A Cornell University-led experiment in which two people play a modified version of Tetris revealed that players who get fewer turns perceived the other player as less likable, regardless of whether a person or an algorithm allocated the turns.
Most studies on algorithmic fairness focus on the algorithm or the decision itself, but researchers sought to explore the relationships among the people affected by the decisions.
“We are starting to see a lot of situations in which AI makes decisions on how resources should be distributed among people,” said Malte Jung, associate professor of information science, whose group conducted the study. “We want to understand how that influences the way people perceive one another and behave towards each other. We see more and more evidence that machines mess with the way we interact with each other.”
In an earlier study, a robot chose which person to give a block to and studied the reactions of each individual to the machine’s allocation decisions.
“We noticed that every time the robot seemed to prefer one person, the other one got upset,” said Jung. “We wanted to study this further, because we thought that, as machines making decisions becomes more a part of the world – whether it be a robot or an algorithm – how does that make a person feel?”
Predictive Model Allows Researchers to Encode Commands for Cells to Carry Out
Using new machine learning techniques, researchers at UC San Francisco (UCSF), in collaboration with a team at IBM Research, have developed a virtual molecular library of thousands of “command sentences” for cells, based on combinations of “words” that guided engineered immune cells to seek out and tirelessly kill cancer cells.
The work, published online Dec. 8, 2022, in Science, represents the first time such sophisticated computational approaches have been applied to a field that, until now, has progressed largely through ad hoc tinkering and engineering cells with existing, rather than synthesized, molecules.
The advance allows scientists to predict which elements – natural or synthesized – they should include in a cell to give it the precise behaviors required to respond effectively to complex diseases.
“This is a vital shift for the field,” said Wendell Lim, PhD, the Byers Distinguished Professor of Cellular and Molecular Pharmacology, who directs the UCSF Cell Design Institute and led the study. “Only by having that power of prediction can we get to a place where we can rapidly design new cellular therapies that carry out the desired activities.”
Meet the Molecular Words That Make Cellular Command Sentences
Much of therapeutic cell engineering involves choosing or creating receptors that, when added to the cell, will enable it to carry out a new function. Receptors are molecules that bridge the cell membrane to sense the outside environment and provide the cell with instructions on how to respond to environmental conditions.
Artificial intelligence helps shed light on how people’s brains, bodies, and emotions react to listening to music. Music influences parts of the auditory cortex, including the Heschl’s gyrus and superior temporal gyrus, specifically responding to pulse clarity. Changes in dynamics, rhythm, timbre, and the introduction of new instruments cause an uptick in the response. The study also identified the best song types for the perfect workout, sleep, and study.
Your heart beats faster, palms sweat and part of your brain called the Heschl’s gyrus lights up like a Christmas tree. Chances are, you’ve never thought about what happens to your brain and body when you listen to music in such a detailed way.
But it’s a question that has puzzled scientists for decades: Why does something as abstract as music provoke such a consistent response? In a new study, a team of USC researchers, with the help of artificial intelligence, investigated how music affects listeners’ brains, bodies and emotions.
The research team looked at heart rate, galvanic skin response (or sweat gland activity), brain activity and subjective feelings of happiness and sadness in a group of volunteers as they listened to three pieces of unfamiliar music. Continue reading →
A team of scientists has successfully trained a new artificial intelligence (AI) algorithm to make accurate predictions regarding cognitive decline leading to Alzheimer’s disease.
Dr. Mallar Chakravarty, a computational neuroscientist at the Douglas Mental Health University Institute, and his colleagues from the University of Toronto and the Center for Addiction and Mental Health, designed an algorithm that learns signatures from magnetic resonance imaging (MRI), genetics, and clinical data. This specific algorithm can help predict whether an individual’s cognitive faculties are likely to deteriorate towards Alzheimer’s in the next five years.
“At the moment, there are limited ways to treat Alzheimer’s and the best evidence we have is for prevention. Our AI methodology could have significant implications as a ‘doctor’s assistant’ that would help stream people onto the right pathway for treatment. For example, one could even initiate lifestyle changes that may delay the beginning stages of Alzheimer’s or even prevent it altogether,” says Chakravarty, an Assistant Professor in McGill University’s Department of Psychiatry.Continue reading →