Yann LeCun’s New AI Paper Argues AGI Is Misdefined and Introduces Superhuman Adaptable Intelligence (SAI) Instead

Editor's take

Yann LeCun and his colleagues propose that the prevailing concept of Artificial General Intelligence (AGI) is fundamentally flawed, lacking clear definition and measurability, and introduce "Superhuman Adaptable Intelligence" (SAI) as a more accurate descriptor.

This reframing is significant because the current pursuit of AGI, as exemplified by models like OpenAI's GPT-4 and Google's Gemini, is driven by an ill-defined target. SAI, with its emphasis on the ability to adapt and learn across diverse, unforeseen circumstances, offers a potentially more grounded and achievable objective for AI development, shifting focus from a nebulous "human-level" to a quantifiable adaptive capability.

Future research should clarify how SAI can be empirically tested and benchmarked, moving beyond current performance metrics on static datasets. The practical implications for AI safety and the development of truly robust, general-purpose systems will depend on establishing concrete methodologies for assessing and cultivating this adaptive intelligence.