Data-centric AI, on the other hand, began in earnest in the 1970s with the invention of methods for automatically constructing “decision trees” and has exploded in popularity over the last decade with the resounding success of neural networks (now dubbed “deep learning”). Data-centric artificial intelligence has also been called “narrow AI” or “weak AI,” but the rapid progress over the last decade or so has demonstrated its power.
Deep-learning methods, coupled with massive training data sets plus unprecedented computational power, have delivered success on a broad range of narrow tasks from speech recognition to game playing and more. The artificial-intelligence methods build predictive models that grow increasingly accurate through a compute-intensive iterative process. In previous years, the need for human-labeled data to train the AI models has been a major bottleneck in achieving success. But recently, research and development focus has shifted to ways in which the necessary labels can be created automatically, based on the internal structure of the data.
The GPT-3 language model released by OpenAI in 2020 exemplifies both the potential and the challenges of this approach. GPT-3 was trained on billions of sentences. It automatically generates highly plausible text, and even sensibly answers questions on a broad range of topics, mimicking the same language that a person might use.
This essay is part of MIT Technology Review’s 2022 Innovators Under 35 package recognizing the most promising young people working in technology today. See the full list here or explore the winners in this category below.
