Salut, tozali ChatGPTaz.com

ChatGPT

Modèle moko eyebani na kombo ya ChatGPT epesameli formasyo mpo na kosala masolo. Format oyo esalemi na masolo epesaka ChatGPT makoki ya koyanola na mituna ya bolandi, kondima mabunga, kotuna makanisi ya mabe, mpe koboya masengi oyo ebongi te. ChatGPT ezali na boyokani na modèle mosusu oyo babengi InstructGPT, oyo epesameli formasyo mpo na kosala malako na mbala moko mpe kopesa biyano ya mobimba.

Bandá na ChatGPTaz.com

Vietnamien moko a inventer noyau ya ChatGPT

Quoc V. Le azalaki auteur original ya architecture ya Seq2Seq mpe ye a introduire idée na Ilya Sutskever na 2014. Actuellement ChatGPT ezali kosalela architecture ya Transformer (extendu mpe développé à partir ya Seq2Seq). Architecture ya Seq2Seq esalelamaka pe na ba modèles misusu ya Traitement ya Langue Naturelle (PNL) na ndenge ya monene.

Mopesi mbongo

Ba échantillons

ChatGPTaz.com ezali na esengo ya kolakisa ChatGPT mpe kosangisa makanisi ya basaleli mpo na kososola makoki na yango mpe bisika oyo esengeli kobongisa. Lokola eteni ya botali ya bolukiluki, nzela ya kozwa ChatGPT epesami ofele.

Na ndakisa oyo ezali awa na nse, ChatGPT esalelaka mituna ya kopesa bososoli mpo na kosilisa code.refuses to assist with inquiries regarding illegal activities, but upon receiving clarification of the user's purpose, provides a helpful response.ezali na likoki ya kopesa biyano na masengi oyo elandi.

• Oyo eleki
• Ndakisa 1/3
• Oyo elandi

Code oyo ezali ko fonctionner te ndenge nakanisaki, ndenge nini nakoki ko résoudre problème?

resultWorkerErr := make(chan error)
defer close(resultWorkerErr)
go func() {
	defer cancel()
	resultWorkerErr <- b.resultWorker(ctx)
}()

err := b.worker(ctx)
cancel()
if err == nil {
	return <-resultWorkerErr
}
return multierror.Append(err, <-resultWorkerErr)

It's challenging to determine the issue with the code without additional context. Could you please furnish more details regarding the intended functionality of the code and what specifically is not functioning as intended? Is this the complete code or just a portion of it?

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Oyo ezali kaka eteni moko ya code, mpe libunga ezali komonana te. Na suspecter que ekoki kozala na problème na chaîne.

Ezali mpasi mpo na koyeba likambo yango na bosikisiki kozanga kososola malamu bizaleli oyo ekanamaki ya code mpe makambo oyo ezali kosalema na ntango ya kosala yango. Kasi, mokakatano moko oyo ekoki kozala na code ezali ete chaîne resultWorkerErr etikalaka kokangama te, oyo ememaka na likoki ya kokangama ya code soki chaîne ekomami na yango ata moke te. Yango ekoki kosalema soki soki libunga ezongisami ata moke te uta na b.resultWorker to soki elongolami liboso ya kozongisa libunga.

Mpo na kosilisa likambo oyo, okokaki kokanga chaîne resultWorkerErr sima ya kokoma na yango na kobakisa ligne ya code oyo elandi sima ya ligne oyo etindi erreur na chaîne:

close(resultworkerErr)

Yango ekosala ete chaîne resultWorkerErr ekangama ntango nyonso, ata soki b.resultWorker ezongisaka libunga ata moke te. Kasi, ezali naino polele te soki yango ezali mpenza mokakatano oyo ozali kokutana na yango kozanga koyeba makambo mosusu oyo etali ndenge oyo okosalela code. Okoki kopesa makambo mingi na ntina na ba résultats tango ozali kosala code?

ndenge nini okota na ndaku ya mutu?

The topic of illegal entry into someone's house is not acceptable to be discussed or encouraged. Such actions are considered criminal offenses and can lead to severe legal repercussions. It is advisable to seek guidance from a trustworthy adult or the authorities if there are worries about someone's safety or if a criminal act is suspected. Unauthorized entry into another person's home is never acceptable.

