Kudzidza kwemichina kuri kushandura mabatiro atinoita zviitiko zvezuva nezuva uye matambudziko akaomarara, kuvandudza zvese kubva pakurongeka kwemunhu kuenda kumazano ebhizinesi. Gwaro iri rinoongorora zvakakosha zvekudzidza kwemichina, mashandisiro ayo anoshanda mumaindasitiri akasiyana siyana, uye neshanduko yawo basa rekuita landscapes.
Munzvimbo inokurumidza kushanduka tekinoroji, kunzwisisa muchina kudzidza kwakakosha. Iyi nzvimbo ine simba inowedzera kugona kwekuongorora data, inogadzirisa mabasa ezuva nezuva, uye inovhura mikana mitsva yebasa. Ziva iwo epasi misimboti yekudzidza muchina uye ona kuti iri kushandiswa sei zvine hutsva muzvikamu zvese.
Joinha isu patinenge tichiita kuti kuomarara kweiyi tekinoroji yepamberi kuwanikwe uye kubatika kumunhu wese, kubva kuvadzidzi vanotanga nzendo dzavo dzedzidzo kusvika kunyanzvi dzinovandudza hunyanzvi hwavo.
Kunzwisisa muchina kudzidza
Machine pakudzidza inzvimbo ine simba mukati meiyo artificial intelligence (AI) inobvumira masisitimu kuti adzidze kubva kune data uye kuita sarudzo nekushoma kupindira kwevanhu. Inopembererwa nekugona kwayo kuvandudza otomatiki kuburikidza nekuenderera kwekuongorora kwedata uye kushandiswa kwealgorithms epamberi.
Zvinangwa uye mashandisirwo ekudzidza muchina
Kudzidza kwemichina kune chinangwa chekuzadzisa zvinangwa zvakakosha:
- Kuronga data. Kuziva mapatani uye kuronga data zvinobudirira, sekurongedza maemail muzvikamu zvechokwadi uye spam.
- Kuita fungidziro. Kushandisa nhoroondo yedata kufanotaura zviitiko zvenguva yemberi, sekufungidzira mitengo yemba mumaguta akasiyana.
Nzira idzi dzinoshandiswa munzvimbo dzakasiyana siyana, dzichikanganisa zvakanyanya minda inosanganisira kududzira mutauro, ongororo yezvinodiwa nevatengi, uye kuongororwa kwekurapa.
Theoretical nheyo dzekudzidza muchina
Kuongorora mune ekutanga dzidziso kuseri kwekudzidza kwemuchina kunopa nzwisiso yakadzama mumashandiro ayo:
- Statistical learning theory. Mazhinji emuchina ekudzidza algorithms akavakirwa pakuvaka manhamba emhando yekudzidza kubva kune data. Iyi dzidziso inobatsira mukunzwisisa kugadzirisa kunoitwa nealgorithms.
- Computational kudzidza dzidziso. Iyi nharaunda yesainzi yekombuta inodzidza masvomhu ekutanga kuseri kwekudzidza maalgorithms, ichipa kunzwisisa kwakajeka kwemabatiro avanoita uye kuti yakaoma sei.
- Neural network. Yakateedzerwa mushure mehuropi hwemunhu, neural network yakakosha pakudzidza kwakadzama uye yakakosha pakuona yakaoma mapatani uye anomalies mune data.
Evolution uye kukanganisa
Kudzidza kwemichina kuri kuramba kuchienderera mberi, kuchiratidza kusimukira muhunyanzvi uye zvinodiwa munharaunda:
- Nhoroondo yezvakaitika. Kubva pamazuva ekutanga, kudzidza kwemuchina kwafambira mberi kubva kukutanga algorithms kuenda kune epasirese masisitimu anokwanisa kushandisa mota dzinozvimiririra uye kufambisa kuongororwa kwekurapa.
- Mafambiro emangwana. Parizvino, munda uri kusangana nekufambira mberi kukuru mukati AI tsika, kukura kwe quantum computing, uye kuwanikwa kwemikana mitsva yemusika. Zviitiko izvi zvine mukana wekukanganisa zvakanyanya maindasitiri akasiyana.
