Isingeniso
Ukubikezela imakethe yamasheya sekuyisikhathi eside kube "i-grail engcwele" yezezimali efunwa yibo bobabili abatshalizimali bezikhungo nabadayisi emhlabeni jikelele. Ngokuthuthuka kwakamuva ku- Artificial Intelligence (AI) nokufunda komshini (ML) , abaningi bayazibuza ukuthi ingabe lobu buchwepheshe bugcine buyivula yini imfihlo yokubikezela amanani esitoko. Ingabe i-AI ingabikezela imakethe yamasheya? Leli phepha elimhlophe lihlola lowo mbuzo ngombono womhlaba wonke, lichaza ukuthi amamodeli aqhutshwa yi-AI azama kanjani ukubikezela ukunyakaza kwezimakethe, izisekelo zethiyori ezingemuva kwalawa mamodeli, kanye nemikhawulo yangempela abhekene nayo. Sethula ukuhlaziya okungachemile, okusekelwe ocwaningweni kune-hype, yalokho i-AI engakwazi noma engakwazi ukukwenza kumongo wokubikezela imakethe yezezimali.
Kuthiyori yezezimali, inselele yokubikezela igcizelelwa i- Efficient Market Hypothesis (EMH) . I-EMH (ikakhulukazi efomini layo “eliqinile”) ibeka ukuthi amanani esitoko abonisa ngokugcwele lonke ulwazi olutholakalayo nganoma isiphi isikhathi, okusho ukuthi akekho umtshali-zimali (ngisho nabangaphakathi) abangase baphumelele ngokuqhubekayo imakethe ngokuhweba ngolwazi olutholakalayo ( Amamodeli okubikezela isitoko aqhutshwa yidatha asekelwe kumanethiwekhi e-neural: Isibuyekezo ). Ngamagama alula, uma izimakethe zisebenza kahle kakhulu futhi izintengo zihamba ngokungahleliwe , ukubikezela ngokunembile izintengo zesikhathi esizayo kufanele cishe kungenzeki. Ngaphandle kwalo mbono, ukuheha kokunqoba imakethe kugqugquzele ucwaningo olunzulu ezindleleni zokuqagela ezithuthukile. I-AI nokufunda ngomshini sekube yingqikithi yalokhu kufunwa, sibonga ikhono lakho lokucubungula amanani amaningi edatha nokuhlonza amaphethini acashile abantu abangase bawageje ( Ukusebenzisa Ukufunda Ngomshini Ukubikezela Kwemakethe Yezitoko... | FMP ).
Leli phepha elimhlophe linikeza umbono obanzi wamasu e-AI asetshenziselwa ukubikezela izimakethe zamasheya futhi ahlole ukusebenza kwawo. Sizocubungula izisekelo zethiyori zamamodeli adumile (kusukela ezindleleni zochungechunge lwesikhathi esivamile kuya kumanethiwekhi ajulile e-neural nokufunda okuqiniswayo), sixoxe ngedatha nenqubo yokuqeqesha yalawa mamodeli, futhi sigqamise imikhawulo eyinhloko nezinselelo amasistimu anjalo abhekana nazo, njengokusebenza kahle kwemakethe, umsindo wedatha, nezenzakalo zangaphandle ezingalindelekile. Izifundo zomhlaba wangempela nezibonelo zifakiwe ukukhombisa imiphumela exubile etholwe kuze kube manje. Okokugcina, siphetha ngokulindela okungokoqobo kubatshalizimali nabasebenzi: sivuma amandla ahlaba umxhwele e-AI kuyilapho siqaphela ukuthi izimakethe zezimali zigcina izinga lokungaqiniseki okungekho i-algorithm engakwazi ukuliqeda ngokuphelele.
Izisekelo zethiyori ze-AI ku-Stock Market Prediction
Isibikezelo sesitoko esisekelwe ku-AI samanje sakhela phezu kwamashumi eminyaka ocwaningo lwezibalo, ezezimali, nesayensi yekhompyutha. Kuyasiza ukuqonda i-spectrum yezindlela kusuka kumamodeli wendabuko kuya ku-AI esezingeni eliphezulu:
-
Amamodeli Ochungechunge Lwesikhathi Esivamile: Ukubikezela kwesitoko sangaphambi kwesikhathi kuncike kumamodeli ezibalo acabanga ukuthi amaphethini ezintengo ezidlule angaveza ikusasa. Amamodeli afana ne -ARIMA (Auto-Regressive Integrated Integrated Moving Average) kanye ne -ARCH/GARCH agxile ekuthwebuleni amathrendi aqondile kanye nokuhlangana okuguquguqukayo kudatha yochungechunge lwesikhathi ( amamodeli okubikezela isitoko aqhutshwa yidatha asekelwe kumanethiwekhi emizwa: Isibuyekezo ). Lawa mamodeli ahlinzeka ngesisekelo sokubikezela ngokwenza imodeli yokulandelana kwamanani omlando ngaphansi kokuqagelwa kokuma kanye nomugqa. Nakuba ewusizo, amamodeli endabuko avame ukulwa namaphethini ayinkimbinkimbi, angenawo umugqa wezimakethe zangempela, okuholela ekubikezelweni okulinganiselwe kokusebenza ( amamodeli okubikezela amasheya aqhutshwa yidatha asekelwe kumanethiwekhi e-neural: Ukubuyekezwa ).
-
Ama-algorithms wokufunda ngomshini: Izindlela zokufunda ngomshini zidlula amafomula ezibalo achazwe ngaphambilini ngokufunda amaphethini ngokuqondile kudatha . Ama-algorithms afana nemishini ye-vector yokusekela (SVM) , amahlathi angahleliwe , nokukhuliswa kwe-gradient kusetshenziswe ekubikezelweni kwesitoko. Angahlanganisa izinhlobonhlobo zezici zokufaka - kusukela ezinkomba zobuchwepheshe (isb, ama-avareji anyakazayo, umthamo wokuhweba) kuya ezinkomba ezibalulekile (isb., umholo, idatha yomnotho omkhulu) - futhi bathole ubudlelwano obungaqondile phakathi kwazo. Isibonelo, imodeli yehlathi engahleliwe noma imodeli yokukhulisa i-gradient ingacabangela inqwaba yezinto ngesikhathi esisodwa, ithwebule ukusebenzisana imodeli yomugqa elula engase iphuthelwe. Lawa mamodeli e-ML abonise ikhono lokuthuthukisa ngesizotha ukunemba kokubikezela ngokuthola amasiginali ayinkimbinkimbi kudatha ( Ukusebenzisa Ukufunda Ngomshini Ukubikezela Kwemakethe Yezitoko... | FMP ). Nokho, zidinga ukushuna okucophelelayo kanye nedatha eyanele ukuze zigweme ukugcwala ngokweqile (umsindo wokufunda kunesiginali).
