Iyini i-Generative AI?

I-Generative AI ibhekisela kumamodeli adala okuqukethwe okusha - umbhalo, izithombe, umsindo, ividiyo, ikhodi, izakhiwo zedatha - ngokusekelwe emaphethini afundiwe kumadathasethi amakhulu. Esikhundleni sokulebula nje noma ukubeka izinto ngokwezinga, lezi zinhlelo zikhiqiza okuphumayo kwamanoveli okufana nalokhu akubonile, ngaphandle kokuba amakhophi angempela. Cabanga: bhala isigaba, nikeza ilogo, uhlaka lwe-SQL, qamba umculo. Yilowo mqondo owumongo. [1]

Izindatshana ongathanda ukuzifunda ngemva kwalesi:

🔗 Iyini i-AI ye-agent echazwe
Thola ukuthi i-AI ye-agent ihlela, yenza, futhi ifunde kanjani ngokuhamba kwesikhathi.

🔗 Iyini i-AI scalability ekusebenzeni namuhla
Funda ukuthi kungani amasistimu e-AI angalawuleki ebalulekile ekukhuleni nasekuthembekeni.

🔗 Luyini uhlaka lwesoftware ye-AI
Qonda izinhlaka ze-AI ezisebenziseka kabusha ezithuthukisa isivinini futhi zithuthukise ukungaguquguquki.

🔗 Ukufunda ngomshini vs AI: umehluko omkhulu uchaziwe
Qhathanisa i-AI nemiqondo yokufunda komshini, amakhono, nokusetshenziswa komhlaba wangempela.

Kungani abantu belokhu bebuza ukuthi “Iyini i-Generative AI?” noma kunjalo 🙃

Ngoba kuzwakala njengomlingo. Uthayipha ukwaziswa, bese kuphuma okuthile okuwusizo - ngezinye izikhathi okukhazimulayo, ngezinye izikhathi okungajwayelekile. Kungokokuqala ngqa ukuthi isoftware ibonakala iyingxoxo futhi inobuhlakani esikalini. Futhi, igqagqana nosesho, abasizi, izibalo, idizayini, namathuluzi e-dev, afiphalisa izigaba futhi, ngokweqiniso, axabanise amabhajethi.

Yini eyenza i-Generative AI ibe wusizo ✅

• Isivinini sokwenza okusalungiswa - sikutholela ukudlula okuhloniphekile ngokushesha ngendlela engenangqondo.

• Ukuhlanganiswa kwephethini - kuhlanganisa imibono kuyo yonke imithombo ongase ungayixhumi ngoMsombuluko ekuseni.

• I-interfaces eguquguqukayo - ingxoxo, izwi, izithombe, izingcingo ze-API, ama-plugin; khetha indlela yakho.

• Ukwenza ngendlela oyifisayo - kusukela kumaphethini wokwaziswa angasindi ukuya ekulungiseni kahle idatha yakho siqu.

• Ukugeleza komsebenzi okuhlanganisiwe - uchungechunge lwezinyathelo zemisebenzi yezigaba eziningi njengocwaningo → uhlaka → okusalungiswa → QA.

• Ukusetshenziswa kwamathuluzi - amamodeli amaningi angabiza amathuluzi angaphandle noma imininingwane yolwazi ngokuthi amaphakathi nengxoxo, ukuze angaqageli nje.

• Amasu okuqondanisa - izindlela ezifana nokusiza amamodeli e-RLHF aziphathe ngendlela ewusizo futhi ephephile ekusetshenzisweni kwansuku zonke. [2]

Masikhulume iqiniso: akukho kulokhu okwenza kube yibhola lekristalu. Kufana nomfundi ofundela umsebenzi onekhono ongalali futhi ngezinye izikhathi abone ngamehlo engqondo i-bibliography.