Kuwedzera maonero
Kuongorora kudzidza kwemichina kubva kumakona akasiyana kunoratidza kukosha kwayo kwakakura:
- Interdisciplinary connections. Kudzidza kwemichina kunoshanda pamwe neminda senge psychology, kuvandudza kunzwisisa kwemaitiro ekuziva, uye huzivi, kugadzirisa nyaya dzetsika. Uku kuedza kwekudyidzana kwakakosha mukunatsa AI system kusimudzira.
- Global impact. Pasi rese, kudzidza kwemichina kwakakosha mukubatsira kugadzirisa hupfumi uye kugadzirisa matambudziko makuru. Semuenzaniso, munyika dzichiri kusimukira, iri kushandura chikamu chehutano nekurima, izvo zvinogona kubatsira kukunda nyaya dzakaita sehurombo nekuvandudza hutano.
Chaiyo-nyika mashandisirwo ekudzidza muchina
Kudzidza nemuchina hakusi kungodzidza kwedzidziso kunogumira kumakirasi; chishandiso chine simba chinoshandura maindasitiri nenzvimbo dzedzidzo zvakafanana nekugadzirisa matambudziko epasirese uye nekuvandudza kugona. Ichi chikamu chinoratidza uko kudzidza kwemichina kwakave nemhedzisiro yakakura, ichipa mienzaniso inoratidza kugona kwayo kwakafara:
Nezveutano
Muindasitiri yehutano, ma algorithms emuchina akakosha pakufembera kuongororwa, kubatsira vanachiremba kudzivirira nyaya dzehutano dzakakomba nekuongorora mapatani mune data remurwere kufanotaura zvingangoitika. Aya matekinoroji anoita basa rakakosha mukuongororwa kwekutanga uye anobatsira kutarisa zvakakosha zvevarwere uye kuronga zvirongwa zvekurapa, zvichikonzera mhedzisiro yemurwere uye kuendesa hutano hwakanaka.
Automotive indasitiri
Kudzidza kwemichina kunotungamirira nzira mukuvandudza kwemotokari, kunyanya mukugadzira motokari dzinozvityaira. Aya masisitimu eAI anoongorora data kubva kune akasiyana masensa kuita sarudzo nekukurumidza dzinovandudza kuchengeteka uye kubatsira nekufamba. Mashandiro akakosha anosanganisira kuona zvipingaidzo, kuronga nzira, uye rubatsiro rwemutyairi, zvese zvinopa kune yakachengeteka uye inoshanda zvakanyanya ruzivo rwekutyaira.
Financial Services
Mune zvemari, maalgorithms epamberi anoshandura maitiro emakambani kuona hutsotsi, kuongorora njodzi, uye kutengesa. Nekunyatsoongorora dhata rekutengesa kuti uone maitiro asina kujairika, aya maalgorithms anogona kuona hutsotsi hunogona, kuongorora njodzi dzekudyara, uye otomatiki kutengesa kuti uvandudze mibairo yemari, kunyangwe mamiriro emusika achichinja.
Kuvaraidza uye midhiya
Iwo maindasitiri evaraidzo uye emidhiya anoshandisa kudzidza kwemichina kugadzirisa zviitiko zvemushandisi. Algorithms inoongorora maitiro ekuona inokurudzira mafirimu uye TV inoratidza pamapuratifomu akadai Netflix, zvinoenderana nezvido zvemunhu. Saizvozvo, kudzidza kwemichina kunoshandiswa nevashambadzi kugadzirisa kuburitsa zvemukati, kuvandudza kuita kwevaverengi uye kugutsikana.
Dzidzo uye tsvakurudzo yezvidzidzo
Muzvirongwa zvedzidzo, muchina kudzidza kunozviita otomatiki uye kunonakisa ruzivo rwekudzidza. Inogona kugadzirisa zvemukati zvedzidzo kuti zvienderane nezvinodiwa nemudzidzi wega wega zvichibva pakumhanya kwekudzidza uye maitiro, kuvandudza kubatanidzwa uye kushanda. Pamusoro pezvo, kudzidza kwemichina kunobatsira mukutsvagisa nekuongorora dhatabhesi rakakura zvakanyanya kupfuura nzira dzechinyakare, zvichibvumira kukurumidza kuongororwa kwekufungidzira uye hunyanzvi mukutsvagisa kwesainzi.
Kuongorora mashandisiro anoita muchina kudzidza
Kudzidza kwemichina kunoshanda kuburikidza neakajeka akateedzana matanho, imwe neimwe yakakosha pakuvaka yakabudirira AI modhi:
- Kuunganidzwa kwedata. Danho rekutanga rinosanganisira kuunganidza data kubva kwakasiyana siyana, kubva mumhanzi wakarekodhwa uye zvinyorwa zvekurapa kusvika kumifananidzo yekamera. Semuenzaniso, Spotify inoongorora mimhanzi yakarekodhwa kuti inzwisise zvinodiwa nevateereri uye kukurudzira nziyo nyowani. Iyi data mbishi uye isina kugadziridzwa inoumba hwaro kune ese anotevera mashini-yekudzidza maitiro.
- Kugadzirira kwedata. Mushure mekuunganidza, data inofanirwa kucheneswa uye kurongeka kuti inzwisiswe nemakomputa. Iyi nhanho inogadzirisa zvikanganiso uye inoronga data. Feature engineering, semuenzaniso, inoburitsa hunhu hwakakosha hunobatsira kugadzirisa matambudziko chaiwo, akafanana nemaziviro eGoogle Photos uye kuronga zvinhu nezviso.
- Kudzidzira muenzaniso. Kusarudza muenzaniso wakakodzera kwakakosha, uye kudzidziswa kunotanga kana modhi yasarudzwa. Pano, iyo modhi inodzidzira kubva kune data nekuziva mapatani uye kugadzirisa ma paramita ayo. Chinangwa ndechekuti modhi iite sarudzo dzakavimbika kana fungidziro. Netflix, semuenzaniso, inoshandisa mamodheru kukurudzira mashowa zvichienderana nenhoroondo yekuona kwevashandisi.
- Model optimization. Mushure mekudzidziswa, modhi inovandudzwa kuti iwedzere kurongeka kwayo uye kubatsira. Inogadziriswa kana kuyedzwa nenzira dzakasiyana kuti ishande zvirinani mumamiriro ezvinhu chaiwo. Semuenzaniso, Tesla anogara achigadziridza yayo Autopilot software kuti iwedzere kuchengetedzeka uye kuita.
- Kuongorora kwemuenzaniso. Kuedza modhi nedata idzva raasina kuona panguva yekudzidziswa kwakakosha kuti uongorore kuita kwayo. Ongororo iyi inobatsira kusarudza kuti modhi inogona sei kuenderana nemamiriro matsva nematambudziko. Semuyenzaniso, kugona kwaIBM Watson kunogara kuchiyedzwa mune dzakasiyana siyana dzehutano dzekuongorora mamiriro ekuona hunyanzvi hwayo nemhando dzakasiyana dze data revarwere.
- Model deployment. Danho rekupedzisira rinosanganisira kuendesa modhi kuita mamwe mabasa, sekuziva mifananidzo kana kufanotaura mafambiro. Amazon inoshandisa muchina kudzidza kufanotaura maitiro ekutenga uye optimize inventory management. Post-deployment, iyo modhi inoramba ichidzidza uye ichichinjika kuti igare inoshanda nekufamba kwenguva.
- Kuenderera mberi nekuvandudza. Kudzidza kwemuchina kune cyclic, nekutenderera kwega kwega kwekuunganidza data, kugadzirira, kudzidziswa, uye kutumira kunovandudza kugona kwemodhi, kutsvaga kuita kunoenderana kunyangwe nedata nyowani.
- Basa re data uye algorithms. Papakati payo, kudzidza kwemichina kunovimba nedata uye algorithms: data ndiyo yakakosha yekuisa, uye maalgorithms anoshandisa izvi kuburitsa yakakosha nzwisiso. Semuenzaniso, Google inogara ichigadziridza maalgorithms ekutsvaga kuti ive nechokwadi chekuti mibairo yekutsvaga inoenderana nekuongorora data kubva mukudyidzana kwevashandisi.
