Marcel van Gerven, Computational Foundations of Natural Intelligence, Front. Comput. Neurosci., 07 December 2017 | https://doi.org/10.3389/fncom.2017.00112.
New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence.
Understanding how mind emerges from matter is one of the great remaining questions in science. How is it possible that organized clumps of matter such as our own brains give rise to all of our beliefs, desires and intentions, ultimately allowing us to contemplate ourselves as well as the universe from which we originate? This question has occupied cognitive scientists who study the computational basis of the mind for decades. It also occupies other breeds of scientists. For example, ethologists and psychologists focus on the complex behavior exhibited by animals and humans whereas cognitive, computational and systems neuroscientists wish to understand the mechanistic basis of processes that give rise to such behavior.
The ambition to understand natural intelligence as encountered in biological organisms can be contrasted with the motivation to build intelligent machines, which is the subject matter of artificial intelligence (AI). Wouldn't it be amazing if we could build synthetic brains that are endowed with the same qualities as their biological cousins? This desire to mimic human-level intelligence by creating artificially intelligent machines has occupied mankind for many centuries. For instance, mechanical men and artificial beings appear in Greek mythology and realistic human automatons had already been developed in Hellenic Egypt (McCorduck, 2004). The engineering of machines that display human-level intelligence is also referred to as strong AI (Searle, 1980) or artificial general intelligence (AGI) (Adams et al., 2012), and was the original motivation that gave rise to the field of AI (Newell, 1991; Nilsson, 2005).
Excitingly, major advances in various fields of research now make it possible to attack the problem of understanding natural intelligence from multiple angles. From a theoretical point of view we have a solid understanding of the computational problems that are solved by our own brains (Dayan and Abbott, 2005). From an empirical point of view, technological breakthroughs allow us to probe and manipulate brain activity in unprecedented ways, generating new neuroscientific insights into brain structure and function (Chang, 2015). From an engineering perspective, we are finally able to build machines that learn to solve complex tasks, approximating and sometimes surpassing human-level performance (Jordan and Mitchell, 2015). Still, these efforts have not yet provided a full understanding of natural intelligence, nor did they give rise to machines whose reasoning capacity parallels the generality and flexibility of cognitive processing in biological organisms.
The core thesis of this paper is that natural intelligence can be better understood by the coming together of multiple complementary scientific disciplines (Gershman et al., 2015). This thesis is referred to as the great convergence. The advocated approach is to endow artificial agents with synthetic brains (i.e., cognitive architectures, Sun, 2004) that mimic the thought processes that give rise to ethologically relevant behavior in their biological counterparts. A motivation for this approach is given by Braitenberg's law of uphill analysis and downhill invention, which states that it is much easier to understand a complex system by assembling it from the ground up, rather than by reverse engineering it from observational data (Braitenberg, 1986). These synthetic brains, which can be put to use in virtual or real-world environments, can then be validated against neuro-behavioral data and analyzed using a multitude of theoretical tools. This approach not only elucidates our understanding of human brain function but also paves the way for the development of artificial agents that show truly intelligent behavior (Hassabis et al., 2017).
The aim of this paper is to sketch the outline of a research program which marries the ambitions of neuroscientists to understand natural intelligence and AI researchers to achieve strong AI (Figure 1). Before embarking on our quest to build synthetic brains as models of natural intelligence, we need to formalize what problems are solved by biological brains. That is, we first need to understand how adaptive behavior ensues in animals and humans.
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David Hays and I thought about this some time ago. See, William Benzon and David Hays, Principles and Development of Natural Intelligence, Journal of Social and Biological Structures, Vol. 11, No. 8, July 1988, 293-322, Download: https://www.academia.edu/235116/Principles_and_Development_of_Natural_Intelligence, or, https://ssrn.com/abstract=1504212.