Alesi Avũ ɖea vodadawo ɖa le kaka si li ke ŋu (2023)

Le vodadawo ɖeɖeɖa ŋutinya siwo womenɔ mɔ kpɔm na o wu le AI ƒe ŋutinya me dometɔ ɖeka me la, aƒe me avu aɖe kpe ɖe mɔ̃ɖaŋudɔwɔlawo ŋu le manyamanya me be woade dzesi yamenutome ɣaɣla ƒe tɔtɔ vevi aɖe le Stable Diffusion ƒe nɔnɔmetata dzidzi ƒe mɔ̃ me. Ƒe 2023 ƒe nudzɔdzɔa va zu nudzɔdzɔ ɖedzesi aɖe le alesi xexeame ŋutɔŋutɔ ƒe nusiwo womate ŋu agblɔ ɖi o ate ŋu aɖe vodada siwo gaƒoƒo akpe geɖe ƒe dodokpɔ si wowɔ ɖe ɖoɖo nu to vovo kura.

Nuka tututue Dzɔ Le Avũ Kple Kaka Si Li Ke Ŋu?

Le ƒe 2023 ƒe gɔmedzedze la, mɔ̃ɖaŋudɔwɔla aɖe si nɔ dɔ wɔm tso aƒeme de dzesi nu tɔxɛ aɖe. Woƒe avu, esi wòzɔ to keyboard la dzi le Stable Diffusion hehexɔxɔ ƒe duɖimekeke me ta la, tsɔ ŋɔŋlɔdzesi siwo me susu mele o ƒe ka aɖe va ƒuƒoƒo aɖe si wowɔ kaba me. Le esi teƒe be wòawɔ nusiwo to vovo alo atsɔ vodada aɖe aƒu gbe la, kpɔɖeŋua wɔ nɔnɔmetata siwo kplɔ wo nɔewo ɖo kple nukpɔkpɔ ƒe asinudɔwɔwɔ si mewɔa tɔtrɔ o eye wòle tẽ ŋutɔ — tessellation ƒe kpɔɖeŋu si gbugbɔna si mele be wòanɔ anyi hafi ne wotsɔ nya siwo wotsɔ de eme kaba o.

Esia menye toɣliɖeɖe si dzɔ le vome o. Kpɔɖeŋua ɖe akpaɖekedzimademade aɖe si womede dzesii tsã o fia le kpɔɖeŋua ƒe cross-attention layers me, vevietɔ le alesi U-Net ƒe xɔtuɖaŋu wɔa dɔ tso dzesi ƒe ƒuƒoƒo aɖewo siwo dze le gbegbɔgblɔ ƒe liƒo siwo sɔ godo ŋu. Avũa ƒe keyboard mashing wɔ nyuie be tsitretsiɖeŋu ƒe ʋuʋu si amegbetɔ dodokpɔwɔla aɖeke mesusu be yeate kpɔ o, si klo nu le vodada aɖe si le kpɔɖeŋua ƒe CLIP nuŋɔŋlɔ encoder ƒe ƒoƒo ɖekae me si kpɔ ŋusẽ ɖe alesi wobua teƒe ƒe ƒomedodowo le toɣliɖeɖe ɖeɖeɖa ƒe ɖoɖoa me dzi.

Mɔ̃ɖaŋudɔwɔlawo ƒe ƒuƒoƒoa zã kwasiɖa siwo kplɔe ɖo tsɔ di asinudɔwɔwɔa yi eƒe ke gbɔ: nya aɖe si le tsia dzi ƒe goglome le kaka ƒe ɖoɖowɔɖi ɣaɣla me si dzena le tokenization edge cases tɔxɛwo te ko. Dzadzraɖoa na nɔnɔmetata ƒe ɖekawɔwɔ nyo ɖe edzi le nyabiase ƒomeviwo katã me to akɔntabubu si nye 3-4%, si nye viɖe gã aɖe le vidzidzi AI ƒe dɔwɔwɔ me.

Nukatae Nyatakaka Siwo Mebɔ O Lãa Nudzodzoe Siwo QA Ƒuƒoƒowo To?

Dodokpɔ si woɖo ɖe ɖoɖo nu kplɔa amegbetɔ ƒe susuŋudɔwɔwɔ ɖo. Mɔ̃ɖaŋudɔwɔlawo ŋlɔa dodokpɔ ƒe nɔnɔmewo ɖe zãla ƒe nuwɔna si wokpɔ mɔ na, ​​nugbɔ ƒe nɔnɔme siwo woate ŋu asusu, kple kpododonu ƒe nɔnɔme siwo wonya tso gbugbɔgawɔ siwo va yi me dzi. Gake kɔmpiutadziɖoɖowo — vevietɔ AI ɖoɖo siwo si nɔnɔme biliɔn geɖe le — me le nɔnɔme siwo ate ŋu adzɔ ƒe wowó ƒokpli si dodokpɔ ƒe ɖoɖo aɖeke mate ŋu atsyɔ bliboe o.

ƒe nyawo

"Nu gbegblẽ siwo me afɔku le wu la menye esiwo ɣla ɖe kɔda si mèdo kpɔ o me o. Woawoe ɣla ɖe kɔda si nèdo kpɔ kple susu gbegblẽwo me." — Gɔmeɖose sia, si gɔme wose ɣeyiɣi didi aɖe le kɔmpiuta dɔwɔɖoɖo xoxowo me la, va zua nu vevi aɖe le mɔ̃wo ƒe nusɔsrɔ̃ ƒe ɖoɖowo me afisi nusiwo wotsɔ dea eme ƒe teƒea seɖoƒe meli na o le nyateƒe me.

Avũ ƒe nudzɔdzɔa do ŋusẽ nusi tɔtɔ ƒe mɔ̃ɖaŋudɔwɔlawo nya ƒe geɖe: nusiwo wotsɔ de eme le vome, siwo womate ŋu agblɔ ɖi o la ɖea ɖoɖowɔɖi ƒe gbɔdzɔgbɔdzɔ siwo dodokpɔmɔnu mate ŋu awɔ o la fiana. Gɔmeɖose ma ke si le megbe na fuzz dodokpɔ, afisi woɖoe koŋ tsɔa nyatakaka siwo womewɔ nyuie o la dea ɖoɖowo me be woatsɔ aɖe afɔkuwo afia. Vovototo si le afisia enye be afɔ ene kple asike nɔ fuzzer la si.

Nukae Esia Ðe Ðe Go Tso AI Debugging Kuxiwo Ŋu?

Debugging generative AI models to vovo kura tso debugging dekɔnu software gbɔ. Ne dɔwɔnu si wozãna ɖaa do kpo nu la, èxɔa vodadawo ŋuti nuŋlɔɖi, stack trace, mɔ si woate ŋu agbugbɔ awɔ. Ne AI ƒe kpɔɖeŋu aɖe wɔ nusiwo mesɔ o le mɔ si menya kpɔna dzea sii bɔbɔe o nu la, kpododonua ateŋu adzo ɣleti geɖe elabena ŋuɖoɖo ɖeka "si sɔ" aɖeke meli woatsɔ asɔ kplii o.

ƒe nyawo
• Latent space opacity: Ememe nɔnɔmetata siwo le kaka ƒe kpɔɖeŋuwo me la gɔmeɖeɖe sesẽ, si wɔe be esesẽna be woadi nusiwo woɖe tso eme ƒe asinudɔwɔwɔwo tso akɔntabubu ƒe kpododonu tɔxɛwo gbɔ.
• Seselelãme kaba: Tɔtrɔ suesuesuewo le nuŋɔŋlɔwo dede me ateŋu ahe emetsonu siwo to vovo kura vɛ, si fia be nudzodzoewo ateŋu ado le nɔnɔme kpui siwo womateŋu agblɔ ɖi o me ko.
• Evaluation subjectivity: To vovo na hatsotsowo me toto ƒe dɔ siwo ƒe nyateƒetoto woate ŋu adzidze la, nɔnɔmetatawo wɔwɔ ƒe nyonyome ƒe akpa aɖe nye subjectivity, si wɔnɛ be nugbegblẽ siwo menya kpɔna dzea sii bɔbɔe o la te ŋu ƒua du to automated checks me.
• Cascading dependencies: Vodada ɖeka si le nuŋɔŋlɔ ƒe encoder me ateŋu akaka to cross-attention mechanism, denoising scheduler, kple VAE decoder me, si ana root cause analysis sesẽ ŋutɔ.
• Hehenana nyatakakawo ƒe ƒoƒo ɖe wo nɔewo ŋu: Vovototodede vodada siwo le kpɔɖeŋu ƒe xɔtuɖaŋu me kple biase siwo ƒe dome wonyi tso hehenana nyatakakawo me dome bia beléle na ablation numekuku siwo xɔa ɣeyiɣi eye wòxɔa ga geɖe le akɔntabubu me.

Aleke Nudzɔdzɔ sia Kpɔ ŋusẽ ɖe AI ƒe ŋgɔyiyi ƒe nuwɔnawo dzi?

Avuwo ƒe vodadawo ɖeɖeɖa ŋutinya, togbɔ be enye nukokoe le gotagome hã la, eʋã tɔtrɔ ŋutɔŋutɔ geɖe le alesi AI ƒuƒoƒowo te ɖe nyonyome ƒe kakaɖedzi ŋui me. Tso ɣemaɣi la, habɔbɔ geɖewo keke woƒe fuzz dodokpɔ ƒe ɖoɖowo ɖe enu na vidzidzi ƒe kpɔɖeŋuwo, vevietɔ wotsɔ dzesi ƒe ɖoɖo siwo wowɔ le vome kple esiwo tsi tre ɖe wo nɔewo ŋu siwo srɔ̃a nusiwo menye gbegbɔgblɔ ƒe nyawo o la de eme. Some teams now run automated "keyboard walk" simulations as part of their continuous integration pipelines.

