Izinketho Eziyisithupha Wonke Unjiniyela We-AI Okufanele Azenze (futhi Akekho Ofundisayo)

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Izinketho Eziyisithupha Wonke Unjiniyela We-AI Okufanele Azenze (futhi Akekho Ofundisayo)

ukukufundisa ukwenza imodeli olunembile. Abavamile ukukufundisa izinqumo eza ngemva kwalokho.

Wazi kanjani ukuthi kufanele nini ngokugcwele zenzekela okuthile ngokumelene nokugcina umuntu ku-loop?

Nini ukugqugquzela yeka ukwanela futhi ukuhleleka Kwayo zifaneleke izindleko? Kusho ukuthini ngempela ukukhetha inkomba yesikhathi sangempela phezu kwenqwaba lapho umthethosivivinywa ufika?

Le mibuzo ayiveli emsebenzini wezifundo. Zibonisa iviki lakho lokuqala ekukhiqizeni!

Lesi sihloko siyahamba 6 ukuhwebelana ezibonakala phakathi ukukhiqiza Umsebenzi we-AI. Konke kusekelwe ucwaningo lwakamuva, ukuze uthole amazwibela okuthi abantu babhekana kanjani nalokhu kuhwebelana okuvamile.

Azikho izimpendulo ezifanele lapha. Kukhona ozimele abawusizo, izinombolo zangempela, kanye nohlobo lomongo okwenza isinqumo esilandelayo sisheshe.

Yakha vs. Thenga Ngenkathi ye-LLM (Uma ukushayela i-API kuyeka ukwenza umqondo)
Imodeli Eyinkimbinkimbi vs. Ukugcinwa (Ubani olungisa lokhu ezinyangeni eziyisi-6?)
Ubuningi Bedatha vs. Ikhwalithi Yedatha (Idatha eyengeziwe ayihlali iyimpendulo)
I- throughput vs. Ukubambezeleka (Iqoqo noma isikhathi sangempela)
I-Prompt Engineering vs. Ukushuna Kahle (Amajika amabili okutshala ahluke kakhulu)
I-Automation vs. Human Oversight (Uyethemba kangakanani imodeli ukuthi izosebenza yodwa?)

Sawubona! Igama lami nginguSara Nóbrega futhi ngikufundisa ukuthi ungaba kanjani umsebenzisi wamandla we-AI ku Funda i-AI. Kumahhala ukubhalisa!

1. Yakha vs. Thenga Ngenkathi ye-LLM

Uma ushayela i-API iyeka ukwenza umqondo

I endala Inguqulo yalo mbuzo yayiwukuthi: ingabe siqeqesha imodeli yethu? Lowo uxazululiwe kakhulu. Cishe akekho osaqeqeshwa kusukela ekuqaleni.

I 2026 inguqulo inzima.

Unayo 3 ongakhetha kukho manje: shayela i-API, lungisa kahle imodeli yomthombo ovulekile, noma yakha futhi usingathe esakho isitaki. Ngayinye inamajika ezindleko ahluke kakhulu nezindlela zokwehluleka ezihluke kakhulu.

Isithombe sidalwe nge-DALL-E.

Inhlolovo ka-Omdia yango-2025 yababambe iqhaza kwezobuchwepheshe nezamabhizinisi abangama-376 ithole ukuthi 95% isakhiwo okuvunyelwene ngaso sinikeza ngokwezifiso nokulawula okwengeziwe

Inhlolovo efanayo ithole umkhumbi wezingxenyekazi ezakhiwe ngaphambilini ezingama-91%. Ngokushesha. Zombili izinombolo ziyiqiniso ngesikhathi esisodwa, okuyinkinga.

Lapho ethola khona ukhonkolo kulapho isikali. Ngaphansi kwezicelo zansuku zonke ezingu-100k, ukubiza i-API efana ne-GPT-4o Mini ngokuvamile kuwucingo olulungile. Ikhanda eliphansi. Ukuphindaphinda okusheshayo. Ngaphezulu kwezicelo zansuku zonke ezingu-1M, izindleko zethokheni ngayinye ziqala ukudla imajini [2].

Nansi ingxenye yamaqembu angabalulekile. Ukuhlaziywa kuka-2024 kwathola ukuthi ihadiwe nogesi kwenza kuphela ama-20 kuye kwangama-30% wezindleko zokuzibamba. Abasebenzi abanye 70 kuya ku-80% [2]. Lokhu kusho ukuthi amaspredishithi amaningi e-build-vs-buy ama-GPU futhi akhohlwe onjiniyela.

