AI ki madad se Asarat Napnay Mein Taqat: Aalmi Taraqqi Tafheem-e-Mosarat Mein Zaban Barriers Ko Paar Karna
Warren Buffet ka ek izhar hai jo un logon ke liye istemal hota hai jo relative asoodgi ke haalat mein paida hue hain – un ke mutabiq, unho ne “overyan lottery” jeet li hai. Aam taur par, jo log overyan lottery jeet gaye hain, woh log hain jo un logon ki madad kar rahe hain jo is lottery mein shamil nahi hain. Lekin amuman yeh zaban aur tehqiqi rukawat paida karte hain jo do groups ke darmiyan “sunnayi ka fasla” banati hai, jo ghareebon ke masail ki samajh ko rokta hai. Agar yeh sunnayi ka fasla ko daryaft nahi kiya jaye, to ghareebon ke masail ka ilaj asar nahi kar sakta.
Artificial intelligence (AI) is sunnayi ka fasla kam karne mein aham kirdar ada kar sakta hai. Haal hi mein, ChatGPT ne duniya ki tawajju ko apni taraf kheench liya hai jese ke yeh pehla AI platform hai jo itni samajhdaar hai ke ise amm logon ne apnaya, jiski wajah se is par tajaweez, shak o shubha aur tanqeed ka dhor chala hai. Lekin ChatGPT ke peshanumaan kirdar se pehle, iske purane nasli versions ki banai gayi technology ne maliyat, insan sarmaya, marketing, telecom aur cyber security jese industries mein amal ki gayi hai. ChatGPT ke sath mila hua tajaweez aur in saalon ki tajurbat se, yeh sab sikhne ke liye ahem hain ke naye nasl ke predictive AI ko sunnayi ka fasla karna ke liye kaise istemal kiya ja sakta hai, transcribed natural language ko samajhne mein madad karte hue.
Niche hum kuch is tarah ke lessons par gaur karenge, jo yeh aane wale AI tools hamare poverty alleviation kaam par kis tarah asarat andaz ho rahe hain Decodis mein — aur is par roshni dalenge ke is jaddojahad mein insan sarmaya ko in koshishon ko rehnumai karne mein kis tarah istemal karna chahiye.
PEHLE CHATGPT: HUMARE AI-BASED TOOLS KE SHURUATI TAJURBAT
Decodis ek samaji tahqiqati company hai jo kamzor tabqon ke samna karne wale masail ke bare mein tafseelat farahem karti hai. Hamari tajurbaton ki bunyad natural speech ki taqat par hai, aur is taqat ko barhane aur tajziyaat mein madad karne wale tech ko istemal karte hain. Ham is technology ko apne clients ki tafseelati aur samaji programming, siyasi nizaam, asarat napnay aur doosre maamlat mein madad farahem karne ke liye istemal karte hain.
Hamare kaam ka bara hissa survey data collection ko behter banane mein shamil hai, taki samajh saken ke log kya keh rahe hain aur kaise keh rahe hain — ek aisa process jo unki experiences aur nazariyat ko behtar se izhaar karsakta hai, organizations ko unki khidmat behtar taur par farahem karne ki rahnumai kar sakta hai. Jab ek survey participant ek set ke open-ended sawalon ka jawab deta hai, to ham us audio ko text mein convert karte hain aur phir us text ko tahlil karte hain taake hum consistent themes aur topics ko nikal sakein. Yeh process qeemti idaray dikhata hai, lekin yeh bohot mehnat ka kaam hai aur ise bina AI ke madad ke karna mumkin nahi hai. Ham hazaron respondents se is survey data ko collect karte hain, jise amuman kai alag zuban mein kiya jata hai, aur jo ke bohot zyada hota hai, jise koi researchers manual taur par nahi kar sakta.
Is process ko streamline karne ke liye, hum Natural Language Processing (NLP) ka istemal karte hain, jo hamain yeh anumati deta hai ke ham itna text do din mein tahlil kar sakein jise ek qualitative researcher do mahinon mein kar sakta hai. Hamne off-the-shelf products dwara diye gaye rule-based topic classification ka istemal karke is process ko simple banaya. Lekin ham pareshan the ke is tarah ke models ki accuracy ko kaise calculate kiya jaye, jo hamare efforts ko mazeed taqat dete. Hamare liye zaroori tha ke hamare nizaam mein shaamil hone wale nuance ke mutabiq, jese ke maali istehqaq, gender norms aur maashi quwwat, ke baray mein mazeed samajh hasil ki ja sake.