-
Ukufunda Okujulile (Amanethiwekhi We-Neural): Amanethiwekhi e-neural ajulile , aphefumulelwe ukwakheka kobuchopho bomuntu, adumile ekubikezelweni kwemakethe yamasheya eminyakeni yamuva. Phakathi kwalokhu, amanethiwekhi e-Recurrent Neural Networks (RNNs) kanye namanethiwekhi awo e- Long Short-Term Memory (LSTM) aklanyelwe ngokuqondile idatha yokulandelana njengochungechunge lwesikhathi sentengo yesitoko. Ama-LSTM angagcina inkumbulo yolwazi lwangaphambilini futhi athwebule ukuncika kwesikhashana, awenze afanelekele amamodeli amathrendi, imijikelezo, noma amanye amaphethini ancike esikhathini kudatha yemakethe. Ucwaningo lubonisa ukuthi ama-LSTM namanye amamodeli okufunda ajulile angathwebula ubudlelwano obuyinkimbinkimbi, obungeyona imigqa kudatha yezezimali egejwa amamodeli alula. Ezinye izindlela zokufunda ezijulile zihlanganisa ama-Convolutional Neural Networks (CNNs) (ngezinye izikhathi asetshenziswa “ezithombeni” zenkomba yezobuchwepheshe noma ukulandelana okufakwe ikhodi), Ama-Transformers (asebenzisa izindlela zokunaka ukuze alinganise ukubaluleka kwezinyathelo zesikhathi ezihlukene noma imithombo yedatha), kanye nama -Graph Neural Networks (GNNs) (ukuze afanekisele ubudlelwano phakathi kwamasheya kugrafu yemakethe). Lawa amanetha e-neural athuthukisiwe awakwazi ukufaka idatha yentengo kuphela kodwa neminye imithombo yedatha efana nombhalo wezindaba, imizwa yenkundla yezokuxhumana, nokuningi, ukufunda izici ezingabonakali ezingase zibikezele ukunyakaza kwemakethe ( Ukusebenzisa Ukufunda Ngomshini Ukubikezela Kwemakethe Yezitoko... | FMP ). Ukuvumelana nezimo zokufunda okujulile kuza nezindleko: zilambele idatha, zisebenza kakhulu ngokwekhompyutha, futhi zivame ukusebenza “njengamabhokisi amnyama” anokutolika okuncane.
-
Ukufunda Ukuqinisa: Omunye umngcele ekubikezelweni kwesitoko se-AI ukuqinisa ukufunda (RL) , lapho umgomo ungekona nje ukubikezela amanani, kodwa ukufunda isu elilungile lokuhweba. Ohlakeni lwe-RL, i -ejenti (imodeli ye-AI) isebenzisana nendawo (imakethe) ngokwenza izenzo (ukuthenga, ukuthengisa, ukubamba) nokuthola imiklomelo (inzuzo noma ukulahlekelwa). Ngokuhamba kwesikhathi, umenzeli ufunda inqubomgomo ekhulisa umvuzo oqongelelwayo. I-Deep Reinforcement Learning (DRL) ihlanganisa amanethiwekhi we-neural nokufunda okuqiniswayo ukuze isingathe indawo enkulu yezwe yezimakethe. Isikhalo se-RL kwezezimali yikhono layo lokucabangela ukulandelana kwezinqumo nokuthuthukisa ngokuqondile imbuyiselo ye-investimenti, kunokubikezela izintengo ngazodwa. Isibonelo, umenzeli we-RL angafunda ukuthi kufanele angene nini noma aphume nini ezikhundleni ngokusekelwe kumasiginali amanani futhi azivumelanise nezimo njengoba izimo zemakethe zishintsha. Ngokuphawulekayo, i-RL isetshenziselwe ukuqeqesha amamodeli e-AI ancintisanayo emiqhudelwaneni yokuhweba elinganiselwe nakwezinye izinhlelo zokuthengisa zobunikazi. Kodwa-ke, izindlela ze-RL nazo zibhekana nezinselele ezibalulekile: zidinga ukuqeqeshwa okubanzi (ukulingisa iminyaka yokuhweba), zingahlupheka ngenxa yokungazinzi noma ukuziphatha okuhlukile uma kungashuniwe ngokucophelela, futhi ukusebenza kwazo kuzwela kakhulu endaweni yemakethe ecatshangwayo. Abacwaningi baqaphele izinkinga ezifana nezindleko eziphezulu zekhompiyutha kanye nezinkinga zokuzinza ekusebenziseni ukufunda okuqinisiwe ezimakethe zamasheya eziyinkimbinkimbi. Naphezu kwalezi zinselele, i-RL imele indlela ethembisayo, ikakhulukazi uma ihlanganiswe namanye amasu (isb, ukusebenzisa amamodeli wokubikezela amanani kanye nesu lokwaba elisekelwe ku-RL) ukuze kwakheke uhlelo lokuthatha izinqumo oluxubile ( Ukubikezelwa Kwemakethe Yezitoko Ukusebenzisa Ukufunda Okujulile Kokuqinisa ).
Imithombo Yedatha kanye Nenqubo Yokuqeqesha
Ngaphandle kohlobo lwemodeli, idatha iwumgogodla wokubikezela kwemakethe yamasheya ye-AI. Amamodeli ngokuvamile aqeqeshwa kudatha yemakethe yomlando nakwamanye amadathasethi ahlobene ukuze athole amaphethini. Imithombo yedatha evamile nezici zifaka:
-
Amanani Omlando Nezinkomba Zobuchwepheshe: Cishe wonke amamodeli asebenzisa izintengo zesitoko ezedlule (ezivulekile, eziphezulu, eziphansi, ezivaliwe) kanye namavolumu okuhweba. Kulokhu, abahlaziyi bavame ukuthola izinkomba zobuchwepheshe (izilinganiso ezihambayo, inkomba yamandla ahlobene, i-MACD, njll.) njengokufakwayo. Lezi zinkomba zingasiza ukugqamisa amathrendi noma umfutho imodeli engase iwasebenzise. Isibonelo, imodeli ingase ithathe njengokufakiwe kwezinsuku eziyi-10 zokugcina zentengo nevolumu, kanye nezinkomba ezifana nesilinganiso sokuhamba sezinsuku ezingu-10 noma izilinganiso zokuguquguquka, ukubikezela ukunyakaza kwentengo kosuku olulandelayo.
-
Izinkomba Zezimakethe Nedatha Yezomnotho: Amamodeli amaningi afaka ulwazi lwemakethe olubanzi, olufana namazinga enkomba, amanani enzalo, ukwehla kwamandla emali, ukukhula kwe-GDP, noma ezinye izinkomba zomnotho. Lezi zici ezinkulu zinikeza umongo (isb, umuzwa wemakethe iyonke noma impilo yezomnotho) ezingaba nomthelela ekusebenzeni kwesitoko ngasinye.
-
Izindaba Nemininingwane Yomzwelo: Inani elikhulayo lezinhlelo ze-AI lingenisa idatha engahlelekile efana nezihloko zezindaba, okuphakelayo kwenkundla yezokuxhumana (i-Twitter, Stocktwits), nemibiko yezezimali. Amasu Okucubungula Ulimi Lwemvelo (NLP), okuhlanganisa amamodeli athuthukile njenge-BERT, asetshenziselwa ukukala imizwa yemakethe noma ukuthola imicimbi efanele. Isibonelo, uma imizwa yezindaba iphenduka ibe mubi kakhulu enkampanini noma emkhakheni, imodeli ye-AI ingase ibikezele ukwehla kwezintengo zesitoko ezihlobene. Ngokucubungula izindaba zesikhathi sangempela kanye nomuzwa wenkundla yezokuxhumana , i-AI ingasabela ngokushesha kunabadayisi abangabantu olwazini olusha.
-
Enye Idatha: Ezinye izikhwama ze-hedge eziyinkimbinkimbi kanye nabacwaningi be-AI basebenzisa eminye imithombo yedatha - izithombe zesathelayithi (zokuthola ithrafikhi yesitolo noma umsebenzi wezimboni), idatha yokwenziwe yekhadi lesikweletu, amathrendi osesho lwewebhu, njll. - ukuze bathole imininingwane eqagelayo. Lawa madathasethi okungewona awendabuko kwesinye isikhathi angasebenza njengezinkomba eziholayo ekusebenzeni kwesitoko, nakuba futhi ethula inkimbinkimbi ekuqeqesheni amamodeli.
Ukuqeqesha imodeli ye-AI yokubikezela isitoko kuhilela ukuyiphakela le datha yomlando kanye nokulungisa amapharamitha emodeli ukuze kuncishiswe iphutha lokubikezela. Ngokuvamile, idatha ihlukaniswa ibe isethi yokuqeqesha (isb, umlando omdala wokufunda amaphethini) kanye nesethi yokuhlola/yokuqinisekisa (idatha yakamuva kakhulu yokuhlola ukusebenza kwezimo ezingabonakali). Uma kubhekwa imvelo elandelanayo yedatha yemakethe, ukunakekelwa kuthathwa ukuze kugwenywe “ukubheka esikhathini esizayo” - isibonelo, amamodeli ayahlolwa kudatha kusukela ezikhathini zangemva kwesikhathi sokuqeqeshwa, ukuze kulingise indlela angenza ngayo ekuhwebeni kwangempela. okuqinisekisa okuphambanayo aguqulelwe ochungechungeni lwesikhathi (njengokuqinisekisa kokuya phambili) asetshenziselwa ukuqinisekisa ukuthi imodeli yenziwa kahle futhi ayifakwanga nje enkathini ethile ethile.
Ngaphezu kwalokho, ochwepheshe kufanele babhekane nezinkinga zekhwalithi yedatha nokucubungula kusengaphambili. Idatha engekho, izinto eziphuma ngaphandle (isb, ukukhuphuka okungazelelwe ngenxa yokuhlukana kwesitoko noma izehlakalo zesikhathi esisodwa), kanye nezinguquko zesimiso ezimakethe konke kungathinta ukuqeqeshwa kwamamodeli. Amasu afana nokujwayelekile, ukuhoxisa, noma ukususa isizini angasetshenziswa kudatha yokufaka. Ezinye izindlela ezithuthukisiwe zibolisa uchungechunge lwamanani lube izingxenye (okuthrendayo, imijikelezo, umsindo) futhi zizimodela ngokwehlukana (njengoba kubonakala ocwaningweni oluhlanganisa ukubola kwemodi eguquguqukayo namanethi e-neural ( Isibikezelo Semakethe Yezitoko Ukusebenzisa Ukufunda Okujulile Kokuqinisa )).
Amamodeli ahlukene anezidingo ezihlukile zokuqeqesha: amamodeli okufunda ajulile angase adinge amakhulu ezinkulungwane zamaphoyinti edatha futhi azuze ekusheshiseni kwe-GPU, kuyilapho amamodeli alula afana nokuhlehla kwezinto angafunda kumadathasethi amancane uma kuqhathaniswa. Amamodeli okufunda okuqinisa adinga isifanisi noma indawo ozosebenzisana nayo; kwesinye isikhathi idatha yomlando idlalelwa kabusha kumenzeli we-RL, noma izifanisi zemakethe zisetshenziselwa ukukhiqiza umuzwa.
Ekugcineni, uma eseqeqeshiwe, lawa mamodeli aveza umsebenzi wokubikezela - isibonelo, okukhiphayo okungaba inani elibikezelwe lakusasa, amathuba okuthi isitoko sizokhuphuka, noma isenzo esinconyiwe (ukuthenga/ukuthengisa). Lezi zibikezelo ngokuvamile zihlanganiswa nesu lokuhweba (ngokulinganisa isikhundla, imithetho yokulawula ubungozi, njll.) ngaphambi kokuba imali yangempela ibekwe engcupheni.
Imikhawulo Nezinselele
Yize amamodeli e-AI abe yinkimbinkimbi ngendlela emangalisayo, ukubikezela kwemakethe yesitoko kusewumsebenzi oyinselele ngokwemvelo . Okulandelayo yimikhawulo eyinhloko nezithiyo ezivimbela i-AI ekubeni ngumbhuli oqinisekisiwe ezimakethe:
-
Ukusebenza Kwemakethe Nokungahleliwe: Njengoba kushiwo ngaphambili, i-Efficient Market Hypothesis ithi amanani asevele abonisa ulwazi olwaziwayo, ngakho-ke noma yiluphi ulwazi olusha ludala ukulungiswa ngokushesha. Ngokwezinto ezibonakalayo, lokhu kusho ukuthi izinguquko zentengo ziqhutshwa kakhulu ezingalindelekile noma ukushintshashintsha okungahleliwe. Ngempela, amashumi eminyaka ocwaningo athole ukuthi ukunyakaza kwamanani entengo yesikhathi esifushane kufana nokuhamba okungahleliwe ( Amamodeli okubikezela amasheya aqhutshwa yidatha asekelwe kumanethiwekhi e-neural: Ukubuyekezwa ) - intengo yayizolo ayinakho ukuthonya okuncane kwakusasa, ngaphezu kwalokho okungenzeka ukubikezela. Uma izintengo zesitoko empeleni zingahleliwe noma "zisebenza kahle," ayikho i-algorithm engakwazi ukuzibikezela ngokunemba okuphezulu. Njengoba olunye ucwaningo lukubeka ngamafuphi, "i-hypothesis yokuhamba okungahleliwe kanye ne-hypothesis yemakethe ephumelelayo isho ngokuyisisekelo ukuthi akunakwenzeka ukubikezela ngokuhlelekile, ngokuthembekile izintengo zesitoko zesikhathi esizayo" ( Ukubikezela ukubuyisela okuhlobene kwesitoko se-S&P 500 kusetshenziswa umshini wokufunda | Ukuqamba Okusha Kwezezimali | Umbhalo Ogcwele ). Lokhu akusho ukuthi izibikezelo ze-AI zihlala zingenamsebenzi, kodwa kugcizelela umkhawulo oyisisekelo: ukunyakaza okuningi kwemakethe kungase kube umsindo ngisho nemodeli engcono kakhulu engakwazi ukubikezela kusengaphambili.
