All research directions01 / MOTIVATING OBSERVATION
Biological intelligence and artificial intelligence invite striking energy comparisons. Those comparisons become much harder when the task, the quality of the result, and the boundaries of the system have to match.
02 / STARTING CONTEXT
Thermodynamics offers a foundation for asking about the physical cost of computation. Connecting that foundation to the energy cost of an actual cognitive task requires measurements and an explicit definition of the task.
03 / WORKING HYPOTHESIS
Some apparent differences in efficiency may come from how computation, memory, and learning are organized. A useful comparison should separate architectural effects from differences in workload and accounting.
04 / PROPOSED INVESTIGATION
- Define one bounded task and an acceptable level of performance.
- Specify what energy is counted: learning, inference, memory, hardware, and supporting infrastructure.
- Compare available measurements and identify where a fair comparison is still impossible.
- Propose a reproducible benchmark for energy per successful task.
05 / WHAT WOULD CHALLENGE THE IDEA?
If an apparent advantage disappears when task quality and system boundaries are matched, the proposed architectural explanation needs revision.
STARTING LITERATURE
External work by other researchers provides context for this direction.
DOCUMENTING THE WORK
Papers, simulations, and video explanations will be linked as this investigation develops.
Visit the notebookConnected questions.
02Foundations of intelligence
Could a different mathematical language reveal a different way to model cognition?
Explore what quantum probability and related formal models could contribute to learning, memory, and reasoning, alongside explicit classical comparisons.
03Foundations of intelligence
Which mechanisms in nature could change how we build machines?
Investigate biological mechanisms as sources of precise computational ideas, from adaptive networks to the physical organization of information and energy transfer.