Hamne Hugging Face jese open-source AI platform par mazeed tajaweez ke liye zyada taweel safar tay kiya. Yeh models off-the-shelf products ke muqablay mein user-friendly nahi the, jin ki aor ziada samajhdar user interfaces hoti hain, lekin hamare engineers ne inhe apne zarurat ke mutabiq aasani se adapt kiya. Ek set ke multi-label transformer-based AI models hamare liye khaas tor par ahem the: yeh models “seekh” sakte hain — yani ke inka maahir hona text ko samajhne mein mazeed behtareen ban sakta hai jab inhe similar text par “practice” kiya jata hai aur jab tak ke inka processing fine-tune ho jata hai. Text ki tahlil ke doran, yeh models apne ghalat jawab ko sahi karte rahe aur apne sahi jawab ko mazboot karte rahe. Lekin phir bhi kuch kamiyan thi. Maslan, yeh AI models sirf ek topic ko text se pehchante the. Agar text mein yeh likha hota hai ke “mera cassava crop nakam ho gaya tha is li
ye mujhe school fees bharnay ke liye paisay udhar lena para,” to yeh models sirf isay “agriculture” ke hawale se tag kar sakte the, jabke yeh hamare data categorization ki koshishon ke liye ahem tha ke text mein maali products aur taleemi kharch mention kiya gaya tha. To in tools mein ab tak hamare maqasid ko support karne ki quwwat nahi thi.
Phir, November 2022 mein, OpenAI ne ChatGPT ko introduce kiya. ChatGPT ko bohot zyada data par banaya gaya tha, lekin iska yeh bhi gunjaish hai ke yeh conversational memory mein words seekh sakta hai, jisme AI kisi word ko uske context ke zariye seekhta hai. Agar ek mawafiq context topic expert ke zariye diya jaye, to ChatGPT ke model ko text analysis ke liye train karne ke liye zyada data ki zarurat nahi hoti. Hum Decodis mein strong context-setting ke sath sath topic-specific training data ka growing corpus istemal karke ab ChatGPT ko istemal kar rahe hain tak ke hum kabhi se bhi zyada accuracy hasil kar sakein. Hamare NLP models ab 95% ya usse zyada levels par perform kar rahe hain — dusre models ke mukable, jo humne try kiye hain, jin ki accuracy 60-70% ke aspaas hai.
AI-POWERED NLP KE LIYE ASAL MAAYAR: TOPIC-SPECIFIC DATA AUR ‘HUMAN IN THE LOOP’ KO RAKHNA
Context-relevant modelling ke liye behtareen data sets — yani ke AI models jo text ke ma’ne ko surrounding context ke basis par pehchante hain — natural language se aate hain, jo Decodis voice-led surveys ke zariye collect karta hai. Iska matlab hai ke hamare paas aise data ka barah-e-raast izafah hota hai jise ham in AI models ko train karne ke liye istemal kar sakte hain. Jitna hum in models ko naye data par chalate hain, utni zyada unki accuracy barhti hai — jis tarah se ham apne topics se mutalliq poverty alleviation ke maamlat ke liye makhsoos in-house models bana sakte hain.
In models ki performance sirf recorded natural language ke bohot se data se nahi aati, balki isme ek process shamil hai jise human-led context setting kehte hain. Is process mein, ek qualitative researcher — yaani ke “human in the loop,” jo ke moamlat ke maamalat mein mahir hai — ek chhota set ke jawabon ko manually categorize karta hai taake ChatGPT ko dikhaya ja sake ke hum text se kya maloomat hasil karna chahte hain. Esentially, hum AI model ko text analysis ke liye train karne ke liye conversations ke building blocks farahem karte hain, phir hum ise mazeed text ko analyze karne ke liye istemal karte hain, jisme researcher tweaks karta hai taake model phir se seekhe aur apni accuracy ko mazeed barhaye. Niche diye gaye table mein is process ka ek example diya gaya hai, jisme ek chhota sa text ka tukda hai. Columns mein di gayi contextual information ek human ki taraf se di gayi hai jo financial inclusion mein mahir hai, taake NLP models ko dikhaya ja sake ke wo text mein humare maqsad ke liye kya talash karna chahiye. Yeh ek process hai jise supervised artificial intelligence kehte hain, jisme hum initially apne expertise ke mutabiq output ko direct karte hain. Pehle, input dene mein human involvement hota hai, takay AI sahi output dene mein trained ho sake. Agar context aur maloomat sahi format mein nahi di jati, to accuracy kam ho jayegi aur model galat maloomat dene lagayega, bilkul waise hi jese ChatGPT galat data ko diye gaye to karta hai. Isse bachne ke liye, process ke shuru mein human direction bohot zaroori hai, takay model ko sabse zyada accuracy ke sath kaam karne ke liye train kiya ja sake — ek zaroori khasiyat agar hum development professionals aur vulnerable populations ke darmiyan sunnayi ka fasla puri tarah se bridge karna chahte hain.
