Abstract Since their origins in the 1950s, cognitive science and artificial intelligence have made slow but steady progress together toward afunctional understanding of human intelligence. The arrival of large language models (LLMs) has upended this dynamic, with unprecedented commercial investment driven by the bet that superhuman AI could emerge simply from learning patterns in language at sufficient scale. While language is a singular tool for human thinking, there is far more to intelligence than language, as evidenced by how young children and nonhuman animals learn quickly and robustly even without language, and by the continuing jagged frontier of successes and failures in LLM-based AI. This essay considers another route to intelligent machines, grounded in principled theories and well-tested models of how minds and brains think before language and how learning language transforms thinking. By deploying AI breakthroughs in LLMs as models of language use-rather than as end-to-end models of intelligence-and connecting them to models of the thinking and learning our minds do prior to and independent of language, we have the opportunity not only to build more robust and efficient AI systems, but to rebuild the bridge between cognitive science and AI. This is a route to answering the biggest open questions about how human minds work, and with that understanding, making AI that positively impacts mental health, education, and society in ways unlikely to come from machine learning alone.
Joshua B. Tenenbaum (Thu,) studied this question.