AI's progress isn't the same as creating human intelligence in machines

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.