Human-like Artificial Intelligence Research Articles

Psychomatics-A Multidisciplinary Framework for Understanding Artificial Minds.

Although large language models (LLMs) and other artificial intelligence systems demonstrate cognitive skills similar to humans, such as concept learning and language acquisition, the way they process information fundamentally differs from biological cognition. To better understand these differences, this article introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science. It aims to delve deeper into the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs. To achieve this goal, Psychomatics will rely on a comparative methodology, starting from a theory-driven research question-is the process of language development and use different in humans and LLMs?-drawing parallels between LLMs and biological systems. Our analysis shows how LLMs can map and manipulate complex linguistic patterns in their training data. Moreover, LLMs can follow Grice's Cooperative principle to provide relevant and informative responses. However, human cognition draws from multiple sources of meaning, including experiential, emotional, and imaginative facets, which transcend mere language processing and are rooted in our social and developmental trajectories. Moreover, current LLMs lack physical embodiment, reducing their ability to make sense of the intricate interplay between perception, action, and cognition that shapes human understanding and expression. Ultimately, Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence, both artificial and biological. Moreover, by drawing parallels between LLMs and human cognitive processes, Psychomatics can inform the development of more robust and human-like artificial intelligence systems.

Unethical Consumer Behavior Following Artificial Intelligence Agent Encounters: The Differential Effect of AI Agent Roles and its Boundary Conditions

Recent research has shown that consumers tend to behave more unethically when encountering artificial intelligence (AI) agents than with human agents. Nevertheless, few studies have explored the differential impact of AI agents on unethical consumer behavior. From the perspective of the power relationship between AI and consumers, we classify the role of an AI agent as that of a “servant” or “partner.” Across one field study and four scenario-based experiments (offline and online), we reveal that consumers are more likely to engage in unethical behavior when encountering servant AI agents than partner AI agents due to increased anticipatory moral disengagement. We also identify the boundary conditions for the moral disengagement effect of AI agents, finding that this effect is attenuated (a) among consumers with high moral identity, (b) with human-like AI agents, and (c) in the context of high behavioral visibility. This research provides new insight into the AI morality literature and has practical implications for service agencies using AI agents.

Brain-consistent architecture for imagination.

Imagination represents a pivotal capability of human intelligence. To develop human-like artificial intelligence, uncovering the computational architecture pertinent to imaginative capabilities through reverse engineering the brain's computational functions is essential. The existing Structure-Constrained Interface Decomposition (SCID) method, leverages the anatomical structure of the brain to extract computational architecture. However, its efficacy is limited to narrow brain regions, making it unsuitable for realizing the function of imagination, which involves diverse brain areas such as the neocortex, basal ganglia, thalamus, and hippocampus. In this study, we proposed the Function-Oriented SCID method, an advancement over the existing SCID method, comprising four steps designed for reverse engineering broader brain areas. This method was applied to the brain's imaginative capabilities to design a hypothetical computational architecture. The implementation began with defining the human imaginative ability that we aspire to simulate. Subsequently, six critical requirements necessary for actualizing the defined imagination were identified. Constraints were established considering the unique representational capacity and the singularity of the neocortex's modes, a distributed memory structure responsible for executing imaginative functions. In line with these constraints, we developed five distinct functions to fulfill the requirements. We allocated specific components for each function, followed by an architectural proposal aligning each component with a corresponding brain organ. In the proposed architecture, the distributed memory component, associated with the neocortex, realizes the representation and execution function; the imaginary zone maker component, associated with the claustrum, accomplishes the dynamic-zone partitioning function; the routing conductor component, linked with the complex of thalamus and basal ganglia, performs the manipulation function; the mode memory component, related to the specific agranular neocortical area executes the mode maintenance function; and the recorder component, affiliated with the hippocampal formation, handles the history management function. Thus, we have provided a fundamental cognitive architecture of the brain that comprehensively covers the brain's imaginative capacities.

Application of multifunctional artificial synapse Cs2AgBiBr6 in pain simulation, Pavlov learning, letter learning and number recognition

Artificial synapses combined with artificial neural networks can develop next-generation computer framework. This computer framework has the characteristics of low energy consumption, high density, self-learning and self-storage. We designed and prepared artificial synapse based on all-inorganic perovskite Cs2AgBiBr6 (CABB). This work studies the synaptic excitatory behavior of CABB synapses in neurology. By applying electrical spikes, it can exhibit excitatory behaviors similar to biological synapses. On this basis, using its synaptic excitement, we simulated some conceived functions of human-like artificial intelligence, including pain simulation, Pavlov learning, letter learning and number recognition. After testing, it can be found that CABB synapse can complete these tasks. In order to realize number recognition, we designed a multi-layer neural network according to the weight update method of CABB synapse. It shows a recognition accuracy of 91.57 %.

