It’s looking highly likely that the current paradigm of AI architecture (Foundation models), basically just scales all the way to AGI. These things are “General Cognition Engines” (watching that linked video helped it click for me). Also consider multimodal - the same architecture can do text, images, audio, video, sensor data, robotics. Add in planners, plugins and memory (they “System 2” to the foundation model’s “System 1″) and you have AGI. This will be much more evident with Google Gemini (currently in training).
It seems like there is no “secret sauce” left—all is needed is more compute and data (for which there aren’t significant bottlenecks). More here.
It’s looking highly likely that the current paradigm of AI architecture (Foundation models), basically just scales all the way to AGI. These things are “General Cognition Engines” (watching that linked video helped it click for me). Also consider multimodal - the same architecture can do text, images, audio, video, sensor data, robotics. Add in planners, plugins and memory (they “System 2” to the foundation model’s “System 1″) and you have AGI. This will be much more evident with Google Gemini (currently in training).
It seems like there is no “secret sauce” left—all is needed is more compute and data (for which there aren’t significant bottlenecks). More here.