-
Umsindo kanye Nezici Zangaphandle Ezingalindelekile: Izintengo zesitoko zithonywa inqwaba yezinto, eziningi zazo ezingaphandle futhi azibikezeli. Izehlakalo zezwe (izimpi, ukhetho, izinguquko zokulawula), izinhlekelele zemvelo, izifo eziwubhadane, amahlazo ezinkampani angazelelwe, noma namahemuhemu ezinkundleni zokuxhumana amagciwane anganyakazisa izimakethe kungazelelwe. Lena imicimbi lapho imodeli ingakwazi ukuba nedatha yokuqeqeshwa kwangaphambilini (ngoba ayikaze ibonwe ngaphambili) noma ezenzeka njengokushaqisa okungajwayelekile. Isibonelo, ayikho imodeli ye-AI eqeqeshwe kudatha yomlando kusukela ngo-2010–2019 eyayingase ibone kusengaphambili ukuphahlazeka kwe-COVID-19 ngasekuqaleni kuka-2020 noma ukuphindeka kwayo ngokushesha. Amamodeli ezezimali e-AI adonsa kanzima lapho imibuso ishintsha noma lapho umcimbi owodwa uqhuba amanani. Njengoba omunye umthombo uphawula, izici ezifana nemicimbi ye-geopolitical noma ukukhishwa kwedatha yezomnotho kungazelelwe kungenza izibikezelo zingasebenzi cishe ngokushesha ( Ukusebenzisa Ukufunda Ngomshini Ukubikezela Kwemakethe Yezitoko... | FMP ) ( Ukusebenzisa Ukufunda Ngomshini Ukubikezela Imakethe Yezitoko... | FMP ). Ngamanye amazwi, izindaba ezingalindelekile zingahlala zeqa izibikezelo ze-algorithmic , zifake izinga lokungaqiniseki elingenakuncishiswa.
-
Ukufaka ngokweqile kanye Nokwenza Okuvamile: Amamodeli okufunda omshini athambekele ekufakeni ngokweqile - okusho ukuthi angase afunde "umsindo" noma izinkinga kudatha yokuqeqeshwa kahle kakhulu, kunamaphethini avamile ayisisekelo. Imodeli efakwe ngokweqile ingase isebenze kahle kakhulu kudatha yomlando (ngisho nokubonisa imbuyiselo ehlaziywe umxhwele noma ukunemba okuphezulu kwesampula) kodwa yehluleke ngokudabukisayo kudatha entsha. Lona ugibe ovamile emalini yobuningi. Isibonelo, inethiwekhi eyinkimbinkimbi ye-neural ingase ithole ukuhlobana okungamanga okwenzeka esikhathini esidlule ngokuqondana (njengenhlanganisela ethile yezinkomba eziphambanayo ezenzeke ngaphambi kwemibuthano eminyakeni engu-5 edlule) kodwa lobo budlelwano bungase bungapheli ukuya phambili. Umfanekiso osebenzayo: umuntu angaklama imodeli ebikezela ukuthi abawine isitoko ngonyaka odlule bayohlala bekhuphuka - ingase ilingane nesikhathi esithile, kodwa uma umbuso wemakethe ushintsha, leyo phethini iyaphuka. Ukufakwa ngokweqile kuholela ekusebenzeni okungekuhle kwesampula , okusho ukuthi ukubikezela kwemodeli ekuhwebeni okubukhoma kungase kube ngcono kunokungahleliwe naphezu kokubukeka kukuhle ekuthuthukisweni. Ukugwema ukucwiliswa ngokweqile kudinga amasu afana nokujwayelekile, ukugcina ubunkimbinkimbi bemodeli bubekiwe, nokusebenzisa ukuqinisekiswa okuqinile. Kodwa-ke, wona kanye ubunkimbinkimbi obunikeza amamodeli e-AI amandla nawo buwenza abe sengcupheni yalolu daba.
-
Ikhwalithi Yedatha kanye Nokutholakala: Isisho esithi “udoti ngaphakathi, udoti uphume” sisebenza kakhulu ku-AI ekubikezelweni kwesitoko. Ikhwalithi, inani, nokuhlobana kwedatha kunomthelela omkhulu ekusebenzeni kwemodeli. Uma idatha yomlando inganele (isb, ukuzama ukuqeqesha inethiwekhi ejulile eminyakeni embalwa nje yezintengo zesitoko) noma ingameleli (isb, ukusebenzisa idatha yenkathi ye-bullish enkulu ukubikezela isimo se-bearish), imodeli ngeke ifane kahle. Idatha ingase futhi ichema noma ibe ngaphansi kokusinda (isibonelo, izinkomba zesitoko ngokwemvelo zishiya izinkampani ezingasebenzi kahle ngokuhamba kwesikhathi, ngakho idatha yenkomba yomlando ingase ichekele phezulu). Ukuhlanza nokuhlunga idatha kuwumsebenzi ongewona omncane. Ukwengeza, eminye imithombo yedatha ingabiza noma kube nzima ukuyithola, okungase kunikeze abadlali besikhungo umkhawulo kuyilapho kushiya abatshalizimali bezitolo nedatha engaphelele. Kuphinde kube nenkinga yokuvama : amamodeli okuhweba emvamisa ephezulu adinga idatha yethikhi ngayinye enevolumu enkulu futhi edinga ingqalasizinda ekhethekile, kuyilapho amamodeli wefrikhwensi ephansi angase asebenzise idatha yansuku zonke noma yeviki. Ukuqinisekisa ukuthi idatha ihambisana nesikhathi (isb., izindaba ezinedatha yentengo ehambisanayo) futhi ngaphandle kokuchema kuyinselele eqhubekayo.
