Uhlolojikelele
Ukufunda ngaphakathi kokuqukethwe yikhono elimangalisayo lamamodeli amakhulu olimi lokuthatha umsebenzi omusha ezibonelweni ezimbalwa ezibekwe ekwazisweni, ngaphandle kokuqeqeshwa kabusha. Kuyisizathu sokuthi 'ufundise' imodeli empukaneni ngokuyibonisa nje lokho okufunayo.
I-In-Context Learning iyingxenye yesitaki solimi-AI esetshenziselwa ukufunda, ukukhiqiza, ukuhlukanisa, nokuguqula umbhalo nenkulumo ngezikali.
I-Deep Dive
Imvamisa, ukufundisa inethiwekhi ye-neural umsebenzi omusha kusho ukubuyekeza izisindo zayo ngokuqeqeshwa. Ukufunda okungaphakathi kokuqukethwe kuhlukile: ubhala izibonelo ezimbalwa ngqo ekwazisweni ('umongo'), futhi imodeli ithatha iphethini bese iyisebenzisa kokokufaka okusha. Akukho okushintshile ngaphakathi kwemodeli; izibonelo ziqondisa nje isibikezelo sethokheni elandelayo. Uzozwa 'i-zero-shot' (imiyalo kuphela), 'ishothi eyodwa' (isibonelo esisodwa), kanye 'nokudubula okumbalwa' (izibonelo ezimbalwa). Lokhu kuziphatha kwaduma yi-GPT-3 ngo-2020 futhi kwaba yikhono elivelayo: amamodeli amancane awakwazi ukukwenza, kodwa esikhathini esidlule cishe esikalini sepharamitha eyizigidi eziyizinkulungwane eziyi-100, ukunemba kokwaziswa okumbalwa kukhuphuka kakhulu. Imodeli ifunde ngempumelelo ukubona nokuqhubeka namaphethini phakathi nokuqeqeshwa kwangaphambili, ngakho-ke ingaphinda isebenzise lelo khono ngesikhathi sokunquma.
I-Technical Insight
Ucwaningo lokutolika lulandele ingxenye enkulu yaleli khono 'kumakhanda wokungeniswa' - amasekhethi okunaka avela ngesikhathi sokuqeqeshwa futhi enze ukufanisa isiqalo esingaqondakali: aphinde ahlole lapho kuvela khona ithokheni efanayo, bese ukopisha okulandelayo. Ngakho-ke uma umyalo wakho ubonisa 'i-apula -> isithelo, isanqante -> imifino', imodeli ifana nesakhiwo futhi ibikezela ilebula elilungile lento elandelayo. Okubaluleke kakhulu, awekho ama-gradients agelezayo futhi azikho izisindo ezibuyekezwa lapho kucatshangwa khona. Izibonelo zivele zimise kabusha ukwenza kusebenze okuphakelayo ukusabalalisa kwamathuba wethokheni elandelayo.
Ukufunda Okungaphakathi Kokuqukethwe
Ukufunda ngaphakathi kokuqukethwe yikhono elimangalisayo lamamodeli amakhulu olimi lokuthatha umsebenzi omusha ezibonelweni ezimbalwa ezibekwe ekwazisweni, ngaphandle kokuqeqeshwa kabusha. Kuyisizathu sokuthi 'ufundise' imodeli empukaneni ngokuyibonisa nje lokho okufunayo. I-In-Context Learning iyingxenye yesitaki solimi-AI esetshenziselwa ukufunda, ukukhiqiza, ukuhlukanisa, nokuguqula umbhalo nenkulumo ngezikali. Ukuze wakhe ukuqonda okujulile, phatha i-In-Context Learning njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela efiselekayo, ucacise ukucabanga, futhi uhlukanise lokho isistimu engakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa imiyalo yedizayini ye-In-Context Learning, ukubuyisa, nokubuyekeza amaluphu njengohlelo olulodwa lokuxhumana oludidiyelwe. Babhala imibandela yempumelelo ecacile, ukuhlola okuqhathaniswa nedatha engokoqobo nokugeleza komsebenzi, futhi baphindaphinde ngokusekelwe kumaphethini okuhluleka aqashiwe esikhundleni sokuwina kwebhentshimakhi yesikhathi esisodwa. Yilapho ukuqonda kwethiyori kuguquka kube amandla ahlala njalo kuwo wonke umkhiqizo, inqubomgomo, kanye nokusebenza.
Ukugeleza komsebenzi wolimi kungahamba ngokushesha ngaphandle kokudela ukuvumelana. Ngesikhathi esifanayo, amaqiniso Akhohliwe angafaka imibiko buthule, ukugeleza kosekelo, noma imiphumela yocwaningo. Indlela eqine kakhulu iwukuhlanganisa isivinini sokuhlola nesiyalo sokuphatha: qhuba abashayeli bezindiza, bamba ubufakazi, ushicilele amalogi ezinqumo, futhi ubuyekeze izivikelo ngokuqhubekayo njengoba imodeli yokuziphatha, okulindelwe ngabasebenzisi, kanye nezimfuneko zokulawula zishintsha.
