| Autor: |
Christopher M. Shaffer, Atharva Deo, Andrew Tudor, Rahul Shenoy, Cameron D. Danesh, Dhruva Nathan, Lawren L. Gamble, Daniel J. Inman, Yong Chen |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Advanced Intelligent Systems, Vol 3, Iss 8, Pp n/a-n/a (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2640-4567 |
| DOI: |
10.1002/aisy.202100016 |
| Popis: |
Unlike artificial intelligent systems based on computers which have to be programmed for specific tasks, the human brain “self‐programs” in real time to create new tactics and adapt to arbitrary environments. Computers embedded in artificial intelligent systems execute arbitrary signal‐processing algorithms to outperform humans at specific tasks, but without the real‐time self‐programming functionality, they are preprogrammed by humans, fail in unpredictable environments beyond their preprogrammed domains, and lack general intelligence in arbitrary environments. Herein, a synaptic resistor circuit that self‐programs in arbitrary and unpredictable environments in real time is demonstrated. By integrating the synaptic signal processing, memory, and correlative learning functions in each synaptic resistor, the synaptic resistor circuit processes signals and self‐programs the circuit concurrently in real time with an energy efficiency about six orders higher than those of computers. In comparison with humans and a preprogrammed computer, the self‐programming synaptic resistor circuit dynamically modifies its algorithm to control a morphing wing in an unpredictable aerodynamic environment to improve its performance function with superior self‐programming speeds and accuracy. The synaptic resistor circuits potentially circumvent the fundamental limitations of computers, leading to a new intelligent platform with real‐time self‐programming functionality for artificial general intelligence. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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