Inguqulo emfushane yokuthi isebenza kanjani 🧩

Amamodeli ombhalo aziwa kakhulu asebenzisa ama-transformer - i-neural network architecture ehamba phambili ekuboneni ubudlelwano phakathi kwamalandelanayo, ngakho-ke ingabikezela ithokheni elandelayo ngendlela ezwakala ihambisana. Ezithombeni nakuvidiyo, amamodeli okusabalalisa ajwayelekile - afunda ukuqala emsindweni futhi aphinde awususe ukuze aveze isithombe esibambekayo noma isiqeshana. Lokho ukwenza lula, kodwa kuyasiza. [3][4]

• Ama-Transformers : anekhono olimini, amaphethini okucabanga, kanye nemisebenzi enezimo eziningi lapho eqeqeshwa ngaleyo ndlela. [3]

• Ukusabalalisa : kuqinile ezithombeni ze-photorealistic, izitayela ezingashintshi, nokuhlelwa okulawulwayo kusetshenziswa ukwaziswa noma imaski. [4]

Kukhona nama-hybrids, ukusethwa kwe-retrieval-augmented, nezakhiwo ezikhethekile - isitshulu sisabila.

Ithebula lokuqhathanisa: izinketho ezidumile zokukhiqiza ze-AI 🗂️

Ayiphelele ngenhloso - amanye amaseli ayindida ukuze abonise amanothi omthengi womhlaba wangempela. Izintengo ziyahamba, ngakho phatha lezi njengezitayela zamanani , hhayi izinombolo ezigxilile.

Inothi elincane: abathengisi abahlukene bashicilela amasistimu ahlukene okuphepha, imikhawulo yezilinganiso, nezinqubomgomo. Hlala ubheka amadokhumenti abo - ikakhulukazi uma uthumela kumakhasimende.

Ngaphansi kwe-hood: ama-transformer emoyeni owodwa 🌀

Ama-transformer asebenzisa zokunaka ukukala ukuthi yiziphi izingxenye zokokufaka okubaluleke kakhulu esinyathelweni ngasinye. Esikhundleni sokufunda kwesokunxele kuye kwesokudla njengenhlanzi yegolide enethoshi, babheka kulo lonke ukulandelana ngokuhambisana futhi bafunde amaphethini afana nezihloko, amabhizinisi, ne-syntax. Lokho kufana - kanye nokubala okuningi - kusiza amamodeli ukukala. Uma uzwile ngamathokheni namafasitela womongo, yilapho ehlala khona. [3]

Ngaphansi kwesivalo: ukusakazeka emphefumulweni owodwa 🎨

Amamodeli okusabalalisa afunda amaqhinga amabili: engeza umsindo ezithombeni zokuqeqesha, bese uhlehlisa umsindo ngezinyathelo ezincane ukuze ubuyisele izithombe ezingokoqobo. Ngesikhathi sokukhiqiza baqala emsindweni omsulwa futhi bawubuyisele emuva esithombeni esihambisanayo besebenzisa inqubo yokufunda yokukhipha umsindo. Kufana nokuqopha usuka ku-static - akusona isingathekiso esiphelele, kodwa uyasithola. [4]

Ukuqondanisa, ukuphepha, kanye nokuthi “sicela ungahambi kahle” 🛡️

Kungani amanye amamodeli ezingxoxo enqaba izicelo ezithile noma abuze imibuzo ecacisayo? Isiqephu esikhulu sithi Ukuqiniswa Ukufunda Kusuka Kumpendulo Yomuntu (RLHF) : abantu balinganisela imiphumela yesampula, imodeli yomvuzo ifunda lokho okuthandwayo, futhi imodeli eyisisekelo iyagudluzwa ukuze yenze izinto eziwusizo. Akukhona ukulawula ingqondo - ukuqondisa kokuziphatha okunokwahlulela komuntu ku-loop. [2]

Ngobungozi benhlangano, izinhlaka ezifana ne- NIST AI Risk Management Framework - kanye ne -Generative AI Profile - zinikeza isiqondiso sokuhlola ukuphepha, ukuphepha, ukubusa, ukusebenza, nokuqapha. Uma ukhipha lokhu emsebenzini, le mibhalo iwuhlu lokuhlola olusebenzayo ngokumangalisayo, hhayi nje ithiyori. [5]

I-anecdote esheshayo: Esifundweni sokuhlola, ithimba labasekeli lifinyeze ngamaketanga → likhiphe izinkambu eziyinhloko → impendulo esalungiswa → isibuyekezo sabantu . Iketanga alizange libasuse abantu; kwenza izinqumo zabo zisheshe futhi zingaguquguquki kuwo wonke amashifu.

