Guffaw 发表于 2025-3-21 19:07:22
书目名称Artificial General Intelligence影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0162037<br><br> <br><br>书目名称Artificial General Intelligence影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0162037<br><br> <br><br>书目名称Artificial General Intelligence网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0162037<br><br> <br><br>书目名称Artificial General Intelligence网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0162037<br><br> <br><br>书目名称Artificial General Intelligence被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0162037<br><br> <br><br>书目名称Artificial General Intelligence被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0162037<br><br> <br><br>书目名称Artificial General Intelligence年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0162037<br><br> <br><br>书目名称Artificial General Intelligence年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0162037<br><br> <br><br>书目名称Artificial General Intelligence读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0162037<br><br> <br><br>书目名称Artificial General Intelligence读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0162037<br><br> <br><br>Muffle 发表于 2025-3-21 23:09:50
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The Hierarchical Memory Based on Compartmental Spiking Neuron Model,o create biologically-inspired memory models suitable for implementing the processes of features memorizing and high-level concepts. The presented architecture allows us to describe the bidirectional hierarchical structure of associative concepts related both in terms of generality and in terms of pDiatribe 发表于 2025-3-22 12:11:43
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Post-turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence,tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” reflected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. Thi规范要多 发表于 2025-3-23 09:30:33
Self-explaining AI as an Alternative to Interpretable AI,utonomous vehicles. While it is often possible to approximate the input-output relations of deep neural networks with a few human-understandable rules, the discovery of the double descent phenomena suggests that such approximations do not accurately capture the mechanism by which deep neural network