I think it’s largely true: the narrow AI “arsenal” currently being developed often comes up with results that seem to be transferable between fields. For example, there is a recent paper that applies the same novel strategy both for image understanding and natural language sentence parsing, both with success. Although you often need lots of tinkering to get state-of-art results, producing the same quality just using a general method without any parameters seems to make a good paper.
And while the problem of how to build an AGI is not directly solved by these, we certainly get closer to it using them. (You still need a module to recognize/imagine/process visual data, unless the solution is something really abstract like AIXI...)
I think it’s largely true: the narrow AI “arsenal” currently being developed often comes up with results that seem to be transferable between fields. For example, there is a recent paper that applies the same novel strategy both for image understanding and natural language sentence parsing, both with success. Although you often need lots of tinkering to get state-of-art results, producing the same quality just using a general method without any parameters seems to make a good paper.
And while the problem of how to build an AGI is not directly solved by these, we certainly get closer to it using them. (You still need a module to recognize/imagine/process visual data, unless the solution is something really abstract like AIXI...)