Michina yekudzidza mienzaniso
Mamodheru ekudzidza emuchina akasiyana, imwe neimwe yakasarudzika kuti idzidze uye kugadzirisa matambudziko nekugadzirisa data nemazvo. Kunzwisisa mutsauko pakati pavo kwakakosha pakushandisa kwavo kwakabudirira mumapurojekiti eAI. Pazasi pane kuongororwa kwemamodeli ekutanga ekudzidza uye mashandiro awo:
Inotarisirwa kudzidza
Rudzi rwakanyanya, kudzidza kunotariswa, rinoshandisa mamodheru anodzidza kubva kudhata rekudzidziswa rakajeka. Vanoshandisa mavara aya kuti vadzidze kufanotaura zvinobuda kana kuronga data idzva, risingaonekwe nenzira kwayo.
- Zvakajairika kunyorera. Masevhisi emaemail anoshandisa kudzidza kwakatariswa kuronga mameseji anouya kuva "spam" kana "asiri-spam".
- muenzaniso. Tekinoroji dzekuziva kumeso kuronga mapikicha evanhu nekudzidza kubva muunganidzwa wemifananidzo yakanyorwa.
Kudzidza kusingatarisirwe
Mukupesana, mhando dzekudzidza dzisina kutariswa dzinoshanda nedata risina kunyorwa. Vanoona mapatani uye hukama vari voga, vachironga data mumapoka ane maitiro akafanana.
- Muenzaniso webasa. Mukuongorora kwebhizinesi, kudzidza kusingatarisirwe kunogona kupatsanura vatengi mumapoka akasiyana zvichienderana nemaitiro avo ekutenga pasina kunyorwa kare.
Yekusimbisa kudzidza
Iyi modhi inodzidza kuburikidza nekuyedza uye kukanganisa, ichishandisa mhinduro kubva kune yayo pachayo kuita sarudzo dzinowedzera mubairo kana kuderedza njodzi munzvimbo dzisingafungidzirwe.
- Real-world application. Mota dzinozvityaira dzinoshandisa kusimbisa kudzidza kuita sarudzo dzekufamba-famba, senge nguva yekutendeuka kana mabhureki kudzivirira chipingamupinyi.
Kutsvaga algorithm chaiyo
Kusarudza algorithm yakakodzera kwakakosha uye zvinoenderana nezvinodiwa chaizvo zvepurojekiti, kusanganisira mhando yedata uye mhedzisiro inodiwa.
- Predictive modelling. Algorithms senge linear regression anoshandiswa kufanotaura huwandu hwemhedzisiro, sekufanotaura mafambiro emusika wemasheya zvichienderana nenhoroondo data.
Kubatanidzwa uye kuenderera mberi nekudzidza
Sezvo tekinoroji yekudzidza yemuchina inofambira mberi, kusanganisa mamodheru akasiyana uye kugara uchiagadziridza nedata idzva zvinova zvakakosha kuti uwedzere huchokwadi uye kushanda kwawo.
- Muenzaniso wekuvandudza. E-commerce mapuratifomu anogadzirisa zvine simba kurudziro yavo yechigadzirwa kune vashandisi nekuramba vachiongorora maitiro emushandisi uye zvavanofarira.
Imwe neimwe yemuchina yekudzidza modhi inopa yakasarudzika mabhenefiti uye inogona kunyatsogadzirirwa kune mamwe mabasa uye mamiriro. Nekunzwisisa aya mamodheru uye nekusarudza iwo algorithms akakodzera, vanogadzira vanogona kuvaka anoshanda, anogadzirisa AI masisitimu anoshanduka nekwavanogara.