Olunye ucwaningo lwathola ukuthi amaqembu eqe izindleko zawo ze-LLM isabelomali nge 340% ngokwesilinganiso. Ezimweni eziningi imbangela ibishoda ekulandeleleni ukusetshenziswa komqashi ngamunye kanye nesichasiso sezindleko zeleveli yombuzo, hhayi inani lethokheni ngokwalo. [3].

Amaqembu awakwazanga ukubona ukuthi yisiphi isici noma ukwaziswa okushisa ibhajethi, ngakho awakwazanga ukukulungisa.

Ukukhiya kwe-Framework kubonakala kamuva futhi kubonakala kanzima. I-Hugging Face's Text Generation Inference yangena kumodi yokulungisa ngasekupheleni kuka-2025, futhi amaqembu akhe phezu kwayo kwadingeka ukuthi athuthe. Amaqembu asebenzise i-API bekungafanele enze lutho.

Uhlaka olusebenzayo engilusebenzisayo:

Qala nge-API.
Sebenzisa yonke ikholi ngezindleko, ukubambezeleka, nesici sesichasiso kusukela ngosuku loku-1.
Shintsha lapho izibalo zikuphoqa ukuthi wenze kanjalo.

2. Imodeli Eyinkimbinkimbi vs. Ukugcinwa

Ubani olungisa lokhu ezinyangeni eziyisi-6?

Iphepha elidumile le-Google lethule i I-CACE isimiso: Ukushintsha Noma yini Kushintsha Konke [4].

Ku ML amasistimu, i-tweak encane engxenyeni eyodwa yepayipi ingabangela izinguquko ezimangalisayo kwenye indawo. Lokhu akuvamile ukwenzeka ngokuhlehla komugqa. Kwenzeka kaningi ngama-ensembles kanye namanetha e-neural.

Ucwaningo ngesikweletu sobuchwepheshe be-ML lubonisa leyo datha ukuncika kubiza kakhulu kunokuncika ngekhodi [4].

Kungani? Ngoba idatha ilandeleka kakhudlwana, kunzima ukuyihumusha, futhi kunzima ukuyichaza kunoma ubani ozuza isistimu ezinyangeni ezingu-6 kusukela manje.

Iphepha langempela lilinganisela ukuthi ikhodi yemodeli yangempela iyingxenyana encane yohlelo lwe-ML lomhlaba wangempela. I inqwaba izitolo zesici, ukuqonda kwepayipi, ukuqapha, izibangeli zokuqeqesha kabusha, kanye neglu phakathi kwakho konke [5].

Ekuqeqesheni, amaqembu akhetha okwengeziwe inkimbinkimbi imodeli yenzuzo yokunemba engu-2% futhi ukhokhele lokho kukhetha izinyanga ezingu-18 ngesikhathi sokulungisa iphutha, ukuqeqesha kabusha okungaphezulu, futhi intela “akekho okhumbulayo ukuthi kungani senze lokhu”.

Umbuzo okufanele uwubuze ngaphambili ukuthumela imodeli eyinkimbinkimbi ukuthi: ubani ongumnikazi walokhu ngonyaka? Uma impendulo eqotho “ingacacile,” lelo iphuzu lesinqumo.

Funda indlela yokunikeza intandokazi yakho I-AI okungenamkhawulo kubuyekeziwe umongo: Nikeza I-AI Yakho Enganqunyelwe Ubuyekeziwe | Mayelana neSayensi Yedatha

3. Ubuningi Bedatha vs. Ikhwalithi Yedatha

Idatha eyengeziwe ayihlali iyimpendulo

Okuningi idatha iwina amamodeli ayisisekelo aqeqeshwe ku-inthanethi-scale corpora. Ku-ML esetshenzisiwe, ubudlelwano buphuka ngokushesha.

Ucwaningo lukhombisa ukuthi ngale kwe-a umsindo umkhawulo, ukwengeza idatha yekhwalithi ephansi kwenza isicaba noma kwehlisa izinga lokusebenza kwemodeli [6].

Lokhu kusho ukuthi ubudlelwano phakathi kosayizi wesampula nokunemba buyawohloka lapho umsindo weqa izinga elithile!

Inkinga “yexhaphozi ledatha” ukuthi lokhu kubukeka kanjani ezinkampanini. Amaqembu qoqa yonke into ngoba isitoreji sishibhile futhi bacabanga ukuthi kuyoba usizo ngolunye usuku.

Ngaphandle ukubusauthola ichibi elithatha amasonto ukuhlanza, likhuphule isitoreji nezindleko zamapayipi, futhi libambezele ukuhlolwa ngaphandle kokuthuthukisa imiphumela [7].

I-Medical AI yicala elicacile. Encane amasethi edatha anamalebula aqinisekiswe uchwepheshe asebenze ngokuphindaphindiwe ukusebenza kahle kunamasethi edatha amakhulu anezichasiselo ezingathembekile. Imodeli ifunde amaphethini alungile kudatha encane ngoba isignali ibihlanzekile.

Umbuzo engiwuthola uwusizo kakhulu ekusebenzeni:

ukuthi kunomsindo kangakanani esinakho, futhi lisithengelani ihora eli-1 ngaphezulu uma kuqhathaniswa nolunye usuku lokuqoqa?

4. I-Inputput vs. Ukubambezeleka: Iqoqo noma Isikhathi Sangempela

Iqoqo noma ngesikhathi sangempela

Iqoqo kanye nencazelo yesikhathi sangempela yizakhiwo zesistimu ezi-2 ezihlukene. Ukukhetha okungalungile kungena kwingqalasizinda, izindleko, nezinketho zokuhlangenwe nakho komsebenzisi okunzima ukuzihlehlisa kamuva.

Ukuchazwa kweqoqo: izibikezelo ezenziwe ngeshejuli (ngehora, nsuku zonke), ezigcinwe kusizindalwazi, ezinikezwa kusukela lapho. Izindleko eziphansi. Ingqalasizinda elula futhi kulula ukuyilungisa. Izibikezelo zingadala.

Incazelo yesikhathi sangempela: izibikezelo ngokufunwa, ngama-millisecond ukuya kumasekhondi. Ihlala imanje futhi ibiza kakhulu (isikhathi sokuphumula esingu-24/7). Izingxenye ezihambayo ezengeziwe futhi kunzima ukuziqapha [8].

I ukungezwani ezingeni lesistimu yiqiniso lokuthi osayizi benqwaba abakhulu banikeza ukudlula okuphezulu kodwa ukubambezeleka okuphezulu ngesicelo ngasinye. Amasistimu esikhathi sangempela asebenzisa usayizi we-batch 1, onikeza isivinini kodwa angalahlekelwa ukusebenza kahle.

I iphutha Ngibona iningi amaqembu enza ngokuzenzakalelayo ngesikhathi sangempela ngoba kuzwakala kakhulu umxhwele.

Kodwa izinkinga eziningi zebhizinisi azidingi izibikezelo ze-sub-second!

Izikolo ze-churn zasebusuku, izincomo zeviki ziyavuselelwa, izibuyekezo zansuku zonke zemodeli yokukhwabanisa. Lezi yizinkinga zeqoqo ezenziwa ngokwedlulele njengezesikhathi sangempela, futhi umehluko wezindleko esikalini ubalulekile.

Isignali engokoqobo: uma abasebenzisi bakho bengeke baqaphele ukuthi ukubikezela kuyimizuzu emi-5 ubudala noma ama-millisecond angu-5 ubudala, sebenzisa inkomba yenqwaba esikhundleni sesikhathi sangempela.

5. I-Prompt Engineering vs. Ukushuna Kahle

Amajika amabili okutshala ahluke kakhulu

Umqondo wesinqumo lapha uhlanzeke kakhulu kulezi zinyanga ezedlule.

Ubunjiniyela obusheshayo kuyashesha, kushibhile, futhi kuyavumelana nezimo. Kungathatha amahora kuya ezinsukwini ukuphindaphinda futhi kusebenza kahle emisebenzini eminingi, ikakhulukazi ngamamodeli wemingcele anekhono.

Okubi ukuntekenteke ngoba izinguquko ezincane zokufakwayo zikhiqiza okukhiphayo okungahambisani, futhi ukwaziswa okude okunemithetho yokufometha eyinkimbinkimbi kuvame ukuphuka ngaphansi kwamakesi asemaphethelweni.

Ukuhleleka Kwayo kuyabiza ngaphambi kwesikhathi ekubalweni kwekhompiyutha, ukulungiswa kwedatha, kanye nesikhathi sobunjiniyela. Ithembekile futhi iyaguquguquka esikalini uma umsebenzi usuphelile.

Isibonelo sangempela engisibonile sicashuniwe: ukulungisa kahle i-GPT-4o yengxoxo yokusekelwa kwamakhasimende kudle cishe u-$10k ngekhompyutha kanye namaviki angu-6 okulungiselela idatha. [9]. Enye indlela ye-RAG ithunyelwe emavikini angu-2.

Umbono wami ngesiqondiso sikadokotela samanje: qala izixwayiso.