Waise to AI-based tools ne hamare NLP ke kaam par pehle se asarat dikhaye hain, lekin inke faide is tarah ke technologies jese ke develop hoti ja rahi hain, to yeh in asarat ko aur bhi barha sakte hain. Ek reh gaya sawaal yeh hai ke hum kab dekhein ge ke GPT ka aisa version aata hai jo global development space mein istemal hone wale mukhtalif zubanon ko seedha samajhne mein istemal kiya ja sake. Baaz dafa, kisi foreign text ko English mein translate karna context aur nuance ka nuksan kar sakta hai, is liye behtar hai ke hum local zubanon par seedhe NLP istemal karein. Agla GPT version — GPT-4, jo limited form mein release ho gaya hai — ko 26 zubanon par test kiya gaya hai, jisme kuch low-resource languages bhi shamil hain jin ke liye limited data available hai, jese ke Swahili, Punjabi, Marathi aur Telugu.
Lekin, in zubanon ke liye jo ke chhote amounts of data ke sath test kiye gaye hain, woh bade sets of natural language data par train nahi kiye gaye hain. Jab ke Decodis apne low-resource language text ka corpus barhata hai, local language experts ko istemal karke jo ke development contexts ke mutalliq khaas phrases ko tag karte hain, to hum in languages ke liye barhne wale AI models ko mazeed behtar taur par train kar sakte hain. Lekin doosre zubanon jese ke Luo, Twi aur Xhosa ke liye, jo ke aur bhi kam data ke sath hain aur jinhein near-term mein AI ke liye language models mein shamil nahi kiya ja sakta, to humein dobara mustaqbil ki taraf dekhna hoga — ya phir is masle ko khud hal karna hoga.
AI ki madad se Asarat Napnay Mein Taqat: Aalmi Taraqqi Tafheem-e-Mosarat Mein Zaban Barriers Ko Paar Karna
Warren Buffet ka ek izhar hai jo un logon ke liye istemal hota hai jo relative asoodgi ke haalat mein paida hue hain – un ke mutabiq, unho ne “overyan lottery” jeet li hai. Aam taur par, jo log overyan lottery jeet gaye hain, woh log hain jo un logon ki madad kar rahe hain jo is lottery mein shamil nahi hain. Lekin amuman yeh zaban aur tehqiqi rukawat paida karte hain jo do groups ke darmiyan “sunnayi ka fasla” banati hai, jo ghareebon ke masail ki samajh ko rokta hai. Agar yeh sunnayi ka fasla ko daryaft nahi kiya jaye, to ghareebon ke masail ka ilaj asar nahi kar sakta.
Artificial intelligence (AI) is sunnayi ka fasla kam karne mein aham kirdar ada kar sakta hai. Haal hi mein, ChatGPT ne duniya ki tawajju ko apni taraf kheench liya hai jese ke yeh pehla AI platform hai jo itni samajhdaar hai ke ise amm logon ne apnaya, jiski wajah se is par tajaweez, shak o shubha aur tanqeed ka dhor chala hai. Lekin ChatGPT ke peshanumaan kirdar se pehle, iske purane nasli versions ki banai gayi technology ne maliyat, insan sarmaya, marketing, telecom aur cyber security jese industries mein amal ki gayi hai. ChatGPT ke sath mila hua tajaweez aur in saalon ki tajurbat se, yeh sab sikhne ke liye ahem hain ke naye nasl ke predictive AI ko sunnayi ka fasla karna ke liye kaise istemal kiya ja sakta hai, transcribed natural language ko samajhne mein madad karte hue.
Niche hum kuch is tarah ke lessons par gaur karenge, jo yeh aane wale AI tools hamare poverty alleviation kaam par kis tarah asarat andaz ho rahe hain Decodis mein — aur is par roshni dalenge ke is jaddojahad mein insan sarmaya ko in koshishon ko rehnumai karne mein kis tarah istemal karna chahiye.