Nudzɔdzɔa gatsɔ ɖe le gɔmeɖeɖe dɔwɔnuwo me na kaka ƒe kpɔɖeŋuwo hã. Ne ɖe nusi wokpɔna la medze ƒã o — amadede ƒe tɔtrɔ si menya kpɔna dzea sii bɔbɔe o tsɔ wu be wòanye tessellation dzinɔameƒotɔe — la, anye ne womade dzesii o tegbee. Esia ƒoe ɖe nutoa me tɔwo nu be woato nusiwo mesɔ o didi le wo ɖokui si nyuie wu vɛ na nusiwo wowɔ, ɖoɖo siwo ateŋu ade dzesi akɔntabubu ƒe masɔmasɔwo ne nɔnɔmetata ɖekaɖekawo dze abe ɖe wosɔ le gotagome ene gɔ̃ hã.

Le ƒuƒoƒo siwo le dɔwɔwɔ ƒe ɖoɖo sesẽwo dzi kpɔm le AI ƒe ŋgɔyiyi, adzɔnuwo gbugbɔgawɔ, kple nyonyome ƒe kakaɖedzi katã me gome la, nudzɔdzɔ siawo tɔgbe te gbe ɖe alesi wòhiã be woakpɔ dɔwɔwɔ ƒe dzedzeme le teƒe ɖeka dzi. Ne vodada aɖe xɔ nuŋɔŋlɔ ƒe encoder, ɖoɖowɔɖiwɔla, kple decoder la, numekukua yometiti le dɔwɔnu siwo kaka kple kadodomɔnu siwo metso kadodo me o dzi wɔa eya ŋutɔ ƒe masɔmasɔ ƒe ƒuƒoƒo.

Nyabiase Siwo Wobiana Enuenu

Ðe Stable Diffusion cat debugging nudzɔdzɔa nye nudzɔdzɔ ŋutɔŋutɔa?

Ðe fuzz dodokpɔ ate ŋu alé nugbagbeviwo kakaɖedzitɔe le generative AI models mea?

Fuzz dodokpɔ wɔa dɔ nyuie le vodada ƒe hatsotso aɖewo léle me, vevietɔ esiwo ku ɖe nyawo tsɔtsɔ de eme ƒe vovototodedeameme, tokenization edge cases, kple xexlẽdzesiwo ƒe liƒo nyawo ŋu. Gake menye klosalotue wònye na AI si dzia vi o. Esi wònye be kpɔɖeŋu siawo naa nusiwo ateŋu adzɔ tsɔ wu esiwo woɖo ɖi ta la, nusi nye "kpododonu" gɔmeɖeɖe le fuzz dodokpɔ me bia be woawɔ anomaly detection system deŋgɔwo tsɔ wu be woawɔ pass/fail assertions bɔbɔewo.

Aleke AI ƒe ƒuƒoƒo bibiwo kpɔa dɔwɔwɔ ƒe vodadawo ɖɔɖɔɖo dzi le ɖoɖo sesẽwo me?

Most mature AI teams rely on a combination of experiment tracking platforms, centralized logging, collaborative documentation, and structured project management. Kuxi vevitɔ enye be woalé traceability me ɖe asi — tsɔtsɔ do ƒome kple output artifact tɔxɛ aɖe kple model version, hehenana data, hyperparameters, kple code commit si wɔe. Ƒuƒoƒo siwo ƒoa dɔwɔwɔ ƒe ɖoɖo siawo nu ƒu ɖe dɔwɔwɔ ƒe ɖoɖo ɖeka me la zãa ɣeyiɣi sue aɖe ŋutɔ ɖe ɖoɖowɔwɔ ƒe gazazãwo ŋu eye wozãa ɣeyiɣi geɖe ɖe kuxiwo gbɔ kpɔkpɔ ŋutɔŋutɔ ŋu.

Na Wò Dɔwɔwɔ ƒe Sesẽme Nanɔ bɔbɔe

Eɖanye be èle AI ƒe kpɔɖeŋuwo ƒe vodadawo ɖɔlim alo nèle asitsatsa ƒe dɔwɔwɔ sesẽ bubu ɖesiaɖe dzi kpɔm o, dɔwɔnu siwo ma ɖe akpa vovovowo me wɔa tamebubu si me mama le. Mewayz tsɔa modules 207 siwo wotsɔ wɔ ɖekae vaa asitsadɔwɔɖoɖo ɖeka si dzi ezãla siwo wu 138,000 ka ɖo me — si naa wò ƒuƒoƒoa kpɔa nukpɔkpɔ le teƒe ɖeka si hiã be woatsɔ adi kuxiwo ɖe woƒe dzɔtsoƒe, awɔ ɖoɖo ɖe ŋuɖoɖowo ŋu, eye woaʋu kabakaba wu. Dze wò dodokpɔ femaxee gɔme le app.mewayz.com eye nàkpɔ alesi dɔwɔwɔ ɖekae sena le wo ɖokui me.