General thoughts radio1/3/2024 “We believe it’s important to keep researching the privacy implications of brain decoding, and enact policies that protect each person’s mental privacy. “We think that mental privacy is really important, and that nobody's brain should be decoded without their cooperation,” says Jerry Tang, a PhD student at the university who worked on the project. This, they say, suggests that a decoder couldn’t be applied to someone’s brain activity unless that person was willing and had helped train the decoder in the first place. They found that they performed “barely above chance.” They did this by trying to decode perceived speech from each participant using decoder models trained on data from another person. With this in mind, the team set out to test whether you could train and run a decoder without a person’s cooperation. It may not work so well yet, but the experiment raises ethical issues around the possible future use of brain decoders for surveillance and interrogation. But I’m a bit skeptical that we’re really approaching thought-reading level.” “There might be some interesting use cases, like inferring what you have dreamed about, on a general level. Below are our top 75 thought-provoking and fun questions to ask at your next social gathering. “The way the algorithm works is basically that an AI model makes up sentences from vague information about the semantic field of the sentences inferred from the brain scan,” he says. Romain Brette, a theoretical neuroscientist at the Vision Institute in Paris who was not involved in the experiment, is not wholly convinced by the technology’s efficacy at this stage. The researchers also showed the participants short Pixar videos that didn’t contain any dialogue, and recorded their brain responses in a separate experiment designed to test whether the decoder was able to recover the general content of what the user was watching. For example, a user heard the words “I don’t have my driver’s license yet.” The decoder returned the sentence “She has not even started to learn to drive yet.” When they tested the model on new podcast episodes, it was able to recover the gist of what users were hearing just from their brain activity, often identifying exact words and phrases. It predicted how the brain would respond to the guessed words, and then compared that with the actual measured brain responses. To decode, it guessed sequences of words and checked how closely that guess resembled the actual words. The model learned to predict the brain activity that reading certain words would trigger. The idea was to collect a wealth of data the team says is over five times larger than the language data sets typically used in language-related fMRI experiments. We chatted about recurrent struggles postpartum and in motherhood in general and how we navigate those struggles with our clients and in our own lives.“We all like to listen to podcasts, so why not lie in an MRI scanner listening to podcasts?” jokes Alexander Huth, assistant professor of neuroscience and computer science at the University of Texas at Austin, who led the project.ĭuring the study, three participants each listened to 16 hours of different episodes of the same podcasts while in an MRI scanner, plus a couple of TED talks. In this episode we have the opportunity to talk with Joni Lybbert! Joni is a psychiatric mental health nurse practitioner and the host of "The Sad Moms Club" podcast and she is an expert in postpartum and maternal mental health.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |