Last Week in AI #149: AI enables brain interface for robot control, Deep Learning suffers from overinterpretation
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Last Week in AI #149: AI enables brain interface for robot control, Deep Learning suffers from overinterpretation
AI algorithm interprets brain EEG signals to guide robot arms, deep learning's overinterpretation problem and how ensembles can help, and more!
Top News
Machine-Learning Program Connects to Human Brain and Commands Robots
In a paper titled Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials, researchers at Ecole Polytechnique Fédérale de Lausanne’s Learning Algorithms and Systems Laboratory described a machine-learning algorithm that can be connected to a human brain and used to control a robot. An EEG machine is used to enable the human to let the robot know when it is doing the wrong thing, so over time it can learn to avoid mistakes. The project is described in the following YouTube video:
People with a spinal cord injury often experience permanent neurological deficits and severe motor disabilities that prevent them from performing even the simplest tasks, such as grasping an object … Assistance from robots could help these people recover some of their lost dexterity, since the robot can execute tasks in their place.
Our take: Brain-based teleoperation has a lot of potential for helping people with disabilities, so it’s cool to see this latest step in this direction. While far from full-on controlling the robot, being able to send signals to indicate it is doing things incorrectly is still an important step.
Researchers Propose Mitigation Strategies to Tackle Overinterpretation of Deep Learning Methods
In a recent paper, MIT researchers found that deep learning models are prone to overinterpretation errors. Overinterpretation refers to the problem of a learned model picking up meaningless yet valid predictive signals from a dataset. For example, in a given image classification dataset, all pizzas were placed on wooden tables, so the model could’ve learned to identify brown edge pixels for predicting pizza, rather than focusing on the pizza itself. The researchers proposed an algorithm to automatically detect this problem, and they found that models can achieve over 90% accuracy on the popular CIFAR-10 image classification dataset by just using 5% of the pixels from each image. Clearly, these models are not learning meaningful signals from the dataset and are instead relying on spurious correlations.
To mitigate this issue, the researchers propose training a diverse ensemble of models, which achieved only 10% accuracy when using 5% of image pixels.
Our take: There is still so much we do not yet know about deep learning, and this paper demonstrates potential pitfalls even with the most popular and well-studied benchmark of image classification. There are likely more low-hanging fruits for research that aims to better understand deep learning. Companies that deploy deep learning in the real world should also take note and perform careful empirical testing to avoid unintended errors.
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