PEHLE CHATGPT: HUMARE AI-BASED TOOLS KE SHURUATI TAJURBAT
Decodis ek samaji tahqiqati company hai jo kamzor tabqon ke samna karne wale masail ke bare mein tafseelat farahem karti hai. Hamari tajurbaton ki bunyad natural speech ki taqat par hai, aur is taqat ko barhane aur tajziyaat mein madad karne wale tech ko istemal karte hain. Ham is technology ko apne clients ki tafseelati aur samaji programming, siyasi nizaam, asarat napnay aur doosre maamlat mein madad farahem karne ke liye istemal karte hain.
Hamare kaam ka bara hissa survey data collection ko behter banane mein shamil hai, taki samajh saken ke log kya keh rahe hain aur kaise keh rahe hain — ek aisa process jo unki experiences aur nazariyat ko behtar se izhaar karsakta hai, organizations ko unki khidmat behtar taur par farahem karne ki rahnumai kar sakta hai. Jab ek survey participant ek set ke open-ended sawalon ka jawab deta hai, to ham us audio ko text mein convert karte hain aur phir us text ko tahlil karte hain taake hum consistent themes aur topics ko nikal sakein. Yeh process qeemti idaray dikhata hai, lekin yeh bohot mehnat ka kaam hai aur ise bina AI ke madad ke karna mumkin nahi hai. Ham hazaron respondents se is survey data ko collect karte hain, jise amuman kai alag zuban mein kiya jata hai, aur jo ke bohot zyada hota hai, jise koi researchers manual taur par nahi kar sakta.
Is process ko streamline karne ke liye, hum Natural Language Processing (NLP) ka istemal karte hain, jo hamain yeh anumati deta hai ke ham itna text do din mein tahlil kar sakein jise ek qualitative researcher do mahinon mein kar sakta hai. Hamne off-the-shelf products dwara diye gaye rule-based topic classification ka istemal karke is process ko simple banaya. Lekin ham pareshan the ke is tarah ke models ki accuracy ko kaise calculate kiya jaye, jo hamare efforts ko mazeed taqat dete. Hamare liye zaroori tha ke hamare nizaam mein shaamil hone wale nuance ke mutabiq, jese ke maali istehqaq, gender norms aur maashi quwwat, ke baray mein mazeed samajh hasil ki ja sake.
Hamne Hugging Face jese open-source AI platform par mazeed tajaweez ke liye zyada taweel safar tay kiya. Yeh models off-the-shelf products ke muqablay mein user-friendly nahi the, jin ki aor ziada samajhdar user interfaces hoti hain, lekin hamare engineers ne inhe apne zarurat ke mutabiq aasani se adapt kiya. Ek set ke multi-label transformer-based AI models hamare liye khaas tor par ahem the: yeh models “seekh” sakte hain — yani ke inka maahir hona text ko samajhne mein mazeed behtareen ban sakta hai jab inhe similar text par “practice” kiya jata hai aur jab tak ke inka processing fine-tune ho jata hai. Text ki tahlil ke doran, yeh models apne ghalat jawab ko sahi karte rahe aur apne sahi jawab ko mazboot karte rahe. Lekin phir bhi kuch kamiyan thi. Maslan, yeh AI models sirf ek topic ko text se pehchante the. Agar text mein yeh likha hota hai ke “mera cassava crop nakam ho gaya tha is li
ye mujhe school fees bharnay ke liye paisay udhar lena para,” to yeh models sirf isay “agriculture” ke hawale se tag kar sakte the, jabke yeh hamare data categorization ki koshishon ke liye ahem tha ke text mein maali products aur taleemi kharch mention kiya gaya tha. To in tools mein ab tak hamare maqasid ko support karne ki quwwat nahi thi.
Phir, November 2022 mein, OpenAI ne ChatGPT ko introduce kiya. ChatGPT ko bohot zyada data par banaya gaya tha, lekin iska yeh bhi gunjaish hai ke yeh conversational memory mein words seekh sakta hai, jisme AI kisi word ko uske context ke zariye seekhta hai. Agar ek mawafiq context topic expert ke zariye diya jaye, to ChatGPT ke model ko text analysis ke liye train karne ke liye zyada data ki zarurat nahi hoti. Hum Decodis mein strong context-setting ke sath sath topic-specific training data ka growing corpus istemal karke ab ChatGPT ko istemal kar rahe hain tak ke hum kabhi se bhi zyada accuracy hasil kar sakein. Hamare NLP models ab 95% ya usse zyada levels par perform kar rahe hain — dusre models ke mukable, jo humne try kiye hain, jin ki accuracy 60-70% ke aspaas hai.