-
Ukucaca Kwemodeli Nokutolika: Amamodeli amaningi e-AI, ikakhulukazi lawo ajulile afundayo, asebenza njengamabhokisi amnyama . Bangase bakhiphe isibikezelo noma isignali yokuhweba ngaphandle kwesizathu esichazwa kalula. Lokhu kungabibikho kwezinto obala kungaba yinkinga kubatshalizimali - ikakhulukazi izikhungo ezidinga ukuthethelela izinqumo kubabambiqhaza noma zihambisane nemithethonqubo. Uma imodeli ye-AI ibikezela ukuthi isitoko sizokwehla futhi incoma ukuthengiswa, umphathi wephothifoliyo angase angabaze uma engasiqondi isizathu. Ukufiphala kwezinqumo ze-AI kunganciphisa ukwethenjwa nokutholwa, kungakhathaliseki ukuthi imodeli inemba kanjani. Le nselelo igqugquzela ucwaningo ku-AI echazekayo yezezimali, kodwa kuhlala kuyiqiniso ukuthi kuvame ukuhwebelana phakathi kwemodeli eyinkimbinkimbi/ukunemba nokutolika.
-
Izimakethe Eziguqukayo Nokuncintisana: Kubalulekile ukuqaphela ukuthi izimakethe zezimali ziyaguquguquka . Uma iphethini yokubikezela itholakele (nge-AI noma iyiphi indlela) futhi isetshenziswe abadayisi abaningi, ingase iyeke ukusebenza. Isibonelo, uma imodeli ye-AI ithola ukuthi isignali ethile ivamise ukwandulela ukukhuphuka kwesitoko, abahwebi bazoqala ukusebenzisa leyo siginali ngaphambili, ngaleyo ndlela baphikise ithuba. Empeleni, izimakethe zingashintsha ukuze zenze ize amasu aziwayo . Namuhla, amafemu amaningi okuhweba kanye nezimali zisebenzisa i-AI ne-ML. Lo mncintiswano usho ukuthi noma yikuphi unqenqema kuvame ukuba kuncane futhi kuhlale isikhathi esifushane. Umphumela wukuthi amamodeli e-AI angase adinge ukuqeqeshwa kabusha nokuvuselelwa njalo ukuze ahambisane nokushintshashintsha kwemakethe. Ezimakethe eziwuketshezi kakhulu nezivuthiwe (njengamasheya amakhulu ase-US), abadlali abaningi abasezingeni eliphezulu bazingela amasiginali afanayo, okwenza kube nzima kakhulu ukugcina umphetho. Ngokuphambene, ezimakethe ezingasebenzi kahle noma ezimpahleni ze-niche, i-AI ingase ithole ukungasebenzi kwesikhashana - kodwa njengoba lezo zimakethe zithuthuka, igebe lingase livaleke. Lokhu kuguquguquka kwezimakethe kuyinselelo ebalulekile: "imithetho yegeyimu" ayimi, ngakho imodeli esebenze ngonyaka odlule ingase idinge ukuthi isetshenziswe kabusha ngonyaka ozayo.
-
Izingqinamba Zomhlaba Wangempela: Ngisho noma imodeli ye-AI ingabikezela amanani ngokunemba okuhle, ukuguqula izibikezelo zibe inzuzo kungenye inselele. Ukuhweba kufaka izindleko zokwenziwa , njengamakhomishini, ukushelela, nezintela. Imodeli ingase ibikezele ukunyakaza okuningi kwamanani amancane ngendlela efanele, kodwa izinzuzo zingasulwa ngezindleko nomthelela wemakethe wokuhweba. Ukulawulwa kobungozi nakho kubalulekile - akukho ukubikezela okuqinisekile okungu-100%, ngakho-ke noma yiliphi isu eliqhutshwa yi-AI kufanele libhekane nokulahlekelwa okungase kube khona (ngemiyalo yokulahlekelwa kokuyeka, ukuhlukahluka kwephothifoliyo, njll.). Izikhungo zivame ukuhlanganisa izibikezelo ze-AI zibe uhlaka olubanzi lwengozi ukuze ziqinisekise ukuthi i-AI ayibhejeli ipulazi ngesibikezelo esingase sibe nephutha. Lokhu kucatshangelwa okusebenzayo kusho ukuthi umphetho wetiyori we-AI kufanele ube mkhulu ukuze ube wusizo ngemva kokungqubuzana komhlaba wangempela.
Kafushane, i-AI inamandla amakhulu, kodwa le mikhawulo iqinisekisa ukuthi imakethe yamasheya ihlala iwuhlelo olungabikezelwa kancane, olungabikezeleki ngokwengxenye . Amamodeli e-AI angakwazi ukutshekisa amathuba ngendlela evuna abatshalizimali ngokuhlaziya idatha ngendlela ephumelela kakhudlwana futhi ngokunokwenzeka embule amasiginali acashile wokuqagela. Kodwa-ke, inhlanganisela yamanani entengo ephumelelayo, idatha enomsindo, izehlakalo ezingalindelekile, kanye nezingqinamba ezingokoqobo kusho ukuthi ngisho ne-AI engcono kakhulu ngezinye izikhathi iyoba iphutha - ngokuvamile kanjalo ngokungalindelekile.
Ukusebenza kwamamodeli we-AI: Buthini Ubufakazi?
