I used to think similarly, but learning about the proven incompleteness of sufficiently rigorous analytic systems has moved me towards appreciating less rigorous ways of interfacing with reality.
Veritasium has a nice video on Godel’s incompleteness theorem. It’s quite ironic that it was mathematically provable that analytic systems can never be complete, and can’t be provably coherent.
>The ASCERTAIN dataset comprises a diverse range of physiological signals, including ECG recordings, collected from 58 participants exposed to video stimuli (36 videos) categorized in different categories based on valence and arousal levels. In particular, there are four subcategories of these 36 video clips. Clip 1 to 9 is categorized into High Arousal and High Valance (HAHV), Clip 10 to 18 Low Arousal and High Valance (LAHV), Clips 19 to 27 Low Arousal and Low Valance (LALV), and 28 to 36 High Arousal and Low Valance (HALV) clips [10]. ECG signals from the right and left arm were recorded at a sampling rate of 256Hz…
our model outperformed the closest rival by a wide margin (0.56), achieving an accuracy of 0.94 for the extraversion trait. Similar trends are seen for agreeableness (0.92 versus 0.55), conscientiousness (0.92 versus 0.60), emotional stability (0.93 versus 0.53), and openness (0.93 versus 0.48).
Predicts Big 5 based on heart beat response to various stimuli. I wonder if EKG’s really contain more information beyond heart rate as the conduction within the heart is pretty consistent normally, regardless of rate. Neural nets seem to work great at finding any signal- too bad it isn’t so clear what those signals are.
Veritasium has a nice video on Godel’s incompleteness theorem. It’s quite ironic that it was mathematically provable that analytic systems can never be complete, and can’t be provably coherent.
https://m.youtube.com/watch?v=HeQX2HjkcNo&pp=ygUUdmVyaXRhc2l...