BWV said:
Have to ask the test subject, although perhaps weeks of therapy might be required to get the answer from them …Swamp Thing said:
Borg said:
Not what I was thinking.Swamp Thing said:
Wouldn't an NLP version be something more like subvocal recognition (which has been around for decades)?Borg said:
Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging procedure that measures and maps the brain's activity. It detects changes in blood flow and oxygen levels, which correspond to neuronal activity. When a specific brain region is more active, it consumes more oxygen, and the fMRI can capture these changes, allowing scientists to visualize brain activity in real-time.
AI algorithms, particularly deep learning models, can be trained to recognize patterns in fMRI data that correlate with specific visual stimuli. By feeding the AI both fMRI scans and corresponding images seen by subjects, the model learns to associate certain brain activity patterns with specific visual inputs. Once trained, the AI can generate images based on new fMRI data by predicting what the subject is seeing.
This technology could have numerous applications, including advancements in neuroscience research, brain-computer interfaces, and even aiding communication for individuals with disabilities who cannot speak or move. It may also provide insights into visual perception and the workings of the human brain, potentially leading to new treatments for visual and cognitive disorders.
There are several ethical concerns, including privacy issues, as the technology could potentially be used to access a person's thoughts or visual experiences without their consent. There is also the risk of misuse in surveillance or interrogation contexts. Additionally, the accuracy and interpretation of the AI-generated images need to be carefully managed to avoid misrepresentation or false conclusions.
While the technology has shown promising results, it is still in the experimental stages and not yet highly accurate. The images generated by AI from fMRI data are often low-resolution and may not perfectly match the original stimuli. Researchers are continuously working to improve the algorithms and the quality of the generated images, but there is still a significant amount of progress needed before it can be considered reliable for practical applications.