Uma kubhekwa kokubili intuthuko kanye nezinselele okuxoxwe ngazo, yini esiyifundile ocwaningweni nasemizameni yomhlaba wangempela yokusebenzisa i-AI ekubikezelweni kwesitoko? Imiphumela kuze kube manje ixubile, igqamisa kokubili impumelelo ethembisayo nokwehluleka okusangulukisayo :
-
Izimo Zethuba Le-AI Elisebenza Kakhulu: Ucwaningo oluningana lubonise ukuthi amamodeli e-AI anganqoba ukuqagela okungahleliwe ngaphansi kwezimo ezithile. Isibonelo, ucwaningo lwango-2024 lwasebenzisa inethiwekhi ye-LSTM ye-neural ukuze ibikezele amathrendi emakethe yamasheya yase-Vietnamese futhi yabika ukunemba okuphezulu kokubikezela - cishe okungu-93% kudatha yokuhlola ( Ukusebenzisa ama-algorithms okufunda ngomshini ukuze ubikezele intengo yesitoko kumakethe yamasheya - Indaba yase-Vietnam | Ezobuntu Nezokuxhumana Kwesayensi Yezenhlalakahle ). Lokhu kusikisela ukuthi kuleyo makethe (umnotho osafufusa), imodeli yakwazi ukuthwebula amaphethini angaguquki, mhlawumbe ngenxa yokuthi imakethe yayinokungasebenzi kahle noma amathrendi aqinile ezobuchwepheshe afundwa yi-LSTM. Olunye ucwaningo ngo-2024 lwathatha indawo ebanzi: abacwaningi bazama ukubikezela imbuyiselo yesikhathi esifushane yazo zonke izitoko ze-S&P 500 (imakethe esebenza kahle kakhulu) besebenzisa amamodeli e-ML. Bayifake njengenkinga yokuhlukanisa - ukubikezela ukuthi isitoko sizodlula inkomba ngo-2% ezinsukwini eziyi-10 ezizayo - kusetshenziswa ama-algorithms afana namahlathi angahleliwe, i-SVM, ne-LSTM. Umphumela: imodeli ye-LSTM isebenze kahle kakhulu kunamanye amamodeli e-ML kanye nesisekelo esingahleliwe , nemiphumela ebaluleke ngokwezibalo ngokwanele ukuphakamisa ukuthi bekungeyona nje inhlanhla ( Ukubikezela ukubuyisela okuhlobene kwesitoko se-S&P 500 kusetshenziswa umshini wokufunda | Ukuqamba Okusha Kwezezimali | Umbhalo Ogcwele ). Ababhali baze baphetha ngokuthi kulokhu kusetha okuqondile, amathuba okuthi i -hypothesis yokuhamba okungahleliwe “ayemancane ngokunganaki,” okubonisa ukuthi amamodeli abo e-ML athole amasiginali wangempela wokubikezela. Lezi zibonelo zibonisa ukuthi i-AI ingakwazi ngempela ukuhlonza amaphethini anikeza umkhawulo (ngisho noma inesizotha) ekubikezeleni umnyakazo wesitoko, ikakhulukazi uma ihlolwa kumasethi amakhulu edatha.
-
Amacala Okusetshenziswa Okuphawulekayo Embonini: Ngaphandle kwezifundo zezemfundo, kukhona imibiko ye-hedge funds nezikhungo zezezimali ezisebenzisa ngempumelelo i-AI emisebenzini yazo yokuhweba. Ezinye izinkampani ezihweba ngamaza aphezulu zisebenzisa i-AI ukuze zibone futhi zisabele kumaphethini wesakhiwo esincane semakethe ngezingxenyana zesekhondi. Amabhange amakhulu anamamodeli we-AI wokwabiwa kwephothifoliyo kanye nokubikezela ingozi , okuthi, nakuba kungahlali njalo mayelana nokubikezela intengo yesitoko esisodwa, kuhilela izici zokubikezela zemakethe (njengokuguquguquka noma ukuhlobana). Kukhona nezimali eziqhutshwa yi-AI (ezivamise ukubizwa ngokuthi “imali eningi”) ezisebenzisa ukufunda ngomshini ukwenza izinqumo zokuhweba - ezinye zisebenze kahle kakhulu emakethe izikhathi ezithile, nakuba kunzima ukukuchaza lokho nge-AI njengoba ngokuvamile zisebenzisa inhlanganisela yobuhlakani bomuntu nomshini. Uhlelo lokusebenza oluphathekayo luwukusetshenziswa kokuhlaziya imizwa ye-AI: isibonelo, ukuskena izindaba kanye ne-Twitter ukubikezela ukuthi izintengo zesitoko zizohamba kanjani ekuphenduleni. Amamodeli anjalo angahle angabi nemba ngo-100%, kodwa anganikeza abahwebi isiqalo esincane sokubeka amanani ezindabeni. Kuhle ukuqaphela ukuthi amafemu ngokuvamile agada imininingwane yamasu e-AI aphumelelayo eduze njengendawo yobuhlakani bengqondo, ngakho ubufakazi esizindeni somphakathi buvame ukunethezeka noma bube yindabakwane.
-
Izimo Zokungasebenzi Ngokungaphansi Nokungaphumeleli: Kuyo yonke indaba yempumelelo, kunezinganekwane eziyisixwayiso. Izifundo eziningi zezemfundo ezifuna ukunemba okuphezulu emakethe eyodwa noma isikhathi esibekiwe zehlulekile ukwenziwa ngokujwayelekile. Ukuhlolwa okuphawulekayo kuzamile ukuphindaphinda ucwaningo oluyimpumelelo lokubikezela imakethe yamasheya yase-India (ebenokunemba okuphezulu kusetshenziswa i-ML kuzinkomba zobuchwepheshe) ezitokweni zase-US. Ukuphindaphinda akutholanga amandla abalulekile okubikezela - empeleni, isu elingenangqondo lokuhlala libikezela isitoko sizokhuphuka ngakusasa lisebenza kahle kakhulu kunamamodeli ayinkimbinkimbi e-ML ngokunemba. Ababhali baphethe ngokuthi imiphumela yabo “isekela ithiyori yokuhamba ngokungahleliwe” , okusho ukuthi umnyakazo wesitoko wawungabikezeleki futhi amamodeli e-ML awazange asize. Lokhu kugcizelela ukuthi imiphumela ingahluka kakhulu ngokwemakethe nangesikhathi. Ngokufanayo, imincintiswano eminingi ye-Kaggle kanye nemincintiswano yocwaningo lwenani kubonise ukuthi nakuba amamodeli engakwazi ukulingana kahle nedatha edlule, ukusebenza kwawo ekuhwebeni okubukhoma kuvame ukuhlehla kufinyelele ku-50% wokunemba (ngokubikezela isiqondiso) uma ebhekane nezimo ezintsha. Izimo ezifana nokuwohloka kwesikhwama semali ka-2007 nobunzima obubhekene nezimali eziqhutshwa yi-AI ngesikhathi sokushaqeka kobhubhane lwango-2020 kukhombisa ukuthi amamodeli e-AI angantenga ngokuzumayo lapho umbuso wemakethe ushintsha. Ukuchema kokusinda kuyisici emibonweni futhi - sizwa ngempumelelo ye-AI kaningi kunokwehluleka, kodwa ngemuva kwezigcawu, amamodeli amaningi kanye nezimali zihluleka buthule futhi zivaliwe ngoba amasu abo ayayeka ukusebenza.
