AI aur data ko lekar ek bada misconception hai. JBS Dev ke president Joe Rose ne is myth ko tod diya hai. Unka kehna hai ki generative aur agentic AI systems ke saath kaam karne ke liye perfect data hona zaroori nahi hai.
AI News ke mutabiq, Rose ne bataya ki vendors aur consultants aksar suggest karte hain ki aapko bade data lakes aur multi-year data transformation programmes chahiye. Iski wajah se executives confused ho jaate hain.
AI mein imperfect data ka myth – kya sach hai?
Rose ka kehna hai ki reality thodi different hai. "The tooling has never been better than it is now to deal with poor quality data," unhone kaha. Unka kehna hai ki LLMs half-written prompts ko bhi samajh sakte hain, jo remarkable hai.
Yeh baat samajh mein aati hai. Agar aapke paas aisa tool available hai, toh woh worth hai. Iska matlab yeh nahi ki aap data quality ko ignore kar sakte hain, lekin aapko perfect data ka wait karne ki zaroorat nahi hai.
AI last mile – model capability se cost sustainability tak
Joe Rose ne AI last mile ke baare mein bhi baat ki. Unka focus hai ki model capability se cost sustainability tak ka safar kaise complete kiya jaye. Iska matlab hai ki sirf model banana kaafi nahi hai, usko deploy karke sustainable banana bhi important hai.
AI systems ko production mein lana aur unhe cost-effective banana – yeh hai asli challenge. Rose ke mutabiq, imperfect data ke saath kaam karna seekhna is journey ka ek important part hai.
Hamaari Baat: AI mein perfect data ka wait karna band karo
Joe Rose ki baat bilkul sahi lagti hai. AI industry mein ek trend hai ki log perfect data ka wait karte hain, lekin aisa kabhi hota nahi. Reality yeh hai ki data hamesha imperfect rahega. LLMs ne is game ko change kar diya hai – woh ab half-baked prompts aur messy data ke saath bhi kaam kar sakte hain.
Hamari nazar mein, companies ko ab wait karne ki bajay action lena chahiye. AI tools aaj pehle se zyada capable hain. Cost sustainability ke liye bhi early adoption important hai. Jitna late karoge, utna zyada competitive disadvantage hoga.
