Some figures within machine learning have argued that the safety of broad-domain future AI is not a major concern. They argue that since narrow-domain present-day AI is already dangerous, this should be our primary concern, rather than that of future AI. But it doesn’t have to be either/or.
Take climate change. Some climate scientists study the future possibilities of ice shelf collapses and disruptions of global weather cycles. Other climate scientists study the existing problems of more intense natural disasters and creeping desertification. But these two fields don’t get into fights over which field is “more important.” Instead, both fields can draw from a shared body of knowledge and respect each other’s work as valuable and relevant.
The same principle applies to machine learning and artificial intelligence. Some researchers focus on remote but high-stakes research like the alignment of artificial general intelligence (AGI). Others focus on relatively smaller but nearer-term concerns like social media radicalization and algorithmic bias. These fields are both important in their own ways, and both fields have much to learn from each other. However, given how few resources have been put into AGI alignment compared to nearer-term research, many experts in the field feel that alignment research is currently more worthy of attention.
Some figures within machine learning have argued that the safety of broad-domain future AI is not a major concern. They argue that since narrow-domain present-day AI is already dangerous, this should be our primary concern, rather than that of future AI. But it doesn’t have to be either/or.
Take climate change. Some climate scientists study the future possibilities of ice shelf collapses and disruptions of global weather cycles. Other climate scientists study the existing problems of more intense natural disasters and creeping desertification. But these two fields don’t get into fights over which field is “more important.” Instead, both fields can draw from a shared body of knowledge and respect each other’s work as valuable and relevant.
The same principle applies to machine learning and artificial intelligence. Some researchers focus on remote but high-stakes research like the alignment of artificial general intelligence (AGI). Others focus on relatively smaller but nearer-term concerns like social media radicalization and algorithmic bias. These fields are both important in their own ways, and both fields have much to learn from each other. However, given how few resources have been put into AGI alignment compared to nearer-term research, many experts in the field feel that alignment research is currently more worthy of attention.
(tech executives, ML researchers)