AI Researcher in 2012: AlexNet wins ImageNet and starts the deep-learning boom

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 person wrote by hand. The work spans university labs inventing new architectures, corporate research groups scaling proven ones, and specialists who spend a career on one narrow slice, such as how a model learns from very little data.

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. Today's dominant approach, deep learning, spent decades as a minority position before a single 2012 image-recognition contest and a 2017 paper on 'attention' reshaped the whole discipline around ever larger neural networks trained on ever more data.

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. What survives that pressure, and what does not, is the question the rest of this profile tries to answer honestly rather than reassuringly.