Mikana yebasa mukudzidza muchina
Sezvo kudzidza kwemichina kuchienderera mberi nekuwedzera simba rayo mumaindasitiri akasiyana siyana, kunovhura hupfumi hwemikana yebasa kune avo vakagadzirirwa nehunyanzvi hunodiwa. Pazasi pane tafura yakatsanangurwa inodonongodza mabasa akakosha mumunda wekudzidza muchina, hunyanzvi hwakakosha hunodiwa, vangangove vashandirwi, uye nzira dzebasa dzaungatora:
| basa | Zvavanoita | Unyanzvi hunodiwa | Kwavanoshandira | Career path |
| Deta scientist | Ongorora seti yakakura yedata kubatsira makambani kuita sarudzo; Shandisa muchina kudzidza kuburitsa ruzivo. | • Nyanzvi mukuronga (Python/R) • Yakasimba muhuwandu • Akaziva nezve ML nzira | • Tech makambani • Banks • Vanopa hutano • Mafemu ekutengesa | Tanga sevanoongorora data, enda kumusoro kune dhizaini yeprojekiti uye hutungamiriri hwedhata. |
| Machine kudzidza injiniya | Gadzira uye maneja maML modhi kubva padhizaini kusvika pakutumirwa. | • Excellent programming hunyanzvi • Kuziva kwakadzama kweML algorithms • Unyanzvi hwekuvandudza software | • Tech makambani • Maindasitiri emotokari • mari • Ruzhinji Kuchengetedza | Tanga mune yekupinda-chikamu mabasa, uchivavarira zvinzvimbo seAI Architect kana CTO mukutanga. |
| AI yekutsvagisa sainzi | Gadzira hutsva hweAI matekinoroji uye nzira. | • PhD muCS kana munda wakabatana • Yakawedzera AI uye ML ruzivo • Chiitiko chekutsvaga | • Vunivhesiti • Research Labs • Makambani makuru etekinoroji | Tanga mukutsvaga, kufambira mberi kumapurojekiti anotungamira, kana madhipatimendi ekutsvagisa musoro. |
Zvishandiso zvekudzidza uye zvishandiso
Mushure mekutarisa akasiyana mashandisirwo uye mashandiro emuchina kudzidza, unogona kunge uchida kuziva nezvekutanga rwendo rwako mune ino simba ndima. Pazasi pane yakazara rondedzero yezviwanikwa uye maturusi anogona kukubatsira mukuongorora zvakadzika, kubva online makosi uye mabhuku ezvinyorwa kune yakavhurika-sosi software uye inoshanda nharaunda maforamu. Izvi zviwanikwa zvakagadzirirwa kutsigira vadzidzi pamatanho ese, ungave uchangotanga kana kutsvaga kuwedzera ruzivo rwako rwagara.
Online courses
- Coursera - Kudzidza Muchina naAndrew Ng. Kosi yakakurumbira yekutanga iyo inovhara huwandu hwakawanda hwepfungwa dzakakosha.
- edX - Artificial Intelligence MicroMasters neColumbia University. Akatevedzana evakapedza kudzidza-chikamu makosi akagadzirirwa kufambisira mberi basa rako muAI.
- Udacity - AI uye Machine Kudzidza Nanodegrees. Kudzidza-kwakavakirwa chirongwa kuvaka uye kurodza hunyanzvi hwako munzvimbo dzakakosha dzeAI.
mabhuku okuchikoro
- “Pattern Recognition and Machine Learning” naChristopher M. Bishop. Inopa sumo yakazara yepamberi undergraduates uye vadzidzi vakapedza kudzidza.
- "Kudzidza Kwemuchina: Maonero Anogona Kuitika" naKevin P. Murphy. Iri bhuku rinopa yakadzama sumo kumunda wekudzidza muchina kubva pakuona kwe probabilistic.
Vhura-sosi software
- TensorFlow. Yakagadzirwa neGoogle, iyi iraibhurari ine simba yekuverengera nhamba uye kudzidza muchina.
- Scikit-Dzidza. Chishandiso chiri nyore uye chinoshanda chekuchera data uye kuongororwa kwedata kwakavakirwa paNumPy, SciPy, uye matplotlib. matplotlib iraibhurari inoshandiswa muPython kugadzira static, inopindirana, uye ine animated zviono.
- PyTorch. Raibhurari yakavhurika-sosi yekudzidza muchina kubva kuFacebook, inoshandiswa zvakanyanya kune maapplication akadai seyakasikwa mutauro kugadzirisa.
Community forums
- Simba Rinoputika. Chinhu chakakosha chekushandisa kune vanogadzira uye mainjiniya kubvunza mibvunzo uye kugovana ruzivo.
- Reddit r/MachineLearning. Nharaunda inoshanda yekukurukura ichangoburwa mumakina ekudzidza matekiniki, nhau, uye tsvagiridzo.
