The method used could be anything, for example scanning the brain and decoding brain waves into language and/or images using machine learning.
Experimentally, we should be able to do things like predict someone's internal monologue with only minor errors, or predict what answers someone will give to questions, with only minor errors.
For example, if someone thinks of a number between 1-100 and we can consistently guess which number they're thinking of, this would be strong evidence for YES.
Reminder to read comments for additional clarifications.
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So far I think images need to be images that have been observed while the subject is previously being MRI'd then, those patterns in later MRI can be seen to correlate with prior training data the AI had learned of the subject. I.e a lab setting that is voluntary and relied upon prior experiments to "see" that the person is seeing what has previously been seen.
Not sure if language is more predictable. Including numbers I guess? I would imagine that people's brains might be different enough that literally everything is stored differently in each individual's brain and requires prior consent and training data to correlate with subject and AI.
I guess this means that paraplegics can do internet with no hands or speech, but the CIA can't get trade secrets out of some random guy, not even in 2100.
Unless they make all students have school in a classroom full of MRI machines for every student
https://ai.meta.com/blog/brain-ai-image-decoding-meg-magnetoencephalography/
Today, Meta is announcing an important milestone in the pursuit of that fundamental question. Using magnetoencephalography (MEG), a non-invasive neuroimaging technique in which thousands of brain activity measurements are taken per second, we showcase an AI system capable of decoding the unfolding of visual representations in the brain with an unprecedented temporal resolution.
This AI system can be deployed in real time to reconstruct, from brain activity, the images perceived and processed by the brain at each instant. This opens up an important avenue to help the scientific community understand how images are represented in the brain, and then used as foundations of human intelligence. Longer term, it may also provide a stepping stone toward non-invasive brain-computer interfaces in a clinical setting that could help people who, after suffering a brain lesion, have lost their ability to speak.
Does your resolution allow for training against a person or are you looking for a general solution?
E.g., if a person has to put on an apparatus, count to 100 out loud to calibrate and then it can predict based on thoughts is that good enough? Or are you looking for the stronger solution of almost any person connects to whatever the apparatus is and it will, sans individual information, be able to identify which numbers are being thought of?