-
Umehluko Kuzo Zonke Izimakethe: Okuqaphelekayo okuthakazelisayo okuvela ezifundweni ukuthi ukusebenza kahle kwe-AI kungase kuncike ekuvuthweni kwemakethe nokusebenza kahle . Ezimakethe ezisebenza kancane noma ezisafufusa, kungase kube namaphethini asebenziseka kalula (ngenxa yokufakwa okuphansi komhlaziyi, imikhawulo yemali yemali, noma ukuchema kokuziphatha), okuvumela amamodeli we-AI ukuthi azuze ukunemba okuphezulu. Ucwaningo lwe-LSTM lwemakethe yaseVietnam ngokunemba okungama-93% lungaba isibonelo salokhu. Ngokuphambene, ezimakethe ezisebenza kahle kakhulu njenge-US, lawo maphethini angase akhishwe ngokushesha. Imiphumela exubile phakathi kwecala lase-Vietnam kanye nocwaningo lokuphindaphinda lwase-US lubonisa lokhu kuhluka. Emhlabeni jikelele, lokhu kusho ukuthi i-AI okwamanje ingase ikhiqize ukusebenza okubikezela okungcono kakhulu ezimakethe ezithile ze-niche noma amakilasi ezimpahla (isibonelo, abanye basebenzise i-AI ukuze babikezele amanani entengo noma amathrendi e-cryptocurrency ngempumelelo ehlukahlukene). Ngokuhamba kwesikhathi, njengoba zonke izimakethe ziqhubekela ekusebenzeni kahle okukhulu, iwindi lokubikezela okulula liyancipha.
-
Ukunemba vs. Inzuzo: Kubalulekile futhi ukuhlukanisa ukunemba kokubikezela nenzuzo yokutshala imali . Imodeli ingaba kuphela, ake sithi, inemba ngo-60% ekubikezeleni ukunyakaza kwansuku zonke kwenyuka noma phansi kwesitoko - okungazwakali kuphezulu kakhulu - kodwa uma lezo zibikezelo zisetshenziswa kuhlelo lokuhweba oluhlakaniphile, zingaba nenzuzo enkulu. Ngokuphambene, imodeli ingase iziqhayise ngokunemba okungu-90% kodwa uma izikhathi ezingu-10% ingalungile ziqondana nokunyakaza okukhulu kwemakethe (futhi ngaleyo ndlela nokulahlekelwa okukhulu), kungase kungabi nanzuzo. Imizamo eminingi yokubikezela isitoko se-AI igxile ekunembeni kokuqondisa noma ekunciphiseni amaphutha, kodwa abatshalizimali banendaba nembuyiselo elungiswe ubungozi. Ngakho-ke, ukuhlola kuvame ukufaka amamethrikhi afana nesilinganiso se-Sharpe, ama-drawdowns, nokuvumelana kokusebenza, hhayi nje izinga lokushaywa okungahluziwe. Amanye amamodeli we-AI ahlanganiswe ezinhlelweni zokuhweba ze-algorithmic ezilawula izikhundla nobungozi ngokuzenzakalelayo - ukusebenza kwazo kwangempela kukalwa ngembuyiselo yokuhweba bukhoma kunezibalo zokubikezela ezizimele. Kuze kube manje, "umhwebi we-AI" ozimele ngokugcwele ofaka imali ngokuthembekile unyaka nonyaka uyinganekwane yesayensi kuneqiniso, kodwa izinhlelo zokusebenza ezincane (njengemodeli ye-AI ebikezela ukuguquguquka abadayisi abangayisebenzisa ekukhetheni amanani, njll.) bathole indawo kukhithi yamathuluzi yezezimali.
Sekuhlangene, ubufakazi buphakamisa ukuthi i-AI ingabikezela amaphethini athile emakethe ngokunemba okungcono kunamathuba , futhi ngokwenza kanjalo inganikeza umkhawulo wokuhweba. Kodwa-ke, lolo mkhawulo ngokuvamile luncane futhi ludinga ukubulawa okuyinkimbinkimbi ukuze kusetshenziswe imali. Uma othile ebuza, ingabe i-AI ingabikezela imakethe yamasheya? , impendulo ethembeke kakhulu esekelwe ebufakazini bamanje ithi: I-AI ngezinye izikhathi ingabikezela izici zemakethe yamasheya ngaphansi kwezimo ezithile, kodwa ayikwazi ukwenza kanjalo ngokungaguquki kuzo zonke izitoko ngaso sonke isikhathi . Impumelelo ivama ukuncika ngokwengxenye kanye nomongo.
Isiphetho: Okulindelwe Okungokoqobo kwe-AI ku-Stock Market Prediction
I-AI nokufunda komshini ngokungangabazeki kube ngamathuluzi anamandla kwezezimali. Basebenza kahle kakhulu ekucubunguleni amadathasethi amakhulu, bembula ukuhlobana okufihliwe, ngisho nokulungisa amasu empukaneni. Emzamweni wokubikezela imakethe yamasheya, i-AI ilethe okubambekayo kodwa okulinganiselwe . Abatshalizimali nezikhungo bangalindela ngokweqiniso ukuthi i-AI isize ekuthathweni kwezinqumo - ngokwesibonelo, ngokukhiqiza amasiginali abikezelayo, amaphothifoliyo okuthuthukisa, noma ukulawula ubungozi - kodwa hhayi ukusebenza njengebhola lekristalu eliqinisekisa inzuzo.
Lokho I-AI
Engakwenza : I-AI ingathuthukisa inqubo yokuhlaziya ekutshaleni imali. Ingahlunga phakathi neminyaka yedatha yemakethe, izifunzo zezindaba, nemibiko yezezimali ngemizuzwana, ithola amaphethini acashile noma okudidayo umuntu angase akunake ( Ukusebenzisa Ukufunda Ngomshini Ukubikezela Imakethe Yezitoko... | FMP ). Ingakwazi ukuhlanganisa amakhulukhulu okuguquguqukayo (ubuchwepheshe, okuyisisekelo, imizwa, njll.) ibe isibikezelo esihlangene. Ekuhwebeni kwesikhathi esifushane, ama-algorithms e-AI angase abikezele kangcono kunokunemba okungahleliwe ukuthi isitoko esisodwa sizodlula esinye, noma ukuthi imakethe isizoba nokwanda kokuntengantenga. Le miphetho ekhuphukayo, lapho ixhashazwa kahle, ingahumushela ekuzuzweni kwezezimali kwangempela. I-AI futhi ingasiza ekulawuleni ubungozi - ukuhlonza izexwayiso zangaphambi kwesikhathi zokwehla noma ukwazisa abatshalizimali ngezinga lokuzethemba lesibikezelo. Enye indima engokoqobo ye-AI isekuzenzakaleni kwamasu : ama-algorithms angenza ukuhweba ngesivinini esikhulu kanye nemvamisa, asabele ezehlakalweni ezingama-24/7, futhi aphoqelele isiyalo (akukho ukuhweba ngokomzwelo), okungaba wusizo ezimakethe ezishintshashintshayo.
