The author also expresses confusion over the explicit training to make the system act more "human-like", arguing that it would have been more interesting if the "human-like" performance had emerged from more general training. While acknowledging the paper as a promising method on an important topic, the author does not consider it an "AI breakthrough" due to its limitations in broader generalization abilities.
Key takeaways:
- The article discusses a research paper by Brenden Lake and Marco Baroni that presents a neural network capable of "human-like systematic generalization". The network was trained using a method called "Meta-Learning for Compositionality" (MLC).
- The MLC network was trained on hundreds of thousands of examples and was able to achieve performance similar to that of humans on a specific class of generalization task. However, it required extensive training, unlike humans who need only minimal training.
- The MLC network was also trained to make errors similar to those made by humans. When tested on new tasks, the network produced error frequencies and types similar to those of humans.
- Despite the success of the MLC network in this specific task, it was not able to generalize its skills to different tasks, a capability that is inherent in human learning and generalization. The author suggests that this limitation indicates that the system is not truly "human-like".