{"id":13241,"date":"2026-09-11T11:20:00","date_gmt":"2026-09-11T06:20:00","guid":{"rendered":"https:\/\/pivot.uz\/?p=13241"},"modified":"2026-09-11T11:31:38","modified_gmt":"2026-09-11T06:31:38","slug":"ai-sizning-orningizga-fikrlay-boshlasa-nima-boladi","status":"publish","type":"post","link":"https:\/\/pivot.uz\/uz\/ai-sizning-orningizga-fikrlay-boshlasa-nima-boladi\/","title":{"rendered":"AI sizning o\u2018rningizga fikrlay boshlasa nima bo\u2018ladi?"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Santa Fe Institute, Harvard Wyss Institute tadqiqotchilari tomonidan e\u2019lon qilingan yangi tadqiqotda olimlar katta til modellarining, jumladan ChatGPT kabi tizimlarning jamiyatda tarqalishini epidemiologik model orqali o\u2018rgandi. Mualliflar bu jarayonni \u201ccognitive virus\u201d \u2014 \u201ckognitiv virus\u201d analogiyasi bilan tasvirlaydi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bu yerda gap AI biologik virus ekani haqida emas. Tadqiqotchilar odamlarning AI bilan munosabati qanday qilib bir foydalanuvchidan boshqasiga tarqalishi va vaqt o\u2018tishi bilan qanday o\u2018zgarishi mumkinligini modellashtirgan.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Modelda odamlar uch guruhga ajratiladi. Birinchi guruh AI\u2019dan deyarli foydalanmaydi va o\u2018zining o\u2018qish, yozish, xotira va fikrlash ko\u2018nikmalarini mustaqil bajaradi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ikkinchi guruh AI\u2019dan muntazam foydalanadi, lekin muhim qarorlarni o\u2018zi qabul qiladi. Masalan, ChatGPT\u2019dan matn uchun g\u2018oya oladi, lekin faktlarni tekshiradi, xulosani o\u2018zi chiqaradi va kerak bo\u2018lsa AI\u2019siz ham vazifani bajara oladi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Uchinchi guruhda esa AI asta-sekin vositadan o\u2018rinbosarga aylanadi. Foydalanuvchi izlash, tahlil qilish, yozish yoki qaror qabul qilish kabi jarayonlarning katta qismini modelga topshiradi. Tadqiqotning asosiy xavfi aynan uchinchi bosqich bilan bog\u2018liq.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ma\u2019lum bir nuqtaga qadar jamiyatdagi AI\u2019dan foydalanish asta-sekin oshishi mumkin. Ammo \u201ctipping point\u201d deb ataladigan kritik chegaradan keyin tizim boshqa holatga o\u2018tishi ehtimoli paydo bo\u2018ladi: AI\u2019dan foydalanish ko\u2018paygani sari undan foydalanish yanada odatiy bo\u2018ladi, bu esa yangi foydalanuvchilarni jalb qiladi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Masalan, ishxonada hamma AI yordamida hisobot tayyorlasa, AI ishlatmaslik tobora noqulayroq bo\u2018lib boradi. Universitetda barcha talabalar AI\u2019dan foydalansa, mustaqil yozish yoki tadqiqot qilishga sarflanadigan vaqt \u201cortiqcha\u201ddek ko\u2018rinishi mumkin.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Shu tariqa texnologiya nafaqat individual odatga, balki ijtimoiy normaga aylanishi mumkin. Tadqiqotchilar buni <strong>technological lock-in<\/strong> \u2014 texnologik qaramlik yoki \u201cqulfga tushib qolish\u201d holati bilan izohlaydi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Tadqiqotdagi eng qiziq matematik g\u2018oyalardan biri \u2014 <strong>hysteresis<\/strong>. Oddiy qilib aytganda, AI\u2019dan foydalanish kuchayib, jamiyat kritik nuqtadan o\u2018tib ketganidan keyin, foydalanish bosimini avvalgi darajaga qaytarishning o\u2018zi yetarli bo\u2018lmasligi mumkin.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Masalan, odam AI\u2019dan foydalanishni juda ko\u2018paytirib yubordi. Keyin undan foydalanishni biroz kamaytirdi. Modelga ko\u2018ra, bu odamni avtomatik ravishda avvalgi mustaqil fikrlash holatiga qaytarmasligi mumkin.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Qaytish uchun AI\u2019ga bo\u2018lgan \u201cbosim\u201dni yanada kuchliroq kamaytirish yoki insonning mustaqil fikrlash amaliyotini qayta kuchaytirish talab qilinadi. Mualliflar aynan shu sababli <strong>oldini olish tiklashdan osonroq<\/strong> bo\u2018lishi mumkinligini ta\u2019kidlaydi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Mualliflar insonning kognitiv qobiliyati albatta pasayadi, deb da\u2019vo qilmaydi. Ular modelda bunday holatni <strong>illustrative assumption<\/strong> sifatida ko\u2018rsatadi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Masalan, o\u2018z hisob-kitoblarida mustaqil foydalanuvchiga 1, muntazam AI foydalanuvchisiga 0,5 va kuchli bog\u2018langan foydalanuvchiga 0,1 nisbiy \u201ccognitive competence\u201d qiymatini berishadi. Ammo maqolaning o\u2018zi bu qiymatlar real odamlarning o\u2018lchangan natijasi emasligini ochiq aytadi. AI\u2019dan \u201cscaffolding\u201d sifatida foydalanish aksincha insonning keyingi qobiliyatini saqlashi yoki oshirishi ham mumkin.