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Iwọn AI
Scale AI provides data services used in training and evaluating AI systems, including annotation, human preference feedback, and model testing.
Gbọ si itọsọna yiiAI-ṣe afihan · ~1 min
Lori iwe yi1 iseju kika
Akopọ
These services supply data and evaluation workflows; their value depends on task design, data quality, and coverage.
Jin Dive
Scale AI was founded in 2016 and initially focused on data labeling for applications such as autonomous driving. Its Data Engine and Generative AI Data Engine describe services for data annotation, curation, human feedback, evaluation, and safety testing. Scale identifies Outlier as a specialized contributor platform. RLHF can use human comparisons or rankings of model responses to construct preference signals. Red-teaming probes a model or system with adversarial or misuse scenarios. These services support data and evaluation work but do not guarantee model quality, safety, or performance beyond the tested tasks.
Real-World imuse
Label objects in vehicle images for a perception dataset.
Collect preference comparisons between candidate language-model responses.
Use subject-matter experts to write or evaluate examples for a specified task.
Run red-team tests against a model and document the vulnerabilities found.
Ojo iwaju ti Asekale AI
The company’s data and evaluation offerings change over time. Teams should verify the exact service, workforce, data handling, and quality-control process for a project and treat evaluation coverage as bounded by its test design.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Ilana olutaja. Awọn maapu opopona olutaja ni ipa kini awọn ẹya ti ẹgbẹ rẹ le kọ ni atẹle.
- Iye owo ati isuna. Awọn ofin iṣowo ati awọn aṣayan imuṣiṣẹ ni ipa lori idiyele igba pipẹ ati eewu.
- Ewu ati ailewu. Awọn imoriya ile-iṣẹ ṣe apẹrẹ awọn abawọn ọja, iduro ailewu, ati ṣiṣi.
Ṣọra fun
- Awọn ikede ifilọlẹ le ju iduroṣinṣin lọ ni awọn iṣan-iṣẹ iṣelọpọ gidi.
- Ifowoleri API tabi awọn iyipada eto imulo le fọ awọn arosinu ni alẹ.
- Igbẹkẹle olutaja ẹyọkan ṣe alekun titiipa-inu ati awọn idiyele ijira.
Next awọn igbesẹ
- Ṣe ayẹwo awọn olupese nipa lilo awọn iṣẹ ṣiṣe tirẹ ati awọn ipilẹ data.
- Ṣe atunyẹwo asiri, aabo, ati awọn ofin ofin ṣaaju iṣọpọ.
- Ṣetọju eto ipadabọ kọja awọn awoṣe tabi awọn olutaja.
- Bojuto awọn akọsilẹ itusilẹ nitoribẹẹ awọn iyipada maapu oju-ọna ma ṣe iyalẹnu awọn ẹgbẹ.
Tesiwaju Ṣiṣawari
Ìwé ìròyìn ọfẹ
Gba ìkànìyàn AI ojoojúmọ́
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Take the Scale AI quiz
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Awọn ibeere ti a beere nigbagbogbo
Kini Asekale AI?
Scale AI provides data services used in training and evaluating AI systems, including annotation, human preference feedback, and model testing. These services supply data and evaluation workflows; their value depends on task design, data quality, and coverage.
Iṣoro pataki wo ni Asekale AI ni akọkọ yanju fun awọn olupilẹṣẹ AI?
Iṣowo AI ti iwọn ti n ṣe agbejade agbara-didara ti aami-giga ati data curated, nitori awọn awoṣe dara bi data wọn nikan.
Iru data wo ni Scale AI dojukọ lori isamisi nigbati o bẹrẹ akọkọ?
Iwọn bẹrẹ nipasẹ isamisi awọn aworan ati data sensọ fun awọn ọkọ ayọkẹlẹ adase, bii awọn ẹlẹsẹ ati awọn ọkọ ayọkẹlẹ.
Kini RLHF, Iwọn Iṣẹ kan pese fun LLMs, duro fun?
RLHF jẹ Ẹkọ Imudaniloju lati Idahun Eniyan, nibiti eniyan ṣe ipo awọn abajade lati ṣe deede awoṣe kan.
Why can expert-generated training examples help improve a model?
Expert examples and preference feedback can target skills or evaluation needs. Their value depends on data quality, coverage, and the model-training objective; they do not guarantee general capability.
Which platform does Scale AI identify as part of its specialized GenAI contributor ecosystem?
Scale describes Outlier as part of its product and contributor ecosystem for generative-AI work; services and contributor availability vary.
Tesiwaju kikọ
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