Beyond interaction: Generative AI in conversational marketing - foundations, developments, and future directions

This paper explores the integration of Generative Artificial Intelligence (AI) in conversational marketing, transitioning from traditional marketing to interactive, customer-centric strategies. It examines the shift from one-way communication to dynamic, AI-driven interactions that personalize customer experiences. Central to this study is how Generative AI facilitates real-time, tailored dialogues between brands and customers, enhancing customer engagement and satisfaction. The paper also addresses the challenges and ethical considerations of using anthropomorphic AI in marketing, balancing human-like AI traits with user expectations. Additionally, it presents a novel framework that conceptualizes the combination of Generative AI and anthropomorphism in conversational marketing into four distinct quadrants, providing a comprehensive analysis of their potential interplay. Conclusively, it offers strategic insights for leveraging AI in marketing while adhering to ethical practices, highlighting the potential of Generative AI to transform customer engagement in the digital age. This research has two important consequences. Practically, it offers valuable insights and strategic recommendations for businesses aiming to integrate Generative AI into their conversational marketing practices effectively. Theoretically, it contributes to the academic discourse by highlighting the transformative role of Generative AI in marketing, suggesting avenues for future research in this rapidly evolving field. This study provides a brief overview of the evolving role of AI in modern marketing strategies, emphasizing the future potential and implications of AI-driven conversational marketing.

New Direction on the Research of ‘Human-Like Artificial Intelligence’ - Through the Necessity of Multi-dimensional Hermeneutics

New Direction on the Research of ‘Human-Like Artificial Intelligence’ - Through the Necessity of Multi-dimensional Hermeneutics

A social path to human-like artificial intelligence

A social path to human-like artificial intelligence

Commonsense psychology in human infants and machines

Human infants are fascinated by other people. They bring to this fascination a constellation of rich and flexible expectations about the intentions motivating people's actions. Here we test 11-month-old infants and state-of-the-art learning-driven neural-network models on the “Baby Intuitions Benchmark (BIB),” a suite of tasks challenging both infants and machines to make high-level predictions about the underlying causes of agents' actions. Infants expected agents' actions to be directed towards objects, not locations, and infants demonstrated default expectations about agents' rationally efficient actions towards goals. The neural-network models failed to capture infants' knowledge. Our work provides a comprehensive framework in which to characterize infants' commonsense psychology and takes the first step in testing whether human knowledge and human-like artificial intelligence can be built from the foundations cognitive and developmental theories postulate.

Human's Intuitive Mental Models as a Source of Realistic Artificial Intelligence and Engineering.

Despite the success of artificial intelligence (AI), we are still far away from AI that model the world as humans do. This study focuses for explaining human behavior from intuitive mental models’ perspectives. We describe how behavior arises in biological systems and how the better understanding of this biological system can lead to advances in the development of human-like AI. Human can build intuitive models from physical, social, and cultural situations. In addition, we follow Bayesian inference to combine intuitive models and new information to make decisions. We should build similar intuitive models and Bayesian algorithms for the new AI. We suggest that the probability calculation in Bayesian sense is sensitive to semantic properties of the objects’ combination formed by observation and prior experience. We call this brain process as computational meaningfulness and it is closer to the Bayesian ideal, when the occurrence of probabilities of these objects are believable. How does the human brain form models of the world and apply these models in its behavior? We outline the answers from three perspectives. First, intuitive models support an individual to use information meaningful ways in a current context. Second, neuroeconomics proposes that the valuation network in the brain has essential role in human decision making. It combines psychological, economical, and neuroscientific approaches to reveal the biological mechanisms by which decisions are made. Then, the brain is an over-parameterized modeling organ and produces optimal behavior in a complex word. Finally, a progress in data analysis techniques in AI has allowed us to decipher how the human brain valuates different options in complex situations. By combining big datasets with machine learning models, it is possible to gain insight from complex neural data beyond what was possible before. We describe these solutions by reviewing the current research from this perspective. In this study, we outline the basic aspects for human-like AI and we discuss on how science can benefit from AI. The better we understand human’s brain mechanisms, the better we can apply this understanding for building new AI. Both development of AI and understanding of human behavior go hand in hand.

