कंप्यूटर विज़न इंजीनियर
कंप्यूटर और गणितAI एक्सपोजर
- डेटा स्रोत: BLSप्रकाशन: 2026-08
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: 2026-03
0.461
0.000यहाँ दिए गए मानों की सीमा0.745मापक्रम, आधार और स्रोत
- डेटा स्रोत: ILOप्रकाशन: 2025
0.55
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 12% 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 % |
|---|---|---|
Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.15-2051 | 0.360026.7 | 0.06003.7 |
Generate standard or custom reports summarizing business, financial, or economic data for review by executives, managers, clients, and other stakeholders.15-2051 | 0.180013.3 | 0.330020.1 |
Maintain or update business intelligence tools, databases, dashboards, systems, or methods.15-2051 | 0.150011.1 | 0.230014.0 |
Write new functions or applications in programming languages to conduct analyses.15-2051 | 0.12008.9 | 0.10006.1 |
Prepare data analysis listings and activity, performance, or progress reports.15-2051 | 0.07005.2 | 0.14008.5 |
Synthesize current business intelligence or trend data to support recommendations for action.15-2051 | 0.06004.4 | 0.15009.1 |
Prepare appropriate formatting to data sets as requested.15-2051 | 0.06004.4 | 0.15009.1 |
Provide technical support for existing reports, dashboards, or other tools.15-2051 | 0.05003.7 | 0.02001.2 |
Evaluate processes and technologies, and suggest revisions to increase productivity and efficiency.15-2051 | 0.04003.0 | 0.01000.6 |
Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.15-2051 | 0.04003.0 | 0.00000.0 |
Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.15-2051 | 0.00000.0 | 0.00000.0 |
Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.15-2051 | 0.00000.0 | 0.00000.0 |
Maintain library of model documents, templates, or other reusable knowledge assets.15-2051 | 0.00000.0 | —0 |
Train staff on technical procedures or software program usage.15-2051 | 0.00000.0 | —0 |
Disseminate information regarding tools, reports, or metadata enhancements. | —0 | 0.00000.0 |
Develop technical specifications for data management programming and communicate needs to information technology staff. | —0 | 0.00000.0 |
Develop project-specific data management plans that address areas such as coding, reporting, or transfer of data, database locks, and work flow processes. | —0 | 0.00000.0 |
| Not observed on any surface — 19 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. | ||
Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use. | —0 | —0 |
Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods. | —0 | —0 |
Design surveys, opinion polls, or other instruments to collect data. | —0 | —0 |
Identify business problems or management objectives that can be addressed through data analysis. | —0 | —0 |
Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies. | —0 | —0 |
Communicate with customers, competitors, suppliers, professional organizations, or others to stay abreast of industry or business trends. | —0 | —0 |
Manage timely flow of business intelligence information to users. | —0 | —0 |
Document specifications for business intelligence or information technology reports, dashboards, or other outputs. | —0 | —0 |
Conduct or coordinate tests to ensure that intelligence is consistent with defined needs. | —0 | —0 |
Analyze technology trends to identify markets for future product development or to improve sales of existing products. | —0 | —0 |
Read technical literature and participate in continuing education or professional associations to maintain awareness of current database technology and best practices. | —0 | —0 |
Contribute to the compilation, organization, and production of protocols, clinical study reports, regulatory submissions, or other controlled documentation. | —0 | —0 |
Track the flow of work forms, including in-house data flow or electronic forms transfer. | —0 | —0 |
Supervise the work of data management project staff. | —0 | —0 |
Design and validate clinical databases, including designing or testing logic checks. | —0 | —0 |
Confer with end users to define or implement clinical system requirements such as data release formats, delivery schedules, and testing protocols. | —0 | —0 |
Design forms for receiving, processing, or tracking data. | —0 | —0 |
Provide support and information to functional areas such as marketing, clinical monitoring, and medical affairs. | —0 | —0 |
Develop or select specific software programs for various research scenarios. | —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 |
|---|---|
Provide technical support for existing reports, dashboards, or other tools.O*NET Task ID 16134 | 1.0 |
Maintain library of model documents, templates, or other reusable knowledge assets.O*NET Task ID 16135 | 1.0 |
Create or review technical design documentation to ensure the accurate development of reporting solutions.O*NET Task ID 16137 | 1.0 |
Maintain or update business intelligence tools, databases, dashboards, systems, or methods.O*NET Task ID 16139 | 1.0 |
Document specifications for business intelligence or information technology reports, dashboards, or other outputs.O*NET Task ID 16142 | 1.0 |
Create business intelligence tools or systems, including design of related databases, spreadsheets, or outputs.O*NET Task ID 16144 | 1.0 |
Conduct or coordinate tests to ensure that intelligence is consistent with defined needs.O*NET Task ID 16145 | 1.0 |
Identify or monitor current and potential customers, using business intelligence tools.O*NET Task ID 16136 | 0.5 |
Communicate with customers, competitors, suppliers, professional organizations, or others to stay abreast of industry or business trends.O*NET Task ID 16138 | 0.5 |
Manage timely flow of business intelligence information to users.O*NET Task ID 16140 | 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
व्यावसायिक जानकारी
इस व्यवसाय से जुड़े हालिया बदलाव
मार्च 2026: एवरग्रीन ब्लॉग विश्लेषण प्रकाशित: 2025 में AI एक्सपोजर 67%, ऑटोमेशन रिस्क 39/100।
[स्रोत: AI Changing Work Blog]