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@nisarsagar9764b: #duet with @dainikbhaskar

shaikh nisar
shaikh nisar
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Tuesday 30 June 2020 06:34:43 GMT
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ateeqshaikh420
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2020-06-30 06:42:57
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ลุงดำ ตั้งแต่เสียลูกชายคนโตไปก็ล้มป่วย เส้นเลือดสมองตีบ🥺🥲 #เมย์ตลาดพลู #เซียนพระAi #สายบุญ #ประมูลพระเครื่องทำบุญ #กลุ่มเมตตาบารมี
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Тг: nurisremix — ссылка в профиле
Тг: nurisremix — ссылка в профиле
#nailschuongmy mưa gió cộng thêm nghỉ lễ làm con tim buồn quá ce ơi😩lên lịch cho bé nào các tình iu#viraltiktok #nailsdesign #nailschuongmy #xuhuongtiktok
#nailschuongmy mưa gió cộng thêm nghỉ lễ làm con tim buồn quá ce ơi😩lên lịch cho bé nào các tình iu#viraltiktok #nailsdesign #nailschuongmy #xuhuongtiktok
Substack is about to change in a big way. Its CEO Chris Best just announced a partnership with Pangram, an AI detector that will score every post. Detectors work by hunting for statistical fingerprints, focal words like “delve,” flattened sentence rhythms, the low perplexity of machine prose. But we trained these machines on our own writing, and now our writing is drifting toward theirs. The errors and implications here do not fall entirely evenly. Pangram claims a false positive rate of 1 in 10,000, the best in the industry. But, research on earlier detectors found they flagged over 60% of essays by non-native English speakers as AI-written. Neurodivergent writers get flagged at higher rates too. So, there you have it: our writing is reduced to a number that is produced by a closed model, trained on data we cannot see. Luckily, we already have a word for that architecture: Surveillance. I’ve got a full breakdown for you in the video. Tell me in the comments: would you run your own writing through a detector? #AIGovernance #AIDetection #Substack #Pangram #writingcommunity
Substack is about to change in a big way. Its CEO Chris Best just announced a partnership with Pangram, an AI detector that will score every post. Detectors work by hunting for statistical fingerprints, focal words like “delve,” flattened sentence rhythms, the low perplexity of machine prose. But we trained these machines on our own writing, and now our writing is drifting toward theirs. The errors and implications here do not fall entirely evenly. Pangram claims a false positive rate of 1 in 10,000, the best in the industry. But, research on earlier detectors found they flagged over 60% of essays by non-native English speakers as AI-written. Neurodivergent writers get flagged at higher rates too. So, there you have it: our writing is reduced to a number that is produced by a closed model, trained on data we cannot see. Luckily, we already have a word for that architecture: Surveillance. I’ve got a full breakdown for you in the video. Tell me in the comments: would you run your own writing through a detector? #AIGovernance #AIDetection #Substack #Pangram #writingcommunity

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