14 1 Introduction In essence this is a list of claims such that, if the reader accepts these claims, they should probably accept that the CogPrime approach to AGI is a viable one. On the other hand if the reader rejects one or more of these claims, they may find one or more aspects of CogPrime unacceptable for some reason. Without further ado, now, the claims: 1. General intelligence (at the human level and ultimately beyond) can be achieved via creating a computational system that seeks to achieve its goals, via using perception and memory to predict which actions will achieve its goals in the contexts in which it finds itself. 2. To achieve general intelligence in the context of human-intelligence-friendly environments and goals using feasible computational resources, it’s important that an AGI system can handle different kinds of memory (declarative, procedural, episodic, sensory, intentional, attentional) in customized but interoperable ways. 3. Cognitive synergy: It’s important that the cognitive processes associated with different kinds of memory can appeal to each other for assistance in overcoming bottlenecks in a manner that enables each cognitive process to act in a manner that is sensitive to the particularities of each others’ internal representations, and that doesn’t impose unreasonable delays on the overall cognitive dynamics. 4. As a general principle, neither purely localized nor purely global memory is sufficient for general intelligence under feasible computational resources; “glocal” memory will be re- quired. 5. To achieve human-like general intelligence, it’s important for an intelligent agent to have sensory data and motoric affordances that roughly emulate those available to humans. We don’t know exactly how close this emulation needs to be, which means that our AGI systems and platforms need to support fairly flexible experimentation with virtual-world and/or robotic infrastructures. 6. To work toward adult human-level, roughly human-l