Intelligent Systems Conference 2018 6-7 September 2018 j London, UK A Computer Science Perspective on Models of the Mind Teresa Nicole Brooks∗, Abu Kamruzzamany, Avery Leiderz and Charles C. Tappertx Seidenberg School of Computer Science and Information Systems, Pace University Pleasantville NY Email: ∗[email protected], [email protected], [email protected], [email protected] Abstract—One area of intense focus in Artificial Intelligence Our hypothesis is that computer science based theories for (AI) research is to implement intelligent agents and machines creating human-like minds are likely better suited for modeling that can think, reason, and solve problems with similar if not learning related functions, such as object and abstract concept better proficiency of human beings. The advancements in our recognition as well as modeling memory for storing low-level understanding of intelligence and its governing principles have to high-level abstract concepts. These theories are strongly lead researchers to explore vastly, different and passionately based on our current understanding of the human mind. In debated approaches to building intelligent systems. However, in recent years a consensus appears to be emerging as observed by particular, the structure and functions of the neocortex as the 2013 AAAI Fall Symposium on Integrated Cognition, where they encapsulate properties such as variation-tolerance, self- an initial proposal is for a standard model of human-like minds. organization, and the ability to continuously adapt to new This model is largely based on current state-of-the-art cognitive input. They also have proven to be practical by the success architectures, such as ACT-R, Sigma and Soar. While we do not of closely related algorithms such deep neural networks and disagree with the proposed model, we believe more computer Hidden Markov Models which have been used to for learning science based theories for modeling human-like minds should be tasks such as pattern and speech recognition. considered for modeling memory and recognition functions. In this paper, we present a survey of the current literature that The goal of this paper is not to offer detailed changes to the supports our position. currently proposed standard model of the mind, but to present a Keywords—Standard model of the mind; cognitive architectures; survey of the current literature that supports our position that artificial intelligence; artificial neocortex; deep learning computer science based approaches to modeling human-like minds should be considered for modeling memory and some recognition functions. I. INTRODUCTION The remainder of this paper is organized as follows. Section Artificial Intelligence (AI) research is unique as it often II provides a summary of related work. In Section III, what requires cross-discipline knowledge from fields such as linguis- we currently know about the human mind is briefly discussed. tics, cognitive science, and neuroscience. As our understanding Section IV highlights key aspects of leading, proposed com- of what constitutes intelligence and the fundamental principles puter science based theories for developing artificial minds. In that governs its growth, researchers have been trying to de- Section V, we discuss the relationships between deep learning velop systems that can reason, learn and solve problems like and brain architecture. The last sections contain proposed humans. This desire has fueled decades of contentious debates, future work and conclusions. regarding the vastly different approaches to build intelligence systems, but in recent years there appears to be an emerging consensus regarding the current state of the field among the AI research community [1]. This consensus was observed during II. RELATED WORK the 2013 AAAI Fall Symposium on Integrated Cognition and In this section, we briefly discuss related research focused ultimately led to an initial proposal for a standard model of on the common threads between computer science, neuro- human-like minds. Since this symposium, there has been a science, and computing. call to engage the international research community to further develop a standard model of the mind. The initially proposed standard model and the extended A. Human Brain Project proposal by Laird et al. is based on the hypothesis that cognitive architectures, such as ACT-R [2], Sigma [3], and In 2011, Markam et al. introduced the Human Brain Project Soar [4] provide abstractions for functions such as memory, [6]. This project’s primary objective is to drive innovation in learning and perception [1]. While we do not disagree with the fields of computing, brain-related medical research, and the currently proposed model, we do propose that the research neuroscience. The project seeks to integrate research from community explore computer science based theories for mod- various disciplines under a single platform to learn more about eling human-like minds. In particular, theories that propose the functions and structure of the brain, with the ultimate models for implementing an artificial neocortex, the part of the goal of using these learning’s to build models that can be mammalian brain that is considered to be where all “thinking” tested and validated by running them against simulations on takes place [5]. supercomputers. 978-1-5386-4185-9/18/$31.00 c 2018 IEEE 1 j P a g e Intelligent Systems Conference 2018 6-7 September 2018 j London, UK B. Biologically Inspired Models We understand the mind to be “the faculty of consciousness and thought” [9]. The mind is capable of perception, judg- Fontana 2017, introduced a storage device model inspired ment, imagination, recognition, memory, feelings, attitudes, by the hippocampus. During this research, similarities were and emotions. All of these things determine our choice of observed between deep neural networks and biological neural action. Various structures of the brain are responsible for networks of the hippocampus and cortex [7]. These obser- specific mental processes. For example, our consciousness is vations led Fontana to the conclusion that the delusions of affected by the prefrontal cortex, using collections of neurons schizophrenia and psychosis, as well as the depersonalized with parallel connections across other regions in the brain thoughts of PTSD, are examples of the brain coming to a [10] Opris [11] suggests that interactions between interlam- flawed decision without training on enough data, just as deep inar prefrontal microcircuits, the posterior parietal cortex, artificial neural networks produced weak models without an and cortico-striatal-thalamo-cortical circuits are responsible for adequate amount of training data. making our decisions. The neocortex of the brain is responsible for sensory perception, recognition of everything from visual C. Neoroscience and Machine Learning objects to abstract concepts, controlling movement, reasoning from spatial orientation, rational thought and language in what In recent years because of the popularity and success of we regard as “thinking” [5]. The neocortex may also recognize deep learning, some researchers are striving to integrate deep patterns. According to Kurzweil, this is possible because the learning and neuroscience. Because of deep learning’s origins neocortex has a columnar organization, as first discovered by in neuroscience, this is a natural progression. Researchers Vernon Mountcastle. such as Marblestone et al. argue that the advances made in machine learning research specifically in the field of artificial We can use the brain’s architecture as a blueprint for neural networks make the fields of neuroscience and machine designing a digital counterpart. Kurzweil [5] estimates the learning “ripe for convergence [8].” They also state that the mind contains “30-100 million bytes of compressed code,” synthesis of machine learning and neuroscience highlights the and artificial intelligence, if created based on this design and many differences in research language, terms, and divergent using hidden Markov models and genetic algorithms, could investment between the two disciplines, and seeks to bring surpass the human mind in its capabilities. But an artificial them back into convergence by documenting their similarities brain of that ability will require massive computational power around three hypotheses discussed in the sections below. that will not be reached for another decade [12]. The Blue Brain Project [13] has thus far only managed to replicate a 1) Brain optimizes cost functions: The first hypothesis rodent brain [6]. Fig. 1 below shows the timeline of the actual is that the brain optimizes cost functions. The term “cost and projected progress of the Blue Brain simulation project functions” is used by Marblestone et al. [8] to describe what by year in a 45-degree line on a graph. The progression in neuroscience calls “learning”. They present evidence that this the X-axis represents computer speed (FLOPS) and in the Y- cost optimizing capability, which is a powerful part of deep axis represents computer memory in bytes needed to run the learning, is also present in the brain at both the micro level of project. individual neurons and the macro level of the cortex. 2) Cost functions in the brain can be localized: The next hypothesis is that cost functions in the brain dynamically change over time and are not necessarily global functions. Marblestone et al. argue that it is possible that different cost functions optimize different things in different parts of the brain and that cost