Synthetic biology routes to bio-artificial intelligence.
Synthetic biology routes to bio-artificial intelligence.
复制标题
生物界智力的合成生物学路线。
DOI：
10.1042/ebc20160014
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发表时间：
2016-11-30
影响因子：
6.4
通讯作者：
Laptyeva T
中科院分区：
文献类型：
--
作者：
Nesbeth DN;Zaikin A;Saka Y;Romano MC;Giuraniuc CV;Kanakov O;Laptyeva T
合成基因网络（SGN）的设计已经发展到了这样的程度，即新的遗传电路现在正在测试其重现机器和动物学习领域首次定义的原型学习行为的能力。在这里，我们讨论的生物实现的感知器算法的线性分类的输入数据。这种生物设计，包括细胞的“教师”和“学生”的扩展也进行了检查。我们还讨论了使用SGN的巴甫洛夫联想学习的实现，并提出了这样一个方案的例子，并在硅片上模拟其性能。除了设计SGN，我们还考虑建立SGN种群可以进化多样性的条件，以便更好地应对复杂的输入数据。最后，我们比较了人工智能（AI）领域最近的伦理问题和生物人工智能（BI）提出的未来挑战。
The design of synthetic gene networks (SGNs) has advanced to the extent that novel genetic circuits are now being tested for their ability to recapitulate archetypal learning behaviours first defined in the fields of machine and animal learning. Here, we discuss the biological implementation of a perceptron algorithm for linear classification of input data. An expansion of this biological design that encompasses cellular ‘teachers’ and ‘students’ is also examined. We also discuss implementation of Pavlovian associative learning using SGNs and present an example of such a scheme and in silico simulation of its performance. In addition to designed SGNs, we also consider the option to establish conditions in which a population of SGNs can evolve diversity in order to better contend with complex input data. Finally, we compare recent ethical concerns in the field of artificial intelligence (AI) and the future challenges raised by bio-artificial intelligence (BI).