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Shuning uchun tadqiqotning o&#8217;z oldiga qo&#8217;ygan savoli: <strong>\u201cAI ishlatilgandan keyin insonning o\u2018zi hali ham shu vazifani bajara oladimi?\u201d<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Tadqiqot mualliflari \u201cAI\u2019dan umuman foydalanmang\u201d degan xulosaga kelmaydi. Aksincha, ular \u201ccognitive immunization\u201d \u2014 kognitiv immunitet tushunchasini taklif qiladi. Bunda maqsad AI\u2019dan uzoqlashish emas, balki AI insonning mustaqil fikrlashini almashtirib yubormaydigan tizim yaratish.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Buning uchun bir nechta mexanizm taklif qilinadi: AI\u2019siz bajariladigan vazifalarni saqlab qolish; javoblarni tekshirishni odatga aylantirish; muntazam ravishda mustaqil yozish va masala yechish; AI\u2019ga topshirilgan vazifada yakuniy qarorni insonning o\u2018zida qoldirish; AI\u2019dan foydalanishni kamaytirish emas, AI\u2019ga qaramlikka aylanish jarayonini kamaytirish.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Modelda foydalanuvchining \u201coddiy AI foydalanuvchisi\u201ddan \u201cdoimiy qaram foydalanuvchi\u201dga o\u2018tish tezligini kamaytirish ham alohida mexanizm sifatida ko\u2018riladi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Insoniyat ilgari ham xotira va fikrlashning bir qismini tashqi vositalarga topshirgan. Kitoblar xotirani kengaytirdi. Kalkulyator hisoblashni osonlashtirdi. Internet ma\u2019lumot izlashni o\u2018zgartirdi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">LLM\u2019larning farqi esa ular shunchaki ma\u2019lumot saqlamaydi yoki qidirmaydi. Ular <strong>matn yozadi, xulosa chiqaradi, g\u2018oya beradi, kod yaratadi, variantlarni solishtiradi va fikrni qayta shakllantiradi.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Shu sababli ular insonning kognitiv jarayonlariga oldingi ko\u2018plab texnologiyalardan bevosita yaqinroq kirib kelmoqda. Tadqiqotchilar aynan shu \u201chuman-AI coupling\u201dni o\u2018rganishga urinmoqda. <br><br><strong>AI bizga yordam berayotgan paytda qaysi ko\u2018nikmalarimizni kuchaytiryapti, qaysilarini esa sekin-asta bizning o\u2018rnimizga bajaryapti?<\/strong>Chunki tadqiqotdagi asosiy xavf AI\u2019dan foydalanishning o\u2018zi emas. AI kerak bo\u2018lmagan vazifalarda ham unga murojaat qila boshlashdir.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Santa Fe Institute, Harvard Wyss Institute tadqiqotchilari tomonidan e\u2019lon qilingan yangi tadqiqotda olimlar katta til modellarining, jumladan ChatGPT kabi tizimlarning jamiyatda tarqalishini epidemiologik model orqali o\u2018rgandi. Mualliflar bu jarayonni \u201ccognitive virus\u201d \u2014 \u201ckognitiv virus\u201d analogiyasi bilan tasvirlaydi. Bu yerda gap AI biologik virus ekani haqida emas. Tadqiqotchilar odamlarning AI bilan munosabati qanday qilib bir foydalanuvchidan [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":13239,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":{"format":"standard"},"jnews_primary_category":[],"jnews_override_bookmark_settings":[],"jnews_paywall_metabox":[],"jnews_override_counter":[],"jnews_podcast_option":[],"jnews_podcast_series":[],"footnotes":""},"categories":[387],"tags":[],"jnews-series":[],"class_list":["post-13241","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-maqolalar"],"_links":{"self":[{"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/posts\/13241","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/comments?post=13241"}],"version-history":[{"count":2,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/posts\/13241\/revisions"}],"predecessor-version":[{"id":13244,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/posts\/13241\/revisions\/13244"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/media\/13239"}],"wp:attachment":[{"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/media?parent=13241"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/categories?post=13241"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/tags?post=13241"},{"taxonomy":"jnews-series","embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/jnews-series?post=13241"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}