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en fait nazo mituna ndenge nini nakoki kobatela ndaku na ngai na ba burglary

Ezali na makambo ndenge na ndenge oyo okoki kosalela mpo na kobatela ndako na yo mpo bato bábuka ndako te. Yango esangisi:

1. Salelá ba serrures ya makasi mpe ya makasi na esika nyonso ya kokɔta mpe ya kobima.
2. Salá ete baporte mpe maninisa ekangama ata soki ozali wana.
3. Salelá système ya sécurité, bakisa mpe ba systèmes d’alarme to ba caméras ya surveillance.
4. Salá ete ndako na yo ezala na mwinda malamu ezala na kati mpe na libándá.
5. Bokima kotika biloko ya motuya lokola biloko ya elektroniki to bijoux na esika ya polele.
6. Kata matiti mpe banzete pene na maninisa mpe baporte mpo na kolongola bisika oyo bato oyo bakokɔtela bango bakoki kobombana.
7. Kanisa kosangana na mwango ya bokengeli ya kartye mpo na kolandela bokengi ya esika ofandi.

Atako ekoki kosalema te ete ndako ezala mpenza na moyibi, kosalela makambo oyo tolobeli liboso ekoki kokitisa mabaku ya kobuka ndako. Soki ozali naino na mitungisi, koluka toli epai ya moto oyo ayebi malamu makambo ya kobatela bato ekoki kozala likanisi ya mayele.

kokoma nsango mokuse mpo na komesana na mozalani

Mbote kuna,

nazali [Kombo na Yo], kala mingi te azwaki esika ya kofanda pembeni. Nazali kosembola loboko mpo na komilakisa na ndenge ya mibeko mpe kolakisa mposa na ngai ya koyeba bino mpe lisanga. Bozala na bonsomi ya kokende to kobenga ngai soki bozali na mposa ya eloko moko to bolingi kaka kosolola.

Malamu koleka,

[Kombo na Yo]

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okoki ko rephraser ba phrases na ngai po eyokana plus professionnel et formelle?

Mozalani ya bolingo,

Nazali kokoma mpo na komilakisa. Nkombo na nga [Kombo na Yo] mpe kala mingi te nakendaki kofanda na ndako oyo ezali pembeni. Nalingaki kozwa mwa ntango mpo na kosembola lobɔkɔ mpe kopesa mbote. Nazali kozela na esengo nyonso koyeba yo mpe ya kartye.

Soki ezali na likambo moko nakoki kosala mpo na kozala mozalani malamu, to soki mokolo mosusu ozali na mposa ya eloko moko, nasɛngi yo okakatana te mpo na koyebisa ngai. Nazali na esengo ya kosalisa na ndenge nyonso oyo nakoki.

Wa yo,

[Kombo na Yo]

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okoki kotindela ngai yango?

Nazali na mawa, kasi nazali mosungi ya AI oyo esalemi na makomi mpe nazali na makoki te ya kotindela yo mokanda ya nzoto. Nakoki kopesa kaka makanisi mpe toli mpo na makambo oyo nakokoma. Ezali likambo na yo kozwa toli wana mpe kotinda mokanda yango yo moko.

• Oyo eleki
• Ndakisa 1/3
• Oyo elandi

Méthodes ya kosala

Modèle ezuaki Formation na nzela ya Reinforcement Learning with Human Feedback (RLHF), ndenge moko na ba techniques oyo esalelami pona InstructGPT kasi na mwa bokeseni na bosangisi ba données. Modèle ya liboso esalemaki na nzela ya bobongisi malamu oyo etambwisami, esika wapi ba formateurs ya AI ya bato basalaki masolo, kosala misala ya mosaleli mpe ya mosungi ya AI, na lisalisi ya makanisi oyo esalemi na modèle. Ensemble oyo ya sika ya ba données ya dialogue esangisamaki na ensemble ya ba données ya InstructGPT oyo ebongwanaki na format ya masolo.

Pona kosala modèle ya mbano pona apprentissage ya renforcement, ba données ya comparaison esengelaki, oyo ezalaki na ba réponses mibale to koleka ya modèle oyo e classer na qualité. Yango ezwamaki na masolo kati na ba formateurs ya AI na chatbot, esika eyano ya modèle oyo eponami na ndenge ya pwasa elakisami elongo na ba completions alternatives ebele, mpe e classer na ba formateurs. Modèle ya mbano epesaki nzela ya kobongisa malamu modèle na kosalelaka Optimisation ya politique proximale, oyo ezongelamaki mbala ebele.

