Jan 23, 2025 03:18 AM
(This post was last modified: Jan 24, 2025 01:55 AM by C C.)
AI judged to be more compassionate than expert crisis responders: Study
https://www.eurekalert.org/news-releases/1071505
INTRO: By definition, robots can’t feel empathy — it requires being able to relate to another person’s human experience, to put yourself in their shoes. But according to new U of T Scarborough research, artificial intelligence (AI) can create empathetic responses more reliably and consistently than humans, even when compared to professionals whose job relies on empathizing with those in need.
“AI doesn’t get tired,” says Dariya Ovsyannikova (HBSc 2023 UTSC), lab manager in Professor Michael Inzlicht’s lab at U of T Scarborough and lead author of the study. “It can offer consistent, high-quality empathetic responses without the emotional strain that humans experience.”
The research, published in the journal Communications Psychology, looked at how people evaluated empathetic responses generated by ChatGPT compared to human responses... (MORE - details, no ads)
New, embodied AI reveals how robots and toddlers learn to understand
https://www.eurekalert.org/news-releases/1071383
INTRO: We humans excel at generalization. If you taught a toddler to identify the color red by showing her a red ball, a red truck and a red rose, she will most likely correctly identify the color of a tomato, even if it is the first time she sees one.
An important milestone in learning to generalize is compositionality: the ability to compose and decompose a whole into reusable parts, like the redness of an object. How we get this ability is a key question in developmental neuroscience – and in AI research.
The earliest neural networks, which have later evolved into the large language models (LLMs) revolutionizing our society, were developed to study how information is processed in our brains. Ironically, as these models became more sophisticated, the information processing pathways within also became increasingly opaque, with some models today having trillions of tunable parameters.
But now, members of the Cognitive Neurorobotics Research Unit at the Okinawa Institute of Science and Technology (OIST) have created an embodied intelligence model with a novel architecture that allows researchers access to the various internal states of the neural network, and which appears to learn how to generalize in the same ways that children do. Their findings have now been published in Science Robotics.
“This paper demonstrates a possible mechanism for neural networks to achieve compositionality,” says Dr. Prasanna Vijayaraghavan, first author of the study. “Our model achieves this not by inference based on vast datasets, but by combining language with vision, proprioception, working memory, and attention – just like toddlers do.” (MORE - details, no ads)
https://www.eurekalert.org/news-releases/1071505
INTRO: By definition, robots can’t feel empathy — it requires being able to relate to another person’s human experience, to put yourself in their shoes. But according to new U of T Scarborough research, artificial intelligence (AI) can create empathetic responses more reliably and consistently than humans, even when compared to professionals whose job relies on empathizing with those in need.
“AI doesn’t get tired,” says Dariya Ovsyannikova (HBSc 2023 UTSC), lab manager in Professor Michael Inzlicht’s lab at U of T Scarborough and lead author of the study. “It can offer consistent, high-quality empathetic responses without the emotional strain that humans experience.”
The research, published in the journal Communications Psychology, looked at how people evaluated empathetic responses generated by ChatGPT compared to human responses... (MORE - details, no ads)
New, embodied AI reveals how robots and toddlers learn to understand
https://www.eurekalert.org/news-releases/1071383
INTRO: We humans excel at generalization. If you taught a toddler to identify the color red by showing her a red ball, a red truck and a red rose, she will most likely correctly identify the color of a tomato, even if it is the first time she sees one.
An important milestone in learning to generalize is compositionality: the ability to compose and decompose a whole into reusable parts, like the redness of an object. How we get this ability is a key question in developmental neuroscience – and in AI research.
The earliest neural networks, which have later evolved into the large language models (LLMs) revolutionizing our society, were developed to study how information is processed in our brains. Ironically, as these models became more sophisticated, the information processing pathways within also became increasingly opaque, with some models today having trillions of tunable parameters.
But now, members of the Cognitive Neurorobotics Research Unit at the Okinawa Institute of Science and Technology (OIST) have created an embodied intelligence model with a novel architecture that allows researchers access to the various internal states of the neural network, and which appears to learn how to generalize in the same ways that children do. Their findings have now been published in Science Robotics.
“This paper demonstrates a possible mechanism for neural networks to achieve compositionality,” says Dr. Prasanna Vijayaraghavan, first author of the study. “Our model achieves this not by inference based on vast datasets, but by combining language with vision, proprioception, working memory, and attention – just like toddlers do.” (MORE - details, no ads)