Developing robust cognitive models poses significant challenges in the pursuit of general purpose artificial intelligence (GPAI). Human cognition arises from complex, interconnected processes that are not fully mapped or understood. Perceptible aims to reverse engineer these processes into AI systems capable of general intelligence.
Modeling capabilities like reasoning, planning, creativity, and social skills requires an incremental approach. We build higher-level functions on top of more basic sensory and memory systems. Testing and refining these layered models involves extensive work.
Challenges also include scoping which aspects of cognition to model in an agent. Biological brains have massive parallel processing power that current computers cannot fully replicate. Capturing the essence of human intelligence within computational constraints requires selectivity.
Additionally, cognition builds on immense implicit knowledge gained through years of experience. Equipping agents with human-like common sense and intuitions enough for general intelligence is an ongoing research problem.
At Perceptible, we believe that despite these difficulties, modelling aspects of human cognition is essential for safe, beneficial GPAI. The mind is too complex to fully replicate, but each incremental advance expands agents' reasoning and capabilities.
There are no shortcuts, but diligent research into the mechanisms of intelligence can get us there. The challenges are significant, but the potential merits the effort. Understanding and modelling cognition remains crucial to Perceptible’s long-term mission.
We welcome collaboration with others pursuing this deep research agenda. Studying the mind promises to uncover mysteries with profound implications even beyond GPAI. Progress will require sustained work by many minds, but the journey leads to a future where AI can cooperate with humans to enrich society.
