not be considered in some decisions to ensure fairness. Our group-supervised learning can first disentangle the race and gender information and only use the remaining information during a decision.
- Using our method as data augmentation method to create new data by imagination.
Tech Briefs: In what ways can an imaginative A.I. help a self-driving car?
Yunhao Ge: We can use the learned experience to synthesize or imagine some extreme or dangerous situation, which can teach the self-driving system to avoid this situation and help to improve the robustness and safety.
Similar to the fairness problem, we do not want the self-driving system to consider some factors during the decision. We can use the controllable disentangled representation learning ability to disentangle the useless factors and delete them during the decision to help eliminate the decision bias, which is helpful for safety.
Tech Briefs: What safeguards this system from developing an imaginative, but dangerous design idea?
Yunhao Ge: This learning paradigm is controlled by the user; the designer should be virtuous.
Tech Briefs: What are you working on next?
Yunhao Ge: We want to make our method more general, which releases the requirements of dataset and applications. We also want to extend our method into different data modalities and tasks.
What do you think about "imaginative" A.I.? Share your questions and comments.