The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence

Abstract In 1950, Alan Turing proposed a test of whether a machine was intelligent: could a machine imitate a human so well that its answers to questions were indistinguishable from a human's? Ever since, creating intelligence that matches human intelligence has implicitly or explicitly been the goal of thousands of researchers, engineers, and entrepreneurs. The benefits of human-like artificial intelligence (HLAI) include soaring productivity, increased leisure, and perhaps most profoundly a better understanding of our own minds. But not all types of AI are human-like-in fact, many of the most powerful systems are very different from humans-and an excessive focus on developing and deploying HLAI can lead us into a trap. As machines become better substitutes for human labor, workers lose economic and political bargaining power and become increasingly dependent on those who control the technology. In contrast, when AI is focused on augmenting humans rather than mimicking them, humans retain the power to insist on a share of the value created. What is more, augmentation creates new capabilities and new products and services, ultimately generating far more value than merely human-like AI. While both types of AI can be enormously beneficial, there are currently excess incentives for automation rather than augmentation among technologists, business executives, and policy-makers.

Getting AI Right: Introductory Notes on AI & Society

Getting AI Right: Introductory Notes on AI & Society

Statistical Analysis of Multisensory and Text-Derived Representations on Concept Learning.

When learning concepts, cognitive psychology research has revealed that there are two types of concept representations in the human brain: language-derived codes and sensory-derived codes. For the objective of human-like artificial intelligence, we expect to provide multisensory and text-derived representations for concepts in AI systems. Psychologists and computer scientists have published lots of datasets for the two kinds of representations, but as far as we know, no systematic work exits to analyze them together. We do a statistical study on them in this work. We want to know if multisensory vectors and text-derived vectors reflect conceptual understanding and if they are complementary in terms of cognition. Four experiments are presented in this work, all focused on multisensory representations labeled by psychologists and text-derived representations generated by computer scientists for concept learning, and the results demonstrate that (1) for the same concept, both forms of representations can properly reflect the concept, but (2) the representational similarity analysis findings reveal that the two types of representations are significantly different, (3) as the concreteness of the concept grows larger, the multisensory representation of the concept becomes closer to human beings than the text-derived representation, and (4) we verified that combining the two improves the concept representation.

Key Advanced Research Initiative: A Manifesto for the New-Generation Artificial Intelligence

The goal here is to identify key directions for the future advanced research initiatives in Artificial Intelligence (AI) and beyond. The following areas are identified as having particular importance: (1) socially emotional, ethical, and moral AI, (2) self-developing and self-sustainable AI, and (3) human-analogous AI, inspired by the human psychology. As a result, a general concept is formulated with the intent to clarify and unify the currently popular slogans, including Artificial General Intelligence (AGI), Strong AI, Human-Level or Humanlike AI (HLAI), Brain-Inspired or Biologically Inspired Cognitive Architectures (BICA), and more. The key idea of the proposed concept is that future AI must open a new angle of view and new perspectives to humans, thereby enriching and transforming the society, helping it to solve its problems and taking the civilization to a new level. While being created by humans, for humans, and fully compatible with humans at the social level, it will not be “a human in silicon”, but rather an “alien”: intelligent, friendly, and welcome. Its principles will combine preprogrammed basic functions and its own natural ontogeny in a virtual social environment. Forms of implementation will range from virtual entities to wearable electronics and autonomous robots. The expected impact on the society will be immense and crucial for its survival.

A TD-Learning Based Bionic Cerebellar Model Controller For Humanoid Robots

The cerebellum is a crucial component of the human body that plays a vital role in human walking. To design a robot gait controller by referring the working mechanism of the cerebellum is one of the hotspots in the bionic control field. This paper designs a bionic cerebellar motion control model to control the slope gait of a humanoid robot. The cerebellum model refers to the connection method between neurons in a human cerebellum, and expresses from a bionic perspective how the neurons in the cerebellum process external information and generate control commands during walking. Inspired by how human walking is learned, this model employs reinforcement learning in the learning process of the bionic cerebellar model. A corresponding simulation environment is also designed to train and test the cerebellar control model's effectiveness when regulating a robot's slope walking stability. The simulation experimental results demonstrate that the cerebellum model can achieve stable control of the walking motion of the humanoid robot after training, verifying its effectiveness, and laying a foundation for further realization of human-like artificial intelligence.

