ऑपरेशंस रिसर्च विश्लेषक
कंप्यूटर और गणितAI एक्सपोजर
- डेटा स्रोत: BLSप्रकाशन: '26.08
बहुत उच्च· सापेक्ष
कमचार सापेक्ष श्रेणियाँबहुत उच्चव्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
चार सापेक्ष बैंड (कम / मध्यम / उच्च / बहुत उच्च)
BLS रोज़गार पूर्वानुमान तालिका के 831 विस्तृत व्यवसायों के आधार पर। मान NEM (नेशनल एम्प्लॉयमेंट मैट्रिक्स) कोड के स्तर पर दिया जाता है, इसलिए एक ही NEM कोड वाले व्यवसायों को वही बैंड मिलता है
- डेटा स्रोत: Anthropicप्रकाशन: '26.03
0.429
0.000यहाँ दिए गए मानों की सीमा0.745मापक्रम, आधार और स्रोत
- डेटा स्रोत: ILOप्रकाशन: '25
0.56
0.09यहाँ दिए गए मानों की सीमा0.70व्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
जेनरेटिव AI एक्सपोजर सूचकांक, प्रकाशित रूप में 0–1
ISCO-08 यूनिट समूह — समान कोड वाले सभी व्यवसायों को यही मान मिलता है
यह इस साइट की गणना है, ILO द्वारा प्रकाशित आँकड़ा नहीं: इस साइट द्वारा ILO डेटासेट से जोड़े गए 1,012 व्यवसायों में से 8% इस मान के बराबर या उससे अधिक हैं।
यह स्रोत किस प्रकार का आँकड़ा प्रकाशित करता है
BLS की श्रेणी सापेक्ष रैंक है, निरपेक्ष स्तर नहीं, और यह प्रथम-हस्त माप भी नहीं है: यह कई प्रकाशित अध्ययनों में व्यवसाय की पर्सेंटाइल रैंकों को चार बैंडों में बांटती है। यह रोज़गार या वेतन का पूर्वानुमान नहीं है, न अपनाए जाने की संभावना, और यह स्वचालन और संवर्धन में अंतर नहीं करती।
Task-level exposure
केवल एक्सपोज़र वाले कार्य| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Observe the current system in operation, and gather and analyze information about each of the component problems, using a variety of sources.15-2031 | 0.090026.5 | 0.330033.0 |
Study and analyze information about alternative courses of action to determine which plan will offer the best outcomes.15-2031 | 0.090026.5 | 0.160016.0 |
Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.15-2031 | 0.050014.7 | 0.04004.0 |
Analyze information obtained from management to conceptualize and define operational problems.15-2031 | 0.040011.8 | 0.200020.0 |
Break systems into their components, assign numerical values to each component, and examine the mathematical relationships between them.15-2031 | 0.040011.8 | 0.05005.0 |
Define data requirements, and gather and validate information, applying judgment and statistical tests.15-2031 | 0.01002.9 | 0.200020.0 |
Perform validation and testing of models to ensure adequacy, and reformulate models, as necessary.15-2031 | 0.01002.9 | 0.01001.0 |
Specify manipulative or computational methods to be applied to models.15-2031 | 0.01002.9 | 0.01001.0 |
Prepare management reports defining and evaluating problems and recommending solutions.15-2031 | 0.00000.0 | 0.00000.0 |
Develop and apply time and cost networks to plan, control, and review large projects.15-2031 | 0.00000.0 | —0 |
Develop business methods and procedures, including accounting systems, file systems, office systems, logistics systems, and production schedules.15-2031 | 0.00000.0 | —0 |
Present the results of mathematical modeling and data analysis to management or other end users.15-2031 | 0.00000.0 | —0 |
Collaborate with others in the organization to ensure successful implementation of chosen problem solutions. | —0 | 0.00000.0 |
| Not observed on any surface — 4 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Collaborate with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives. | —0 | —0 |
Design, conduct, and evaluate experimental operational models in cases where models cannot be developed from existing data. | —0 | —0 |
Educate staff in the use of mathematical models. | —0 | —0 |
Review research literature. | —0 | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.O*NET Task ID 7377 | 1.0 |
Break systems into their components, assign numerical values to each component, and examine the mathematical relationships between them.O*NET Task ID 7385 | 1.0 |
Specify manipulative or computational methods to be applied to models.O*NET Task ID 7386 | 1.0 |
Design, conduct, and evaluate experimental operational models in cases where models cannot be developed from existing data.O*NET Task ID 7388 | 1.0 |
Collaborate with others in the organization to ensure successful implementation of chosen problem solutions.O*NET Task ID 7378 | 0.5 |
Analyze information obtained from management to conceptualize and define operational problems.O*NET Task ID 7379 | 0.5 |
Perform validation and testing of models to ensure adequacy, and reformulate models, as necessary.O*NET Task ID 7380 | 0.5 |
Collaborate with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives.O*NET Task ID 7381 | 0.5 |
Define data requirements, and gather and validate information, applying judgment and statistical tests.O*NET Task ID 7382 | 0.5 |
Study and analyze information about alternative courses of action to determine which plan will offer the best outcomes.O*NET Task ID 7383 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
Occupation information
इस व्यवसाय से जुड़े हालिया बदलाव
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[स्रोत: AI Changing Work Blog]