Can’t labs just pay for new kinds of data if it’s really needed? As for generalisation beyond performance in labs, it’s true that Sutskever suggested the need for better understanding of how to achieve this, but on some level, if you could infer how to do continual learning based on currently existing scientific knowledge, a swarm of capable AI researchers should be able to figure this out. These two factors seem more like “engineering” level hurdles to get over if they occur, maybe require at most months to get over. As for solving certain problems requiring real world interaction, sure, but the whole danger of a genius intellect is the ability to discern and navigate the space of possibilities towards its goals, in a manner inconceivable to a lesser intellect. In particular, it would likely require far less information than expected to make effective decisions. As long as the scaling laws hold, timelines should remain short.
Can’t labs just pay for new kinds of data if it’s really needed? As for generalisation beyond performance in labs, it’s true that Sutskever suggested the need for better understanding of how to achieve this, but on some level, if you could infer how to do continual learning based on currently existing scientific knowledge, a swarm of capable AI researchers should be able to figure this out. These two factors seem more like “engineering” level hurdles to get over if they occur, maybe require at most months to get over. As for solving certain problems requiring real world interaction, sure, but the whole danger of a genius intellect is the ability to discern and navigate the space of possibilities towards its goals, in a manner inconceivable to a lesser intellect. In particular, it would likely require far less information than expected to make effective decisions. As long as the scaling laws hold, timelines should remain short.