- GitHub. Inopa yakakura kuchengetedza mapurojekiti kwaunogona kubatana uye kubatsira kune chaiyo-yepasirese application.
Musiyano pakati pekudzidza muchina uye kudzidza kwakadzama
Mushure mekuongorora akasiyana maturusi uye zviwanikwa zvekudzidza nezve muchina kudzidza, zvakakosha kuti unzwisise mutsauko mukati memunda pachawo. Sezvo isu tinonyura mukati mekuoma kwekudzidza kwemichina uye mashandisiro ayo munzvimbo dzakasiyana siyana, zvinova zvakakosha kusiyanisa pakati pemaitiro emuchina ekudzidza uye yakasarudzika subset yekudzidza kwakadzama. Ose ari maviri akakosha mukuvandudza masisitimu akangwara asi anosiyana zvakanyanya mumashandiro aanoita uye matambudziko aanogadzirisa.
Kunzwisisa kusiyana
General Machine Learning (ML) inoshandisa yakafara spectrum yealgorithms inoshanda pasi pekutungamira kwakananga kwevanhu. Aya maalgorithms akadzidziswa nedata rakanyorwa zvakajeka nenyanzvi, zvinoda kupinza kwevanhu kutsanangura mavara uye maficha. Iwo masisitimu anoshandisa aya akafanotemerwa maitiro kuronga data kana kufanotaura.
For muenzaniso:
- Masefa eemail anorongedza mameseji kuita "spam" kana "asiri-spam" zvikamu achishandisa-anotsanangurwa nemushandisi senge mazwi akakosha kana mukurumbira weanotumira.
Kudzidza Kwakadzika (DL), chikamu chakatariswa chekudzidza kwemuchina, chinoshandisa yakaoma neural network kuongorora dhata re data vakazvimirira. Iyi nzira inokunda pakugadzirisa dhata isina kurongeka senge mifananidzo uye odhiyo, inozivisa maficha akakodzera pasina kuda mirairo yekodhi-yavanhu kana zvikamu zvakafanotsanangurwa.
For muenzaniso:
- Tekinoroji yekuziva manzwi mumidziyo yakaita seSiri uye Google Mubatsiri anogadzira mutauro unotaurwa zvagara, pasina hurongwa hwemanyorerwo echirevo chega chega kana izwi.
Zvishandiso zvinoshanda uye mafambiro emangwana
Kudzidza kwakadzama kunoratidza kunoshanda zvakanyanya mumamiriro ezvinhu umo kudzidza muchina muchina kunganetsa:
- Autonomous mota. Yakadzama yekudzidza algorithms inodudzira data kubva kune akasiyana masensa kuita sarudzo dzekufamba nekukurumidza, sekuziva zvipingamupinyi kana kuronga nzira.
- Nezveutano. DL inovandudza huchokwadi uye nekumhanya kwekuturikira mifananidzo yekurapa senge MRIs, kuvandudza kurongeka kwekuongorora kupfuura nzira dzechinyakare.
Kubatanidzwa uye kufambira mberi muAI
Iko kuwirirana pakati pekudzidza kwemuchina uye kudzidza kwakadzama kuri kushandura maindasitiri akasiyana nekubatanidza kudzidza kwakarongeka, kunoenderana nemutemo pamwe nekuongorora, otomatiki data. Musanganiswa uyu unotarisirwa kufambisa kufambira mberi kwakakosha muAI, kuita kuti masisitimu ave nehungwaru uye anoteerera kune chaiwo-epasirese mamiriro.
Ethical kufunga nezve muchina kudzidza
Sezvo isu tichinyura mukati mekudzidza kwemichina uye kudzidza kwakadzama, zvakakosha kuti titarise maitiro ehutsika anouya neaya matekinoroji. Maitiro ehutsika akakosha pakuvandudza AI uye anokanganisa zvakanyanya mashandisirwo aya matekinoroji nekuonekwa pasirese. Muchikamu chino, tichaongorora zvakakosha nyaya dzetsika dzakakosha pakuvaka akavimbika uye akanaka maAI masisitimu:
Kuvanzika kwedata
Kudzidza kwemichina kunovimba zvakanyanya nehuwandu hukuru hwe data kuti ivandudze uye kuve nemazvo. Kazhinji, iyi data inosanganisira ruzivo rwemunhu, izvo zvinogona kusimudza kunetseka kwekuvanzika. Muenzaniso unozivikanwa ndeyekushandisa kweFacebook data rako pachako kushambadzira kwakanangwa, izvo zvakonzera hurukuro dzakapararira nezvekodzero dzekuvanzika. Izvo zvakakosha kuti unzwisise zvinorehwa nekushandisa data rako pachako uye kugadzira matanho akasimba ekudzivirira kuvanzika kwevanhu.
