2001/01/26 by Juyang Weng, James L. McClelland, Alex Pentland +4 · 5 citations
Mathematics · Engineering · Decision Sciences · #Education, Psychology, and Complexity Research #Technology Assessment and Management #Knowledge Management and Technology
paper · doi:10.1126/science.291.5504.599
openalex publication_date 2001/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
How does one create an intelligent machine? This problem has proven difficult. Over the past several decades, scientists have taken one of three approaches: In the first, which is knowledge-based, an intelligent machine in a laboratory is directly programmed to perform a given task. In a second, learning-based approach, a computer is "spoon-fed" human-edited sensory data while the machine is controlled by a task-specific learning program. Finally, by a "genetic search," robots have evolved through generations by the principle of survival of the fittest, mostly in a computer-simulated virtual world. Although notable, none of these is powerful enough to lead to machines having the complex, diverse, and highly integrated capabilities of an adult brain, such as vision, speech, and language. Nevertheless, these traditional approaches have