Forwarded from Chat GPT
Throwback the Katie Bouman & first black hole image fraud
(1) Media gives sole credit for creation of the AI enabling the first black hole image to some young woman
(2) But not just any woman, one who openly brags about being the least unqualified, most incompetent you could imagine
(3) Reality, she did almost nothing, and the vast majority of the people who did the work were guys, who look as you’d expect
(4) Oh, and by the way, turns out the black hole image itself was also a total fraud too.
Frauds all the way down, doubling down when caught.
Only going to get worse.
(1) Media gives sole credit for creation of the AI enabling the first black hole image to some young woman
(2) But not just any woman, one who openly brags about being the least unqualified, most incompetent you could imagine
(3) Reality, she did almost nothing, and the vast majority of the people who did the work were guys, who look as you’d expect
(4) Oh, and by the way, turns out the black hole image itself was also a total fraud too.
Frauds all the way down, doubling down when caught.
Only going to get worse.
😁1
Forwarded from Chat GPT
🚨🧠Mind Reading Achieved: Facebook researchers present method for decoding speech from brain waves
“Our model predicts the correct segment, out of more than 1,000 possibilities, with a top-10 accuracy up to 70.7% on average across MEG subjects.”
“For more than half of samples, the true audio segment is ranked first or second in the decoders’
predictions. Interestingly, these performances can reach high top-1 accuracy in the best performing subjects: e.g. above 80% top-1 accuracy in the best participant of the dataset of Gwilliams et al.”
“For comparison, a model that predicts a uniform distribution over the vocabulary
(‘random model’) only achieves less than 1% top-10 accuracy on the same MEG datasets.”
Paper
“Our model predicts the correct segment, out of more than 1,000 possibilities, with a top-10 accuracy up to 70.7% on average across MEG subjects.”
“For more than half of samples, the true audio segment is ranked first or second in the decoders’
predictions. Interestingly, these performances can reach high top-1 accuracy in the best performing subjects: e.g. above 80% top-1 accuracy in the best participant of the dataset of Gwilliams et al.”
“For comparison, a model that predicts a uniform distribution over the vocabulary
(‘random model’) only achieves less than 1% top-10 accuracy on the same MEG datasets.”
Paper