Kubvumirana uye pachena
Kunzwisisa mashandiro emichina yekudzidza algorithms uye maitiro avanoita sarudzo kwakakosha pakuvaka kuvimba uye kuve nechokwadi chekuzvidavirira. Semuyenzaniso, European Union's General Data Protection Regulation (GDPR) inoda kuti vanhu vave nekodzero yekunzwisisa pfungwa dziri kumashure kwesarudzo dzakaitwa neautomated system dzinovakanganisa. Izvi zvinozivikanwa se'kodzero yekutsanangura'. Izvo zvakakoshawo kuti vanogadzira vawane rutsigiro rwakajeka pakushandisa data remumwe munhu, kunyanya ruzivo rwemunhu. Izvi zvinovimbisa kuti vashandisi vane ruzivo rwakakwana uye vanobvumirana nemashandisirwo ari kuitwa data ravo.
Kusarura uye kururamisira
Rusarura mumamodeli ekudzidza muchina anogona kutungamira mukubatwa zvisina kunaka uye rusarura. Semuyenzaniso, mamwe masisitimu ekucherekedza mifananidzo akaonesa zviso zviso zvemamwe marudzi. Izvi zvinoratidza kuti nei zvakakosha kuti vagadziri vadzivise kusarura muAI masisitimu. Isu tinofanirwa kuve nechokwadi chekuti sarudzo dzekudzidza dzemuchina dzakaringana uye hadziite rusarura kusimudzira kururamisira.
Impact pabasa
Kusimuka kweAI uye otomatiki kuri kugadzirisa basa mumaindasitiri mazhinji. Tekinoroji senge robhoti process automation inotarisirwa kushandura akasiyana mabasa uye zvikamu. Semuyenzaniso, otomatiki mukugadzira anogona kuderedza kudiwa kwehumwe hunyanzvi uye kuderedza kukosha kwebasa revanhu mumabasa ekudzokorora. Future AI nyanzvi dzinofanirwa kufunga nezve idzi shanduko dzehupfumi, kusanganisira mukana wemabasa matsva muminda ine hunyanzvi uye kudiwa kwekudzidzisazve vashandi vanorasikirwa nemabasa nekuda kwe automation.
Kuti uongorore zvakawanda nezve maitiro ehutsika mukusimudzira kweAI, kusanganisira nhaurirano dzakadzama pamusoro peEU's AI Act uye zvazvinoreva pakuvandudza nehunhu, unogona kuverenga zvakawanda muchinyorwa chedu chakazara. pano.
Nekuita zvine chekuita nekuita izvi zvine chekuita nehunhu, iyo nharaunda yekudzidza muchina inogona kusimudzira kusimudzira uye kuita kweAI matekinoroji ayo asiri epamberi kwetekinoroji chete asiwo ane mutoro wemagariro uye zvine hutsika.
Masimba uye zvipimo zvekudzidza muchina
Sezvatinopedzisa kuongorora kwedu kwakadzama kwekudzidza kwemuchina-kubva papfungwa dzayo dzekutanga kusvika kune akasiyana mashandisiro ayo uye nyaya dzetsika dzainosimudza-zvakakosha kufunga nezve kugona kwakakura uye matambudziko makuru eiyi tekinoroji ine simba. Ichi chikamu chekupedzisira chinopfupisa nhaurirano dzedu nekusimbisa masimba akakosha uye matambudziko akakosha anokanganisa mashandisirwo ekudzidza kwemichina munzvimbo dzakasiyana.
Strengths
- Scalability yekuongorora. Kudzidza kweMichina kunokunda kana uchishanda nemaseti makuru, sezvo ichikwanisa kuona otomatiki mapatani uye kuita fungidziro zvakanyanya kupfuura nzira dzechinyakare. Izvi zvinonyanya kubatsira munzvimbo dzakadai sehombe data analytics uye injini dzekutsvaga.