AI-POWERED NLP KE LIYE ASAL MAAYAR: TOPIC-SPECIFIC DATA AUR ‘HUMAN IN THE LOOP’ KO RAKHNA
Context-relevant modelling ke liye behtareen data sets — yani ke AI models jo text ke ma’ne ko surrounding context ke basis par pehchante hain — natural language se aate hain, jo Decodis voice-led surveys ke zariye collect karta hai. Iska matlab hai ke hamare paas aise data ka barah-e-raast izafah hota hai jise ham in AI models ko train karne ke liye istemal kar sakte hain. Jitna hum in models ko naye data par chalate hain, utni zyada unki accuracy barhti hai — jis tarah se ham apne topics se mutalliq poverty alleviation ke maamlat ke liye makhsoos in-house models bana sakte hain.
In models ki performance sirf recorded natural language ke bohot se data se nahi aati, balki isme ek process shamil hai jise human-led context setting kehte hain. Is process mein, ek qualitative researcher — yaani ke “human in the loop,” jo ke moamlat ke maamalat mein mahir hai — ek chhota set ke jawabon ko manually categorize karta hai taake ChatGPT ko dikhaya ja sake ke hum text se kya maloomat hasil karna chahte hain. Esentially, hum AI model ko text analysis ke liye train karne ke liye conversations ke building blocks farahem karte hain, phir hum ise mazeed text ko analyze karne ke liye istemal karte hain, jisme researcher tweaks karta hai taake model phir se seekhe aur apni accuracy ko mazeed barhaye. Niche diye gaye table mein is process ka ek example diya gaya hai, jisme ek chhota sa text ka tukda hai. Columns mein di gayi contextual information ek human ki taraf se di gayi hai jo financial inclusion mein mahir hai, taake NLP models ko dikhaya ja sake ke wo text mein humare maqsad ke liye kya talash karna chahiye. Yeh ek process hai jise supervised artificial intelligence kehte hain, jisme hum initially apne expertise ke mutabiq output ko direct karte hain. Pehle, input dene mein human involvement hota hai, takay AI sahi output dene mein trained ho sake. Agar context aur maloomat sahi format mein nahi di jati, to accuracy kam ho jayegi aur model galat maloomat dene lagayega, bilkul waise hi jese ChatGPT galat data ko diye gaye to karta hai. Isse bachne ke liye, process ke shuru mein human direction bohot zaroori hai, takay model ko sabse zyada accuracy ke sath kaam karne ke liye train kiya ja sake — ek zaroori khasiyat agar hum development professionals aur vulnerable populations ke darmiyan sunnayi ka fasla puri tarah se bridge karna chahte hain.
Waise to AI-based tools ne hamare NLP ke kaam par pehle se asarat dikhaye hain, lekin inke faide is tarah ke technologies jese ke develop hoti ja rahi hain, to yeh in asarat ko aur bhi barha sakte hain. Ek reh gaya sawaal yeh hai ke hum kab dekhein ge ke GPT ka aisa version aata hai jo global development space mein istemal hone wale mukhtalif zubanon ko seedha samajhne mein istemal kiya ja sake. Baaz dafa, kisi foreign text ko English mein translate karna context aur nuance ka nuksan kar sakta hai, is liye behtar hai ke hum local zubanon par seedhe NLP istemal karein. Agla GPT version — GPT-4, jo limited form mein release ho gaya hai — ko 26 zubanon par test kiya gaya hai, jisme kuch low-resource languages bhi shamil hain jin ke liye limited data available hai, jese ke Swahili, Punjabi, Marathi aur Telugu.
Lekin, in zubanon ke liye jo ke chhote amounts of data ke sath test kiye gaye hain, woh bade sets of natural language data par train nahi kiye gaye hain. Jab ke Decodis apne low-resource language text ka corpus barhata hai, local language experts ko istemal karke jo ke development contexts ke mutalliq khaas phrases ko tag karte hain, to hum in languages ke liye barhne wale AI models ko mazeed behtar taur par train kar sakte hain. Lekin doosre zubanon jese ke Luo, Twi aur Xhosa ke liye, jo ke aur bhi kam data ke sath hain aur jinhein near-term mein AI ke liye language models mein shamil nahi kiya ja sakta, to humein dobara mustaqbil ki taraf dekhna hoga — ya phir is masle ko khud hal karna hoga.