Injin AI-Oracle: Tsarin Lissafi Mai Hikima

1 Gabatarwa
2 Bayyani Game da Injinan AI-Oracle
- 2.1 Muhimman Sassa
- 2.2 Sarrafa Ayyukan Tambaya
3 Cikakkun Bayanai na Fasaha da Tsarin Lissafi
4 Sakamakon Gwaji da Aiki
5 Misalin Aiwar Code
6 Ayyuka na Gaba da Jagorori
7 Nassoshi
8 Bincike na Asali

1 Gabatarwa

Injinan AI-oracle sun ƙarfafa Injinan Turing Oracle (OTMs) ta hanyar maye gurbin tsoffin masu bada shawara na al'ada da samfuran AI kamar LLM, LRM, da LVM. Waɗannan injinan suna amfani da ilimin AI da iyawar fassara don warware ayyuka masu sarƙaƙiya yayin magance batutuwa kamar amincin fitarwa ta hanyar algorithm na kafin tambaya da bayan amsa.

2 Bayyani Game da Injinan AI-Oracle

An ayyana injin AI-oracle M a matsayin OTM tare da saitin samfuran AI a matsayin mai bada shawara, wanda ake nuna shi da O_M. Abin shiga shine tuple (T, Q), inda T shine bayanin gaskiya (rubutu ko fayilolin gani) kuma Q shine bayanin aiki. M yana sarrafa tambayoyi daidai ko ba daidai ba don kammala ayyukan tambaya.

2.1 Muhimman Sassa

Mai bada shawara O_M ya haɗa da samfura irin su GPT-4o (LLM), GPT-o1 (LRM), da DALL-E 3 (LVM). Algorithm na kafin tambaya yana tsara bayanai kuma yana samun sakamako na tsakiya, yayin da algorithm na bayan amsa yana tabbatar da amsoshi akan T.

2.2 Sarrafa Ayyukan Tambaya

Ana samar da tambayoyi a jere, tare da binciken bayan amsa yana tabbatar da daidaito. Misali, a cikin aikin binciken lafiya, LRM na iya yin tunani ta hanyar alamun cuta, kuma algorithm na bayan amsa suna kwatanta sakamako da jagororin likitanci.

3 Cikakkun Bayanai na Fasaha da Tsarin Lissafi

Injin AI-oracle M yana lissafawa kamar haka: $M(T, Q) = ext{PostAnswer}( ext{PreQuery}(Q), O_M)$, inda PreQuery ke canza Q zuwa ƙananan tambayoyi, kuma PostAnswer yana tabbatar da abubuwan da aka fitar. An auna daidaito kamar haka $A = rac{ ext{Amsoshi Masu Daidai}}{ ext{Jimlar Tambayoyi}}$.

4 Sakamakon Gwaji da Aiki

A cikin gwaje-gwaje, injinan AI-oracle sun sami daidaiton kashi 92% akan ayyukan tunani masu ma'ana ta amfani da LRM, idan aka kwatanta da kashi 78% na LLM masu zaman kansu. Taswira (Hoto 1) tana nuna ribar aiki a cikin ayyuka kamar bayyana hoto (LVM + binciken bayan amsa sun inganta dacewa da kashi 30%).

5 Misalin Aiwar Code

class AIOracleMachine:
    def __init__(self, ai_models):
        self.oracle = ai_models  # List of AI models (LLM, LRM, LVM)
    def pre_query(self, task):
        # Break task into sub-queries
        return sub_queries
    def post_answer(self, responses, ground_truth):
        # Validate responses
        return validated_results
    def compute(self, T, Q):
        sub_queries = self.pre_query(Q)
        responses = [self.oracle.query(q) for q in sub_queries]
        return self.post_answer(responses, T)

6 Ayyuka na Gaba da Jagorori

Yuwuwar ayyuka sun haɗa da tsarin cin gashin kai (misali, motocin tuƙa kai ta amfani da LVM don hangen nesa na ainihi) da kiwon lafiya (misali, kayan aikin bincike tare da LRM). Aikin gaba yakamata ya mayar da hankali kan haɓakawa da haɗa sabbin samfuran AI kamar lissafin neuromorphic.

7 Nassoshi

Wang, J. (2024). Injinan AI-Oracle don Lissafi Mai Hikima. arXiv:2406.12213.
Turing, A. M. (1939). Tsarin Tunani Dangane da Ordinals.
Brown, T., et al. (2020). Samfuran Harshe Ƙwararrun Malamai. NeurIPS.
OpenAI. (2023). Rahoton Fasaha na GPT-4. OpenAI.

8 Bincike na Asali

Gaskiya A Kai Tsaye: Wannan takarda ba wani atisaye ne kawai na ka'ida—tsari ne mai aiki don kame yanayin baƙar fata na AI na zamani. Ta hanyar sanya samfuran AI a matsayin "masu bada shawara" a cikin ingantaccen tsarin Turing, Wang ya magance babbar matsala: yadda ake amfani da ƙarfin AI ba tare da mika wuya ga rashin tabbas ba. Sarkar Ma'ana: Hujja tana ginu a hankali: fara da ingantaccen ra'ayi na OTM, musanya ma'anar mai bada shawara don takamaiman samfuran AI (LLM/LRM/LVM), sannan a saka algorithm na kafin/bayan sarrafawa a matsayin shinge. Wannan yana haifar da tsarin rufaffiyar madauki inda ake warware ayyuka, aiwatar da su, kuma a tabbatar da su a jere—kamar yadda Google's AlphaCode ke warware matsalolin code amma tare da fa'ida mai faɗi. Abubuwan Haske da Ra'ayi: Babban motsi shine ɗaukar AI a matsayin ɓangaren da aka haɗa maimakon mafita har zuwa ƙarshe, yana ba da damar tsarin hikima gauraye. Tsarin tabbatarwa na bayan amsa yana da wayo musamman, yana maimaita dabarun daga tabbatarwa na yau da kullun. Duk da haka, takardar ta yi watsi da ɗaurin lissafi—tsara samfuran AI da yawa tare da bincike na ainihi ba mai arha bane. Hakanan tana ɗauka cewa bayanin gaskiya koyaushe yana samuwa, wanda galibi ba gaskiya bane (misali, a cikin ayyukan ƙirƙira). Idan aka kwatanta da tsari irin su Microsoft's AutoGen, wanda ke mai da hankali kawai kan haɗin gwiwar LLM, wannan hanyar ta fi cikewa amma ba ta aiki kai tsaye ba. Ƙwararren Gudunmawa: Ga kamfanoni, wannan yana nufin farawa da yankuna masu ƙarancin matsayi kamar sarrafa takardu don gina amana a cikin sassan tabbatarwa. Masu bincike yakamata su ba da fifikon ingantaccen inganci—watakila aro daga koyon tarayya—don sanya wannan ya yi aiki don na'urori na gefe. Gaskiyar nasara za ta zo lokacin da muka daina ɗaukar AI a matsayin mai bada shawara kuma mu fara ɗaukarsa a matsayin ɓangaren horo a cikin tsarin da aka sarrafa.

Table of Contents