Some notes: - We'll try to abstract some of the common properties of the systems we've observed. - Systems are comprised of simple components or agents - where "simple" means "simple relative to the whole system". - The components of systems often interact in nonlinear ways. - This means that it's not easy to simply "sum up" the activities to derive systemic behavior; the whole is more than the sum of the parts. - The complex systems we observed had no central control; the ants had no central controller, the immune system has no central controller; the global finance network has no central controller. These systems were able to organize themselves in a decentralized way. - A key notion central to all complex systems is the concept of "emergent behavior" - behavior that can't be easily understood from individual components, or even small groups of individual components. Emergent behaviors have to be observed and understood at the system level rather than at the individual level. - One example of an emergent behavior is hierarchical organization - Biological organisms have cells, organs, and body-wide systems, colonies, and societies - Complex systems attempts to understand the process of how these levels of emergence develop and interact. - Another example of emergent behavior is information processing. As the system accepts information from the environment, it uses the information to make decisions about what actions to take. The components don't gain the information or make the decisions individually; the whole system is responsible for this type of information processing. For example: a single neuron doesn't hold a memory. - Another example of emergent behavior is called "dynamics" - this refers to how the system changes in its patterns in space and time. When an ant colony builds a foraging trail, the system of ants takes on patterns that change in complex ways over time. - Another example of emergent behavior is evolution and learning. Whether the system is biological, social, or technological, it will exhibit some kind of evolution in the Darwinian sense. This is what gives us adaptation, or learning. Systems improve themselves to survive and remain "fit" in their environment.
Maybe that why you need inforce memory bedmattress type of structure in brains . you need implement your kniwlege into their platform first for learning?
Easier way gor best petformance is just activate one single function . for example blind is good at smell. Too much data confuse the caluction if you dont need it. Filtration is the key
what if an ant colony is a single organism and individual ants acts as cells in an organism, even one single ant is more complex than the most complex systems humans ever build.
Some notes:
- We'll try to abstract some of the common properties of the systems we've observed.
- Systems are comprised of simple components or agents - where "simple" means "simple relative to the whole system".
- The components of systems often interact in nonlinear ways.
- This means that it's not easy to simply "sum up" the activities to derive systemic behavior; the whole is more than the sum of the parts.
- The complex systems we observed had no central control; the ants had no central controller, the immune system has no central controller; the global finance network has no central controller. These systems were able to organize themselves in a decentralized way.
- A key notion central to all complex systems is the concept of "emergent behavior" - behavior that can't be easily understood from individual components, or even small groups of individual components. Emergent behaviors have to be observed and understood at the system level rather than at the individual level.
- One example of an emergent behavior is hierarchical organization
- Biological organisms have cells, organs, and body-wide systems, colonies, and societies
- Complex systems attempts to understand the process of how these levels of emergence develop and interact.
- Another example of emergent behavior is information processing. As the system accepts information from the environment, it uses the information to make decisions about what actions to take. The components don't gain the information or make the decisions individually; the whole system is responsible for this type of information processing. For example: a single neuron doesn't hold a memory.
- Another example of emergent behavior is called "dynamics" - this refers to how the system changes in its patterns in space and time. When an ant colony builds a foraging trail, the system of ants takes on patterns that change in complex ways over time.
- Another example of emergent behavior is evolution and learning. Whether the system is biological, social, or technological, it will exhibit some kind of evolution in the Darwinian sense. This is what gives us adaptation, or learning. Systems improve themselves to survive and remain "fit" in their environment.
Maybe that why you need inforce memory bedmattress type of structure in brains . you need implement your kniwlege into their platform first for learning?
Chess doer of machine can beat human beings is the computation speed .for example : the traffic calculation
Easier way gor best petformance is just activate one single function . for example blind is good at smell. Too much data confuse the caluction if you dont need it. Filtration is the key
Number one to solve qurstion: ask what you want. Then ask who is best . then learn
@@jc9285 incredible insight thank you both
Thank you. I love you.
what if an ant colony is a single organism and individual ants acts as cells in an organism, even one single ant is more complex than the most complex systems humans ever build.