AI/ML ya bambanta da shirye-shiryen tsari na gida ta wane hanya?

Question

Accepted Answer

**Shirye-shiryen tsari na gida** suna amfani da ka'idoji masu bayani da masu bada shiri suka rubuta, yayin da **ML** ya koyi alamu daga bayanai. Bambantacciyar asali shine cewa sistemon ML ana horar da su akan bayanai maimakon a rubuta su da ka'idoji — halin da yawayi dabam don warware matsaloli.

## Bambantacciyar asali

```text
TRADITIONAL PROGRAMMING → developers write explicit RULES (logic) → 
  RULES + INPUT → OUTPUT (the program follows the coded logic)
  → you specify exactly HOW to solve the problem (step by step)
MACHINE LEARNING → the system LEARNS rules/patterns from DATA →
  DATA + EXAMPLES (input + output) → a trained MODEL → MODEL + INPUT → OUTPUT
  → you provide examples; the model learns the patterns (you don't code the logic)
→ programming: code the logic; ML: learn the logic from data
```

## Lokacin da ake amfani da kowane

```text
TRADITIONAL → problems with clear, expressible RULES (logic you can write):
  → e.g. calculations, business rules, deterministic logic → precise, predictable, explainable
ML → problems where rules are HARD to specify but PATTERNS exist in data:
  → e.g. image recognition, language, recommendations, prediction → patterns too complex
    to hand-code
→ use ML when you can't easily write the rules but have data showing the patterns
```

## Mahimman sakamako

```text
→ ML is PROBABILISTIC/approximate (predictions, can be wrong) vs traditional's deterministic
  exactness
→ ML needs DATA (quality/quantity matters); traditional needs clear requirements/logic
→ ML can be a 'black box' (hard to explain); traditional logic is explicit/traceable
→ they COMPLEMENT each other (most systems use both)
```

## Me yasa ya matuwa

Jiyin alamar yadda AI/ML ya bambanta da shirye-shiryen tsari na gida yana da kima saboda yana **fayyace halin da yawayi dabam na ML**, yana taimakawa jiya lokacin da ta amfani da ita, saboda haka ita mahimman ilimi na tinkarita.

Bambantacciyar asali — **shirye-shiryen tsari na gida** na amfani da ka'idoji masu bayani da masu bada shiri suka rubuta (sanin daidaitaccen hanyar warware matsala) ga **ML** na koyi alamu daga bayanai (bubugi misalan kuma barin jiya model yakoyi lo'akari) — yana wakiltar toshe baba na paradigm a halin da aka warware matsaloli (rubuta lo'akari da koyi dashi daga bayanai).

Jiyin alamar wannan bambanci shine tushe na jiya AI/ML kuma yadda ya bambanta.

Jiyin alamar **yagate lokacin da ake amfani da kowane** — shirye-shiryen tsari na gida don matsaloli masu tsarki, fasali ka'idoji (lissafi, siyasar ciniki — daidai kuma bayyani), da **ML don matsaloli inda ka'idoji suke da wuyar rubuta amma alamu akwai a cikin bayanai** (gane hoto, harshe, tunatarwa — alamu masu rikitarwa da ba za a iya rubuta su da hannu) — shine mahimman gani ture don gane lokacin ML shine halin da ya dace (lokacin da ba za ka iya rubutu daidai ka'idoji amma akwai bayanai nuna alamu).

Jiyin alamar **mahimman sakamakonin** — cewa ML yana masu kira kuma kimanin (tsinkaya da za ta iya kuskure) ga halin da gida na yin haba haba daidai, cewa ML na butuwa bayanai yayin da gida na butuwa lo'akari, cewa ML zai iya zama akwati mara sanin yayin da gida ya iya wakilta, kuma suna taimakunansu (salle sistemi aiki ayyukan biyu) — yana nuna mahimman bambantaccinsa na aiki da musaya.

Wannan jiyin alamar yana fayyace halin da yawayi dabam na ML kuma yagate shi ya dace.

Kamar yadda ML yana wakiltar bambantacciyar paradigm dashi daga shirye-shiryen tsari na gida (koyi dashi daga bayanai da rubuta ka'idoji) kuma jiyin alamar bambanci, yagate lokacin ake amfani da kowane, kuma sakamakonin (ƙimar ƙimar, dogaro akan bayanai, kasa bayyana daidai) ya kasance muhimmi don jiya kuma aiwatarwa ML da ikirari, jiyin alamar yadda AI/ML ya bambanta da shirye-shiryen tsari na gida yana da kima, tushe ilimi — yana fayyace halin da yawayi dabam na ML (koyi alamu daga bayanai ga rubuta ka'idoji masu bayani), mahimmi don jiya yagate ML ta dace (wuyar tsare ka'idoji tare da bayanai masu) kuma tushe ilimi na tinkarita don aiki da rubu akifo AI/ML.