Designs and tests the algorithms behind machine intelligence, in a field now racing to automate a growing share of its own research process. An AI researcher designs, trains and evaluates the algorithms that let software recognize images, generate language, plan and make decisions — usually a form of machine learning, where a model improves at a task by processing large amounts of data rather than following rules a
The field is younger than most people assume: the phrase 'artificial intelligence' was coined in 1955 for a 1956 summer workshop at Dartmouth College, and the work has cycled twice through boom and 'winter' since then — periods when funding collapsed after early promises went unmet.
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| Measure | AI Researcher | Animator | You want |
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| Resists AI | |||
| Pay | |||
| Barrier to entry | |||
| Autonomy | |||
| Demand | |||
| Impact |
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All points done. Next: Open the topic: AI Researcher
Designs and tests the algorithms behind machine intelligence, in a field now racing to automate a growing share of its own research process. An AI researcher designs, trains and evaluates the algorithms that let software recognize images, generate language, plan and make decisions — usually a form of machine learning, where a model improves at a task by processing large amounts of data rather than following rules a
The field is younger than most people assume: the phrase 'artificial intelligence' was coined in 1955 for a 1956 summer workshop at Dartmouth College, and the work has cycled twice through boom and 'winter' since then — periods when funding collapsed after early promises went unmet.
AI research is unusual among the professions on this site because its subject and its instrument are the same technology. Tools built from the field's own output now draft literature reviews, propose experiments and write code for training runs, and a few labs are openly trying to automate parts of the research process itself.
Most days split between reading recent papers, writing and debugging training code, waiting on and analyzing results from long-running experiments, and discussing findings with collaborators. Contrary to the popular image, a large share of the job is diagnosing why a model is underperforming or a result won't replicate, not brainstorming new architectures from scratch.
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