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From Data to Knowledge Processing Machines
| Content Provider | MDPI |
|---|---|
| Author | Renard, Didier A. |
| Copyright Year | 2022 |
| Description | Natural complex adaptive systems arise, live, and eventually, die. One could say that they became unfit for their environment. The science of complexity puts it differently: the equilibrium states of a system are changing all the time, as adjustments to external fluctuations. Down the road, when facing positive feed-back loops, equilibria can drift across the edge of chaos. Applied to the digital world, where control is in our hands, we can imagine steering equilibria to areas where beneficial emergence arises while avoiding collapse. This paper deals with the ground principles to reach such control. |
| Starting Page | 26 |
| DOI | 10.3390/proceedings2022081026 |
| Issue Number | 1 |
| Volume Number | 81 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-11 |
| Access Restriction | Open |
| Subject Keyword | Structural Machines Knowledge Emergence Artificial Intelligence Complex Adaptive Systems Autopoietic Systems |
| Content Type | Text |
