Abstract
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).
Highlights
Received: 14 January 2016 Revised: 19 October 2016 Accepted: 21 October 2016Version of Record published: 30 November 2016Artificial intelligence (AI) can be defined as the decision-making capabilities of machines [1]
synthetic gene network (SGN) and experimental schemes have been proposed that could be capable of evolving increased levels of diversity, enabling classification of complex input data
In future ‘bio-artificial intelligence’ may eventually pose ethical concerns that parallel those raised by recent developments in conventional artificial intelligence
Summary
Artificial intelligence (AI) can be defined as the decision-making capabilities of machines [1]. One mode of this sorting, known as ‘classification’, is to classify all data inputs into one of two states – for instance being above or below a given linear threshold This type of supervised learning is known as linear classification and a number of algorithms have been developed to achieve this task. Subsequent delivery of classification weighting instructions to different student cell types would be influenced by the biological status of teacher cells, providing a more dynamic and sensitive signalling. This comes into play when consortia grow as 3D structures such as biofilms [17].
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