I-Strategic Impact
Ukugeleza komsebenzi wolimi kungahamba ngokushesha ngaphandle kokudela ukuvumelana.
Ukugeleza komsebenzi wolimi kungahamba ngokushesha ngaphandle kokudela ukuvumelana. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Yandisa ukufinyelela kuzo zonke izilimi nezitayela zokuxhumana.
Yandisa ukufinyelela kuzo zonke izilimi nezitayela zokuxhumana. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Amaqembu angachitha isikhathi esiningi ekwahluleleni kuyilapho i-automation isingatha impinda.
Amaqembu angachitha isikhathi esiningi ekwahluleleni kuyilapho i-automation isingatha impinda. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ukuqaliswa Komhlaba Wangempela
Ukunikeza i-chatbot izibonelo ezintathu zamathikithi okusekela nezigaba zawo, bese uwenza uhlukanise ithikithi elisha ngendlela efanayo
Ibonisa imodeli emibili ngaphambi/ngemuva kwamapheya ombhalo ongcolile ofomethiwe kabusha waba i-JSON ehlanzekile ukuze iguqule okunye
Ukunamathisela izincazelo ezimbalwa zesampula zomkhiqizo ngethoni yomkhiqizo wakho ukuze ezintsha zifane nesitayela
Ukubonisa inkinga yezibalo ezikhohlisayo kusetshenzwe isinyathelo ngesinyathelo ukuze imodeli ixazulule izinkinga ezifanayo ngefomethi efanayo yokucabanga.
Amaphethini Okusebenzisa
In-Context Learning in practice
Ukunikeza i-chatbot izibonelo ezintathu zamathikithi okusekela nezigaba zawo, bese uwenza uhlukanise ithikithi elisha ngendlela efanayo.
Ukunikeza izibonelo ezintathu zamathikithi asekela i-chatbot kanye nezigaba zawo, bese uwenza ahlukanise ithikithi elisha ngendlela efanayo Amaqembu avame ukuthola imiphumela engcono uma echaza ikhwalithi ephezulu ngaphambili, egcina indlela yokukhuphuka yabantu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
In-Context Learning in practice
Ibonisa imodeli emibili ngaphambi/ngemuva kwamapheya ombhalo ongcolile ofomethiwe kabusha waba i-JSON ehlanzekile ukuze iguqule okunye.
Ibonisa imodeli emibili ngaphambi/ngemuva kwamapheya ombhalo ongcolile ofomethiwe kabusha waba i-JSON ehlanzekile ukuze iguqule amanye Amathimba ngokuvamile athola imiphumela engcono lapho echaza ikhwalithi ephezulu ngaphambili, egcina indlela yokukhuphuka yomuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
In-Context Learning in practice
Ukunamathisela izincazelo ezimbalwa zesampula zomkhiqizo ngethoni yomkhiqizo wakho ukuze ezintsha zifane nesitayela.
Ukunamathisela izincazelo ezimbalwa zesampula zomkhiqizo ngethoni yomkhiqizo wakho ukuze ezintsha zifane nesitayela Amaqembu ngokuvamile athola imiphumela engcono uma echaza imikhawulo yekhwalithi ngaphambili, egcina indlela yokukhuphuka yabantu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
In-Context Learning in practice
Ukubonisa inkinga yezibalo ezikhohlisayo kusetshenziswe isinyathelo ngesinyathelo ukuze imodeli ixazulule izinkinga ezifanayo ngefomethi efanayo yokucabanga.
Ukubonisa inkinga yezibalo ekhohlisayo kusetshenziswe isinyathelo ngesinyathelo ukuze imodeli ixazulule izinkinga ezifanayo ngefomethi efanayo yokucabanga Amaqembu ngokuvamile athola imiphumela engcono lapho echaza imingcele yekhwalithi ngaphambili, egcina indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Izingozi & Guardrails
Amaqiniso akhonjiwe angafaka ngokuthula imibiko, ukugeleza kosekelo, noma imiphumela yocwaningo.
Ukuzwela okusheshayo kungadala imiphumela engahambisani kuzo zonke izicelo ezifanayo.
Idatha yombhalo ebucayi ingase idalulwe uma izilawuli zokufinyelela zibuthakathaka.
Ukuqalisa Umhlahlandlela
Chaza ifomethi yokuphumayo, ithoni, namazinga wekhwalithi ngaphambi kokukhishwa.
Chaza ifomethi yokuphumayo, ithoni, namazinga wekhwalithi ngaphambi kokukhishwa. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Izimpendulo eziyisisekelo ngemithombo ethembekile noma nini lapho ukunemba kubalulekile.
Izimpendulo eziyisisekelo ngemithombo ethembekile noma nini lapho ukunemba kubalulekile. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Gcina indawo yokuhlola isibuyekezo somuntu ukuze uthole imiphumela ephezulu.
Gcina indawo yokuhlola isibuyekezo somuntu ukuze uthole imiphumela ephezulu. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Landela amaphethini okuhluleka futhi uqeqeshe kabusha imiyalo noma ukuhamba komsebenzi njalo.
Landela amaphethini okuhluleka futhi uqeqeshe kabusha imiyalo noma ukuhamba komsebenzi njalo. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.