Lokho I-AI
Engakwazi Ukuyenza (Okwamanje): Naphezu kwe-hype kweminye imidiya, i-AI ayikwazi ukubikezela ngokungaguquki nangokwethembeka imakethe yamasheya ngomqondo ophelele wokuhlala ishaya imakethe noma ibone kusengaphambili amaphuzu amakhulu okushintsha. Izimakethe zithintwa ukuziphatha komuntu, izehlakalo ezingahleliwe, nezihibe zempendulo eziyinkimbinkimbi ezingahambisani nanoma iyiphi imodeli emile. I-AI ayikuqedi ukungaqiniseki; ikhuluma ngezinto ezingenzeka kuphela. I-AI ingase ibonise amathuba angu-70% okuthi isitoko sikhuphuke kusasa - okusho ukuthi amathuba angama-30% ngeke sikhuphuke. Ukulahlekelwa ukuhweba kanye nezingcingo ezimbi akunakugwenywa. I-AI ayikwazi ukulindela izehlakalo ezintsha ngempela (ezivamise ukubizwa ngokuthi “amadada amnyama”) angaphandle kwendawo yedatha yayo yokuqeqeshwa. Ngaphezu kwalokho, noma iyiphi imodeli yokubikezela ephumelelayo imema ukuncintisana okungacekela phansi inzuzo yayo. Empeleni, ayikho i-AI elingana nebhola lekristalu eliqinisekisa ukubona izinto kusengaphambili ngekusasa lemakethe. Abatshalizimali kufanele baqaphele noma ubani othi ngenye indlela.
Umbono Ongathathi hlangothi, Oweqiniso:
Ngokombono ongathathi hlangothi, i-AI ibonakala kangcono njengesithuthukisi, hhayi esikhundleni sokuhlaziywa kwendabuko kanye nokuqonda komuntu. Empeleni, abatshalizimali abaningi bezikhungo basebenzisa amamodeli e-AI kanye nokufaka okuvela kubahlaziyi abangabantu nabaphathi bephothifoliyo. I-AI ingase inciphise izinombolo nokubikezela kokuphumayo, kodwa abantu babeke izinjongo, bahumushe imiphumela, futhi balungise amasu omongo (isb, ukweqa imodeli ngesikhathi senhlekelele engalindelekile). Abatshalizimali abathengisayo abasebenzisa amathuluzi aqhutshwa yi-AI noma ama-bot okuhweba kufanele bahlale beqaphile futhi baqonde umqondo nemikhawulo yethuluzi. Ukulandela ngokungaboni isincomo se-AI kuyingozi - umuntu kufanele akusebenzise njengokufaka okukodwa phakathi kokuningi.
Ekumiseni okulindelwe okungokoqobo, umuntu angase aphethe ngokuthi: I-AI ingabikezela imakethe yamasheya ngezinga elithile, kodwa hhayi ngokuqiniseka futhi ngaphandle kwephutha . Ingakhuphula amathuba okushaya ucingo okulungile noma ithuthukise ukusebenza kahle ekuhlaziyeni ulwazi, okuthi ezimakethe ezincintisanayo kube umehluko phakathi kwenzuzo nokulahlekelwa. Kodwa-ke, akukwazi ukuqinisekisa impumelelo noma ukuqeda ukuguquguquka okungokwemvelo kanye nobungozi bezimakethe zokulingana. Njengoba enye incwadi yabonisa, ngisho nangama-algorithms asebenzayo, imiphumela emakethe yamasheya ingaba "engabikezeli ngokwemvelo" ngenxa yezici ezingaphezu kolwazi oluyimodeli ( Isibikezelo Semakethe Yezimakethe Ukusebenzisa Ukufunda Okujulile Kokuqinisa ).
Indlela Engaphambili:
Uma sibheka phambili, indima ye-AI ekubikezelweni kwezimakethe zamasheya cishe izokhula. Ucwaningo oluqhubekayo lubhekana nemikhawulo ethile (ngokwesibonelo, ukuthuthukisa amamodeli abangela izinguquko zombuso, noma amasistimu ayingxube ahlanganisa kokubili ukuhlaziywa okushayelwa yidatha kanye nomcimbi). Kuphinde kube nentshisekelo kuma -ejenti okufunda okuqinisa ajwayela ngokuqhubekayo kudatha yemakethe entsha ngesikhathi sangempela, okungase kube namandla okusingatha izimo eziguqukayo kangcono kunamamodeli aqeqeshiwe amile. Ngaphezu kwalokho, ukuhlanganisa i-AI namasu avela ezezimali zokuziphatha noma ukuhlaziywa kwenethiwekhi kungase kuveze amamodeli anothile wokuguquguquka kwemakethe. Noma kunjalo, ngisho ne-AI yesikhathi esizayo ethuthuke kakhulu izosebenza ngaphakathi kwemingcele yamathuba nokungaqiniseki.
Kafushane, umbuzo othi "Ingabe i-AI ingabikezela imakethe yamasheya?" akanayo impendulo elula ethi yebo noma cha. Impendulo enembe kakhulu ithi: I-AI ingasiza ukubikezela imakethe yamasheya, kodwa ayinaphutha. Inikeza amathuluzi anamandla okuthi, lapho esetshenziswa ngokuhlakanipha, angathuthukisa amasu okubikezela nokuhweba, kodwa akususi ukungaqiniseki okuyisisekelo kwezimakethe. Abatshalizimali kufanele bamukele i-AI ngenxa yamandla ayo - ukucubungula idatha nokuqashelwa kwephethini - kuyilapho beqaphela ubuthakathaka bayo. Ngokwenza kanjalo, umuntu angasebenzisa okuhle kakhulu kuyo yomibili imihlaba: ukwahlulela komuntu kanye nobuhlakani bomshini obusebenza ndawonye. Imakethe yamasheya ingase ingabikezeleki ngo-100%, kodwa ngokulindelwe okungokoqobo nokusebenzisa ngobuhlakani i-AI, ababambiqhaza bemakethe bangaphokophela ukuthola izinqumo zokutshala ezinolwazi olungcono nezinokuziphatha okuhle endaweni yezezimali ehlala ishintsha.
Amaphepha amhlophe ongase uthande ukuwafunda ngemva kwaleli:
🔗 Imisebenzi I-AI Engakwazi Ukuyishintsha - Futhi Yimiphi Imisebenzi I-AI Ezoyishintsha?
Thola ukuthi yimiphi imisebenzi enobufakazi besikhathi esizayo nokuthi yimiphi esengozini enkulu njengoba i-AI ibumba kabusha ukuqashwa emhlabeni.
🔗 Yini Okungathenjelwa Kuyo I-Generative AI Ngaphandle Kokungenelela Komuntu?
Qonda imingcele yamanje namandla okuzimela we-AI ekhiqizayo kuzimo ezisebenzayo.
🔗 Ingasetshenziswa Kanjani I-Generative AI Ku-Cybersecurity?
Funda ukuthi i-AI izivikela kanjani ezinsongweni futhi ithuthukisa ukuqina ku-inthanethi ngamathuluzi aqagelayo nazimele.