Uhlolojikelele
I-Covariant yinkampani ye-robotics-AI eyakha 'amamodeli esisekelo' amakhulu amarobhothi, ivumela izingalo zamarobhothi zibone, zicabange ngazo, futhi zikhethe izinto ezingakaze zihlangane nazo ngaphambili. Ibalulekile ngoba ilethe iresiphi yemodeli yolimi yokuqeqeshwa kwangaphambili okubanzi ekuziphatheni ngokomzimba ezindaweni zokugcina impahla.
I-Covariant Robotic Foundation Models iqondwa kangcono kumongo wesu, ukufinyelela okuyimodeli, izinqumo zeplathifomu, nobudlelwano be-ecosystem.
I-Deep Dive
Yasungulwa ngo-2017 abacwaningi be-AI okuhlanganisa u-Pieter Abbeel, u-Peter Chen, no-Rocky Duan base-UC Berkeley kanye nezimpande OpenAI, i-Covariant yakha i-Covariant Brain, isofthiwe ye-AI enika amandla izingalo zerobhothi ukuze ziqoke futhi zihlungwe. Umkhiqizo wayo ovelele, i-RFM-1 (Robotics Foundation Model 1), owethulwe ngo-2024, waqeqeshwa ngenani elikhulu ledatha yokukhetha yomhlaba wangempela kanye nombhalo nezithombe ukuze amarobhothi akwazi ukuphatha imigqomo engcolile yezinto angazazi futhi aphendule ngisho neziyalezo zolimi lwemvelo. Kunokuhlela into ngayinye, isistimu ikhiqiza ngokujwayelekile kusukela kokuhlangenwe nakho njengemodeli yolimi enkulu ehlanganisa umbhalo wonke. Ngo-2024 ingxenye enkulu yeqembu lika-Covariant, okuhlanganisa nabasunguli bayo, yaqashwa yi-Amazon esivumelwaneni selayisense nethalenta, ikhombisa ukuthi amamodeli esisekelo samarobhothi asephenduke amaqhinga kangakanani.
I-Technical Insight
I-RFM-1 iyi-multimodal transformer eqeqeshwe embhalweni, izithombe, ividiyo, ukufundwa kwenzwa yerobhothi, nezenzo zemoto, iziphatha njengamathokheni ngokulandelana okukodwa. Ngokubikezela ithokheni elandelayo kuzo zonke lezi zindlela, ifunda imbangela kanye nomphumela obonakalayo, ngakho-ke ingatshelwa ngolimi kanye nesizathu sokuthi ukubamba kuzokwenzani ngaphambi kokuthatha isinyathelo. Lokhu kuvumela imodeli eyodwa ukuthi ilawule amarobhothi ahlukene futhi ibambe izinto zenoveli ngaphandle kobunjiniyela bento ngayinye, ifanekisela ukuthi ukuqeqeshwa kwangaphambili okubanzi kukhiqize kangakanani ikhono lolimi elijwayelekile.
I-Mastering Covarian Robotic Foundation Models
I-Covariant yinkampani ye-robotics-AI eyakha 'amamodeli esisekelo' amakhulu amarobhothi, ivumela izingalo zamarobhothi zibone, zicabange ngazo, futhi zikhethe izinto ezingakaze zihlangane nazo ngaphambili. Ibalulekile ngoba ilethe iresiphi yemodeli yolimi yokuqeqeshwa kwangaphambili okubanzi ekuziphatheni ngokomzimba ezindaweni zokugcina impahla. I-Covariant Robotic Foundation Models iqondwa kangcono kumongo wesu, ukufinyelela okuyimodeli, izinqumo zeplathifomu, nobudlelwano be-ecosystem. Ukuze wakhe ukuqonda okujulile, phatha amamodeli we-Covariant Robotic Foundation njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela oyifunayo, ucacise ukucabanga, futhi uhlukanise lokho uhlelo olungakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa ama-Covariant Robotic Foundation Models ahlola isu lomthengisi, ukwethembeka kwemephu yomgwaqo, kanye nengozi yokukhiya ngaphambi kokuzibophezela. 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.
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo. Ngesikhathi esifanayo, izimemezelo Zokuqalisa zingase zidlule ukuzinza ekusebenzeni kwangempela kokukhiqiza. 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
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo.
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Imigomo yezohwebo nezinketho zokuthunyelwa zithinta izindleko zesikhathi eside nobungozi.