Khuphukela ku ukuhleleka Kwayo kuphela uma ushaya izindlela zokwehluleka ukwazisa okungakwazi ukulungisa. Ngaphansi kwemibuzo engu-100k, ukwaziswa cishe njalo kuwucingo olulungile. Kuye kwaboniswa ukuthi ukulungisa kahle kuyazuzisa ivolumu ephezulu lapho umsebenzi uzinzile futhi uchazwe kahle [10].

Ukuhlaziywa kuka-2025 kuthole lokho ukwenza kahle ngokushesha ngamathuluzi afana ne-DSPy yehlula ukulungisa kahle ngamaphoyinti angu-6 ukuya kwangu-19 kwamanye amabhentshimakhi, kusetshenziswa ukukhishwa okumbalwa okungu-35x [10].

Kubonakala sengathi igebe ukuvala unyaka nonyaka. Ukucushwa kahle sekuyisinyathelo sokugcina kuzitaki eziningi engizibonayo, ezisetshenziswe ngemuva kokuthi ukwaziswa kushaye ophahleni lwazo ngokusobala.

Iphethini eyingxube ivama kakhulu ekukhiqizeni: imodeli ecushwe kahle kwisitayela sesizinda nethoni, ehlanganiswe ne-RAG ukuze kusekelwe iqiniso. Lezi zindlela ezimbili zixazulula izinkinga ezahlukene.

6. Okuzenzakalelayo vs. Ukubheka Kwabantu

Umethemba kangakanani imodeli ukuthi izosebenza yedwa?

Umbuzo owusizo ekukhiqizeni uwukuthi: yini i- izindleko ngesinqumo esingalungile, futhi I-WHO uyalimunca?

I-Human-in-the-loop (HITL) ihlezi ku-spectrum.

Ngakolunye uhlangothi, abantu babuyekeza konke okukhiphayo kwe-AI ngaphambi kokuthi kusebenze. Ngakolunye uhlangothi, igcwele okuzenzakalelayo abantu bebheke okudidayo kuphela.

Amasistimu amaningi okukhiqiza ahlala ndawana thize phakathi, edlulisa izibikezelo zokuzithemba okuphansi kubantu futhi avumele abanokuzithemba okukhulu ukuthi badlule. [11].

Kodwa izindleko zokusebenza ze-HITL ziyiqiniso: ukubukeza zonke izinqumo zemodeli akulingani!

Iqiniso liwukuthi ukungenelela komuntu kwesikhathi sangempela kunciphisa isistimu futhi ukungahambisani kombuyekezi kwehlisa izinga lelebula.

Uhlelo lokusebenza ukukhetha I-HITL: ukubuyekezwa komuntu kucushwe kuphela umphetho amacala, imiphumela yokungazethembi, kanye nezinqumo ezisezingeni eliphezulu.

Kwezokunakekelwa kwempilo, ezezimali, kanye nezomthetho, i-HITL ivamise ukuba yisidingo sokuthobela. Isazi se-radiologist esibuyekeza izimila ezihlatshwe umkhosi nge-AI noma ummeli obuyekeza izigaba zenkontileka ezihlatshwe umkhosi nge-AI. Lezi yizimo lapho izindleko zephutha ziphezulu kakhulu ukuthi zingazenzakalela ngokugcwele.

Indlela yokucabanga ngokuhlukana:

I-AI iphatha ivolumu, isivinini, nokubonwa kwephethini.
Abantu baphatha ukungahlehliseki.

I umklamo umbuzo uwukuthi lapho ngempela lo mugqa uhlala endaweni ethile yakho ukuhamba komsebenzikanye nokuthi ingabe abantu abakuluphu banegunya elicacile lokweqa imodeli lapho bengavumelani.

Okufanele Uthathe

Uma bekumele ngicindezele ama-trade-offs angu-6 isimiso esisodwakungaba lokhu: ekukhiqizeni, izindleko zesinqumo azivamile ukukhokhwa lapho isinqumo senziwe.

Imodeli eyinkimbinkimbi kakhulu ibiza wena ekuyinakekeleni ngemva kwezinyanga eziyisi-6. Isistimu yesikhathi sangempela ikubiza nge-infra engu-24/7 unaphakade.

Idatha engcolile esikalini ikubiza emijikelezweni yokuqeqesha kabusha. Ukwaziswa okuhlakaniphile kubiza ukuthi ube ntekenteke ngaphansi kwezimo ezibucayi. Futhi i-automation egcwele ikubiza uma okuthile okungenakuhlehliswa kungahambi kahle!

Ingxenye enzima ukwazi ukuthi izindleko zifika kuphi, nokubuza umbuzo ofanele kusenesikhathi ukuze wenze okuthile ngakho.

Siyabonga ngokufunda!