Lapho i-Generative AI ikhanya khona lapho ikhubeka khona 🌤️↔️⛈️

Ikhanya kokuthi:

• Okusalungiswa kokuqala kokuqukethwe, amadokhumenti, ama-imeyili, imininingwane, amaslayidi

• Izifinyezo zezinto ezinde ongathanda ukungazifundi

• Usizo lwekhodi kanye nokwehliswa kwe-boilerplate

• Amagama okucabanga, izakhiwo, amacala okuhlola, ukwaziswa

• Imiqondo yezithombe, okubonwayo komphakathi, okokuhlekisa komkhiqizo

• Ukungqubuzana kwedatha okulula noma isikafula se-SQL

Ukhubeka kokuthi:

• Ukunemba okuyiqiniso ngaphandle kokubuyisa noma amathuluzi

• Izibalo ezinezinyathelo eziningi uma zingaqinisekisiwe ngokusobala

• Izithiyo zesizinda ezicashile emthethweni, imithi, noma ezezimali

• Amacala asemaphethelweni, ukubhuqa, nolwazi olunomsila omude

• Ukuphathwa kwedatha eyimfihlo uma ungayilungisi kahle

Abaqaphi bayasiza, kodwa isinyathelo esifanele ukuklama kwesistimu : engeza ukubuyisa, ukuqinisekiswa, ukubuyekezwa komuntu, nemikhondo yokuhlola. Kuyisicefe, yebo - kodwa isicefe sizinzile.

Izindlela ezisebenzayo zokuyisebenzisa namuhla 🛠️

• Bhala kangcono, ngokushesha : uhlaka → nweba → cindezela → pholisha. Loop kuze kuzwakale sengathi nguwe.

• Cwaninga ngaphandle kwezimbobo zonogwaja : cela incwajana ehlelekile enemithombo, bese ujahe izinkomba ozikhathalelayo ngempela.

• Usizo lwekhodi : chaza umsebenzi, phakamisa izivivinyo, dweba uhlelo lokuphinda umshini; ungalokothi unamathisele izimfihlo.

• Imisebenzi yedatha : khiqiza amathambo e-SQL, i-regex, noma imibhalo yezinga lekholomu.

• Umbono wedizayini : hlola izitayela ezibonakalayo, bese unikeza umklami ukuze aqedele.

• I-ops yekhasimende : izimpendulo ezisalungiswa, izinhloso zokunquma, fingqa izingxoxo ze-handoff.

• Umkhiqizo : dala izindaba zabasebenzisi, indlela yokwamukela, futhi ukopishe okuhlukile - bese u-A/B uhlola ithoni.

Ithiphu: gcina imiyalo esebenza kahle njengezifanekiso. Uma isebenza kanye, cishe izophinde isebenze ngama-tweaks amancane.

Ukujula ngokujulile: ukwazisa okusebenzayo 🧪

• Nikeza isakhiwo : izindima, izinhloso, izithiyo, isitayela. Amamodeli athanda uhlu lokuhlola.

• Izibonelo ezimbalwa : faka izibonelo ezinhle ezi-2–3 zokufaka → okuphumayo okuhle.

• Cabanga ngendlela efanele : cela imibono noma imiphumela ehleliwe lapho ubunkimbinkimbi bukhuphuka.

• Phina izwi : namathisela isampuli emfushane yethoni oyikhethayo bese uthi "lingisa lesi sitayela."

• Setha ukuhlola : cela imodeli ukuthi ihlaziye impendulo yayo ngokuqhathanisa nemibandela, bese ubuyekeza.

• Sebenzisa amathuluzi : ukubuyisa, ukusesha iwebhu, izibali, noma ama-API kunganciphisa ukubona izinto ezingekho ngokuningi. [2]

Uma ukhumbula into eyodwa kuphela: yitshele ukuthi yini okumele uyizibe . Izithiyo zingamandla.

Idatha, ubumfihlo, kanye nokuphatha - izingcezu ezingathandeki 🔒

• Izindlela zedatha : cacisa ukuthi yini efakiwe, egciniwe, noma esetshenziselwa ukuqeqeshwa.

• I-PII nezimfihlo : kugcine kungangeni ekwazisweni ngaphandle uma ukusethwa kwakho kukuvumela ngokusobala futhi kukuvikele.

