Archive
New research from MIT's Computer Science and Artificial Intelligence Laboratory reveals that as generative models grow, the influence of individual training images vanishes, complicating efforts to track data attribution and settle fair use disputes.
Recent analysis suggests that artificial intelligence models excel at complex mathematics primarily due to vast working-memory capacity provided by context windows, rather than emergent human-like reasoning or intuition.
Applying computer science principles to developmental neurobiology reveals how genomic information limits and spatial search constraints dictate the self-assembly of brain networks, offering insights for future artificial intelligence design.
A study evaluating cutting-edge AI agents tasked with running independent scientific projects resulted in unambiguous rejections from expert reviewers, highlighting the limits of current automated research capabilities.
A new paper published in IEEE Annals of the History of Computing traces how computer science terminology has relied on double meanings since the 1950s, leading the public to confuse statistical classification with human cognition.