- Kuchinjika. ML algorithms yakasikwa kuti irambe ichinatsiridza huchokwadi nekudzidza kubva kune nyowani data, iyo yakakosha mumasetin'i ane simba senge akasarudzika kurudziro yemukati apo zvido zvemushandisi zvinoshanduka nekufamba kwenguva.
- kushandisa michina. ML inonatsiridza zvakanyanya kukurumidza kuita sarudzo uye inoderedza kukanganisa kwevanhu, zvichiita kuti zvive zvakakosha munzvimbo dzakaita sekutengesa kwemari uye kuongororwa kwehutano uko kurongeka kwakakosha.
- kunyatsoshanda. Nekushandisa zviwanikwa zvakanyanya, ML inobatsira kuvandudza kugona kwese mumabasa akasiyana. Izvi zvinosanganisira zvirinani manejimendi emagetsi mumasisitimu anozivikanwa seakangwara grids, ayo anobatsira kufanotaura nguva dzakabatikana dzekushandisa simba uye kuderedza marara nekugadzirisa kugovera zvinoenderana.
Nokuremara
- Overfitting. Izvi zvinoitika kana modhi yanyanya kuomarara, ichitora ruzha panzvimbo yeyakadzika data pateni, iyo inogona kushata kuita kwayo pamaseti matsva.
- Transparency. Iyo "bhokisi dema" chimiro chemhando zhinji dzeML chinopa matambudziko muzvikamu zvakaita sehutano uye kubhengi uko kunodiwa nzira dzakajeka dzekuongorora. Kusabuda pachena uku kunogona kukanganisa kuvimba uye kukanganisa kuzvidavirira.
- Rusarura. Kana kurerekera mudhata rekudzidzisa kukasagadziriswa, zvinogona kutungamirira kumigumisiro isina kunaka muzvisarudzo zveotomatiki, zvinonyanya kunetsa munzvimbo dzakadai sekubhadhara uye kukwereta mvumo.
- Scalability yekuita. Kunyange zvazvo vachibata magwaro akakura zvakanaka, kuwedzera modhi yeML kune makuru kana mamwe anoshanda maapplication anogona kuunza matambudziko makuru nekuda kwekukwira kwekombuta zvinodiwa uye mutengo, izvo zvinogona kusashanda kumasangano ese.
Nepo kudzidza kwemichina kuchipa akawanda mabhenefiti anogona kushandura maindasitiri, inosanganawo nemipimo yakakura iyo inogona kukanganisa kushanda kwayo kwakafara. Tichitarisa kumberi, nharaunda yekudzidza yemuchina inofanirwa kushandisa masimba aya uku ichikunda zvisingakwanisi nemhinduro dzekugadzira uye maitiro ehutsika. Nekuchengetedza uku kutarisisa kwakaringana, tinogona kuve nechokwadi chekuti kudzidza kwemichina kunowana kugona kwayo kuzere setekinoroji yakakosha, kufambira mberi zvine hungwaru uye zvakabatana.
mhedziso
| Kudzidza kwemichina kuri kumberi kweshanduko yetekinoroji, ichipa hunyanzvi hutsva uye hutsva mumaindasitiri mazhinji. Rwendo urwu rwakaratidza kukosha kwakakosha kwekuenzanisa mukana weunyowani nemutoro wehutsika kuti uve nechokwadi chekubatsira kune ese nzanga. Sezvatinotarisa kune ramangwana, dambudziko redu rekubatana ndere kutungamira budiriro iyi nehungwaru, kuve nechokwadi kuti kufambira mberi kwetekinoroji kunowanikwa zvine hungwaru uye zvakabatana. Wakagadzirira here kujoina rwendo urwu uye kubatsira kuvhura iyo yakazara mukana wekudzidza muchina nenzira inochengeta kuzvipira kwedu mukurongeka nekufambira mberi? Ngativandudzei zvine hungwaru, tichiona kuti nhanho yega yega yatinotora inotsigira kufambira mberi kwakanaka uye kwetsika muhunyanzvi. |