Imigomo yezohwebo nezinketho zokuthunyelwa zithinta izindleko zesikhathi eside nobungozi. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Izinxephezelo zenkampani zibumba okuzenzakalelayo kwemikhiqizo, ukuma kokuphepha, nokuvuleleka.
Izinxephezelo zenkampani zibumba okuzenzakalelayo kwemikhiqizo, ukuma kokuphepha, nokuvuleleka. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ukuqaliswa Komhlaba Wangempela
Ukukhetha izinto ezihlukahlukene, ezingakaze zibonwe ngaphambili emigqonyeni yokugcina impahla egcwele ama-oda we-e-commerce
Ukuhlunga amaphasela ngendawo okuyiwa kuyo emigqeni yokungeniswa kwempahla ngaphandle kokuhlelwa kwento ngayinye
Ukusebenzisa ukwaziswa kolimi lwemvelo ukutshela ingalo yerobhothi ukuthi ibambe ini noma iphathe kanjani into
Inika amandla amarobhothi e-warehouse yenkampani yangaphandle ngeplathifomu yesoftware ye-Covariant Brain
Amaphethini Okusebenzisa
Amamodeli we-Covariant Robotic Foundation asebenza
Ukukhetha izinto ezihlukahlukene, ezingakaze zibonwe ngaphambili emigqonyeni yokugcina impahla egcwele ama-oda we-e-commerce.
Ukucosha izinto ezihlukahlukene, ezingakaze zibonwe ngaphambili emigqonyeni yokugcina impahla ephithizelayo yama-oda e-e-commerce Amaqembu ngokuvamile athola imiphumela engcono lapho echaza imingcele yekhwalithi ngaphambili, egcina indlela yokukhuphuka kwabantu yamacala asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Amamodeli we-Covariant Robotic Foundation asebenza
Ukuhlunga amaphasela ngendawo okuyiwa kuyo emigqeni yokungeniswa kwempahla ngaphandle kokuhlelwa kwento ngayinye.
Ukuhlunga amaphasela ngendawo okuyiwa kuyo emigqeni yokungeniswa kwempahla ngaphandle kokuhlelwa kwento ngayinye Amathimba ngokuvamile athola imiphumela engcono uma echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka yabantu yamakesi asemaphethelweni, futhi alandelela kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Amamodeli we-Covariant Robotic Foundation asebenza
Ukusebenzisa ukwaziswa kolimi lwemvelo ukutshela ingalo yerobhothi ukuthi ibambe ini noma iphathe kanjani into.
Ukusebenzisa ukwaziswa kolimi lwemvelo ukutshela ingalo yerobhothi ukuthi ibambe ini noma iphathwa kanjani into Amaqembu ngokuvamile athola imiphumela engcono uma echaza izilinganiso zekhwalithi ngaphambili, egcina indlela yokukhuphuka yabantu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Amamodeli we-Covariant Robotic Foundation asebenza
Inika amandla amarobhothi e-warehouse yenkampani yangaphandle ngeplathifomu yesoftware ye-Covariant Brain.
Ukunika amandla amarobhothi e-warehouse yenkampani yangaphandle ngokusebenzisa ipulatifomu yesofthiwe ye-Covariant Brain Amaqembu ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, egcina indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Izingozi & Guardrails
Izimemezelo zokwethula zingase zeqe ukuzinza ekugelezeni komsebenzi wangempela wokukhiqiza.
Izintengo ze-API noma izinguquko zenqubomgomo zingaphula ukucabanga ngobusuku obubodwa.
Ukuncika komthengisi oyedwa kukhulisa izindleko zokukhiya nokufuduka.
Ukuqalisa Umhlahlandlela
Linganisa abahlinzeki usebenzisa eyakho imisebenzi namasethi edatha.
Linganisa abahlinzeki usebenzisa eyakho imisebenzi namasethi edatha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Buyekeza ubumfihlo, ukuphepha, nemibandela yomthetho ngaphambi kokuhlanganiswa.
Buyekeza ubumfihlo, ukuphepha, nemibandela yomthetho ngaphambi kokuhlanganiswa. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Gcina uhlelo lokubuyela emuva kuwo wonke amamodeli noma abathengisi.
Gcina uhlelo lokubuyela emuva kuwo wonke amamodeli noma abathengisi. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Gada amanothi okukhululwa ukuze izinguquko zemephu yomgwaqo zingamangazi amaqembu.
Gada amanothi okukhululwa ukuze izinguquko zemephu yomgwaqo zingamangazi amaqembu. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.