• Izilawuli zokufinyelela : phatha amamodeli njengezizindalwazi zokukhiqiza, hhayi amathoyizi.

• Ukuhlola : ikhwalithi yokulandelela, ukuchema, nokukhukhuleka; kala ngemisebenzi yangempela, hhayi amavayibhu.

• Ukuqondanisa inqubomgomo : izici zemephu ezigabeni ze-NIST AI RMF ukuze ungamangali kamuva. [5]

I-FAQ engiyithola ngaso sonke isikhathi 🙋♀️

Ingabe iyadala noma ivele ixube kabusha?
Ndawana thize phakathi. Ihlanganisa kabusha amaphethini ngezindlela ezintsha - hhayi ubuciko bomuntu, kodwa ngokuvamile iwusizo.

Ngingawethemba amaqiniso?
Themba kodwa uqinisekise. Engeza ukubuyisa noma ukusetshenziswa kwethuluzi kunoma yini ephezulu. [2]

Amamodeli ezithombe akuthola kanjani ukuvumelana kwesitayela?
Ubunjiniyela obusheshayo kanye namasu afana nokulungisa isithombe, ama-adaptha e-LoRA, noma ukulungisa kahle. Izisekelo zokusabalalisa zisiza ngokuvumelana, nakuba ukunemba kombhalo ezithombeni kusengantengantenga. [4]

Kungani amamodeli ezingxoxo “ehlehla” emiyalweni eyingozi?
Amasu okuqondanisa afana ne-RLHF nezendlalelo zenqubomgomo. Ayiphelele, kodwa iwusizo ngendlela ehlelekile. [2]

Umngcele osafufusa 🔭

• Yonke into enezimo eziningi : ama-combos angenamthungo ombhalo, isithombe, umsindo, nevidiyo.

• Amamodeli amancane, asheshayo : izakhiwo ezisebenzayo zamakesi akudivayisi nasemaphethelweni.

• Amaluphu wamathuluzi aqinile : imisebenzi ebiza ama-agent, isizindalwazi, nezinhlelo zokusebenza sengathi akulutho.

• Isiqalo esingcono : i-watermarking, imininingwane yokuqukethwe, namapayipi alandelekayo.

• Ukuphatha kufakwe ku- : amasudi okuhlola kanye nezendlalelo zokulawula ezizwakala njengokusetshenziswa kwe-dev okuvamile. [5]

• Amamodeli ashunwe isizinda : ukusebenza okukhethekile kwehlula ukuqeqebana okuvamile kwemisebenzi eminingi.

Uma kuzwakala sengathi isoftware isiba ngumhlanganyeli - yilokho iphuzu.

Isikhathi eside Kakhulu, Angizange Ngiyifunde - Iyini I-Generative AI? 🧾

Umndeni wamamodeli akhiqiza okuqukethwe okusha kunokwahlulela okuqukethwe okukhona kuphela. Amasistimu ombhalo ngokuvamile ama-transformer abikezela amathokheni; amasistimu amaningi ezithombe namavidiyo okusabalalisa asho ukungahleliwe kube into ehlangene. Uthola isivinini namandla okudala, ngezindleko zombhedo wokuzethemba ngezikhathi ezithile - ongakwazi ukuwuthambisa ngokuwuthola, amathuluzi, namasu okuqondanisa afana ne -RLHF . Kumaqembu, landela imihlahlandlela esebenzayo efana ne- NIST AI RMF ukuze uthumele ngokuzibophezela ngaphandle kokugaya kuze kumiswe. [3][4][2][5]

Izithenjwa

1. I-IBM - Iyini i-Generative AI?
   Funda kabanzi

2. I-OpenAI - Ukuqondanisa amamodeli olimi ukuze ulandele imiyalelo (RLHF)
   funda kabanzi

3. Ibhulogi ye-NVIDIA - Iyini Imodeli Yokuguqula?
   Funda kabanzi

4. Ubuso Obugonayo - Amamodeli Okusabalalisa (Iyunithi Yesifundo 1)
   funda kabanzi

5. I-NIST - I-AI Risk Management Framework (kanye ne-Generative AI Profile)
   funda kabanzi

Thola i-AI yakamuva esitolo esisemthethweni somsizi we-AI

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