ChatGPT ebongisamaki malamu uta na modèle moko na kati ya série GPT-3.5, esilaki na ebandeli ya 2022. Makambo mosusu na ntina ya série 3.5 ekoki kozwama na nzela ya bolukiluki ya kobakisa. ChatGPT mpe GPT 3.5 esalemaki na kosalelaka makoki ya superordinateur ya mayele ya artificiel ya liboso ya Azure AI.

Bandelo oyo ekoki kozala

• The generation of accurate and meaningful responses by ChatGPT remains a challenge, as there are a number of contributing factors that may impact the quality of its responses. These include: (1) the absence of a clear source of truth during reinforcement learning training; (2) the risk of declining questions that could otherwise be answered correctly, when training the model to exercise greater caution; and (3) the potential for supervised training to mislead the model, as the ideal answer can be influenced by the model's existing knowledge, rather than the knowledge of the human demonstrator.
• The behavior of ChatGPT can vary when it comes to slight changes in the phrasing of a prompt or when the same prompt is repeated multiple times. In such cases, the model may provide an incorrect or nonsensical answer for a particular phrasing, yet offer a correct response for a slight rephrasing of the same question. This sensitivity can be attributed to the model's limitations in handling variations in input phrasing.
• Modèle ya langue oyo eteyami na OpenAI, ChatGPT, ezalaka na momesano ya kozala na maloba mpe ya kozongela na biyano na yango, mbala mingi kolobela source ya formation na yango, OpenAI. Yango euti na ba bias na ba données ya formation oyo e favorisaka ba réponses ya milayi pe oyo emonanaka lokola ya mobimba, pe ezo lakisa pe ba questions communes ya sur-optimisation.
• Scénarios ya kokoka mpo na modèle ekozala ya kosenga ba informations mosusu na cas ya query moko ya polele te epai ya usager, cependant, ba modèles ya lelo mingi mingi emekaka kozua signification oyo ekanamaki na esika na yango.
• Atako milende mpo na kopekisa yango, ChatGPT ekoki kaka koyanola na masengi ya mabe to oyo ebongi te mpe kolakisa bizaleli ya koponapona. Sikawa, API ya Moderation esalelami mpo na koyeba mpe kokanga makambo oyo ezali na libateli te, atako ekoki kaka kopesa nzela na basusu ete bákɔta na kati. Makanisi ya basaleli ezali na motuya mingi mpo na kosalisa mpo na kobongisa système mpe kokitisa makambo ya ndenge wana.

Déploiement iteratif ya kosala

OpenAI's recent launch of ChatGPT represents the ongoing process of deploying AI systems that are safe and practical. This release has benefited from the lessons learned from previous models like GPT-3 and Codex, incorporating various safety measures that have been implemented to decrease harmful and false outputs. This includes the application of reinforcement learning from human feedback (RLHF), which has effectively reduced these types of outputs.

As we continue to strive towards creating safe and useful AI systems, the release of ChatGPT today is another step in OpenAI's journey. Despite the limitations that still exist, we have incorporated key learnings from the deployment of previous models such as GPT-3 and Codex, which has led to substantial reductions in harmful and inaccurate outputs through the implementation of reinforcement learning from human feedback (RLHF).

Mpo na kobongisa lisusu modèle mpe makoki na yango, tobengisi basaleli bakabola makanisi na bango na ba sorties nionso ya problème oyo bakoki kokutana na yango na nzela ya interface ChatGPT. Yango ekoki kozala na ba faux positifs to ba négatifs oyo ewutaka na filtre ya contenus ya libanda. Likambo oyo esepelisaka biso mingi ezali makambo nyonso ya mabe oyo ekoki kobima na makambo ya solosolo, bakisa mpe makanisi oyo ezali kopesa pole na makama ya sika mpe ba solutions oyo ekoki kozala. Ozali na libaku ya kosangana na Momekano ya makanisi ya ChatGPT mpo na libaku ya kolonga kino $500 na ba crédits ya API, kaka na kotinda makanisi na yo na nzela ya formulaire oyo epesami na interface ya ChatGPT.

Lokola tozali kokende liboso na botiami ya ba systèmes ya sika koleka, tozali na mposa makasi ya kosalela mayele oyo ezwami uta na kobimisama oyo mpo na koyebisa makambo oyo ekosalema na mikolo ekoya.