Automation index
How much of a Interpreter’s job can AI already do?
A glossary and a prep pack come earlier. Speaking at the same time as the other person, in a room, does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Interpreter
24%
AI can already do 24% of this job.
#269 / 633
View rankingInterpreter
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Prep glossaries, briefings and corpora.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 48% | +9.6 |
Translate support documents for the event.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 35% | +5.6 |
Interpret simultaneous or consecutive in the room.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 12% | +2.9 |
Manage fatigue, booth and handover.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 15% | +2.1 |
Ethics, confidentiality and live correction.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
Clients, credentials and specialism.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 18% | +2.2 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- translate texts
- interpret
- proofread translations
- Prep glossaries, briefings and corpora
- Manage fatigue, booth and handover
- Clients, credentials and specialism
- interprete
- interpretacion simultanea
Sources for this job· O*NET 27-3091.00· ESCO/ISCO 2643
IS WORK OVER?
ML engineer
29%
#64 / 633
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511