Personhood for Synthetic Beings: Legal Parameters and Consequences of the Dawn of Humanlike Artificial Intelligence

There is a growing interest in Artificial Intelligence in tech hubs like Silicon Valley and in the world at large that is finally matching the ambition and creativity that movies and media have been producing for a very long time. This paper takes that cultural interest and economic investment to its logical conclusion and assumes that a humanlike Artificial Intelligence, or synthetic person, can and will be created in the near future. Then it considers what the law should do about that creation. Specifically, this paper argues that a test should be developed for whether a synthetic person can be granted personhood under the law before such test is needed. The tenets, if not the specifics, of such a test are outlined here that provide for a synthetic person that is intelligent, social, self-conscious, and individualized. But that is only half the problem. If a synthetic person can be created, one that passes a pre-determined test that allows it to access legal standing, to what sorts of rights and responsibilities does it have access? In light of the kind of synthetic person that could pass the test, four potential outcomes for rights for a synthetic person are addressed ranging from none to a full suite of human civil rights.

Cogmic space for narrative-based world representation

Representing a world or a physical/social environment in an agent’s cognitive system is essential for creating human-like artificial intelligence. This study takes a story-centered approach to this issue. In this context, a story refers to an internal representation involving a narrative structure, which is assumed to be a common form of organizing past, present, future, and fictional events and situations. In the artificial intelligence field, a story or narrative is traditionally treated as a symbolic representation. However, a symbolic story representation is limited in its representational power to construct a rich world. For example, a symbolic story representation is unfit to handle the sensory/bodily dimension of a world. In search of a computational theory for narrative-based world representation, this study proposes the conceptual framework of a Cogmic Space for a comic strip-like representation of a world. In the proposed framework, a story is positioned as a mid-level representation, in which the conceptual and sensory/bodily dimensions of a world are unified. The events and their background situations that constitute a story are unified into a sequence of panels. Based on this structure, a representation (i.e., a story) and the represented environment are connected via an isomorphism of their temporal, spatial, and relational structures. Furthermore, the framework of a Cogmic Space is associated with the generative aspect of representations, which is conceptualized in terms of unconscious- and conscious-level processes/representations. Finally, a proof-of-concept implementation is presented to provide a concrete account of the proposed framework.

Why general artificial intelligence will not be realized

The modern project of creating human-like artificial intelligence (AI) started after World War II, when it was discovered that electronic computers are not just number-crunching machines, but can also manipulate symbols. It is possible to pursue this goal without assuming that machine intelligence is identical to human intelligence. This is known as weak AI. However, many AI researcher have pursued the aim of developing artificial intelligence that is in principle identical to human intelligence, called strong AI. Weak AI is less ambitious than strong AI, and therefore less controversial. However, there are important controversies related to weak AI as well. This paper focuses on the distinction between artificial general intelligence (AGI) and artificial narrow intelligence (ANI). Although AGI may be classified as weak AI, it is close to strong AI because one chief characteristics of human intelligence is its generality. Although AGI is less ambitious than strong AI, there were critics almost from the very beginning. One of the leading critics was the philosopher Hubert Dreyfus, who argued that computers, who have no body, no childhood and no cultural practice, could not acquire intelligence at all. One of Dreyfus’ main arguments was that human knowledge is partly tacit, and therefore cannot be articulated and incorporated in a computer program. However, today one might argue that new approaches to artificial intelligence research have made his arguments obsolete. Deep learning and Big Data are among the latest approaches, and advocates argue that they will be able to realize AGI. A closer look reveals that although development of artificial intelligence for specific purposes (ANI) has been impressive, we have not come much closer to developing artificial general intelligence (AGI). The article further argues that this is in principle impossible, and it revives Hubert Dreyfus’ argument that computers are not in the world.

The St. Petersburg Paradox: A Fresh Algorithmic Perspective

The St. Petersburg paradox is a centuries-old puzzle concerning a lottery with infinite expected payoff on which people are only willing to pay a small amount to play. Despite many attempts and several proposals, no generally-accepted resolution is yet at hand. In a recent paper, we show that this paradox can be understood in terms of the mind optimally using its limited computational resources (Nobandegani et al. 2019). Specifically, we show that the St. Petersburg paradox can be accounted for by a variant of normative expected-utility valuation which acknowledges cognitive limitations: sample-based expected utility (Nobandegani et al. 2018). SbEU provides a unified, algorithmic explanation of major experimental findings on this paradox. We conclude by discussing the implications of our work for algorithmically understanding human cognition and for developing human-like artificial intelligence.

An Overview Studying of Deep Learning

Deep learning is a subfield of machine learning however both drop under the broad category of artificial intelligence. Deep learning is what powers the most human-like artificial intelligence that consents computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Deep learning is making major advances in solving problems hence categorized in wider section of artificial intelligence. The main advantage of Deep Learning is to create an artificial neural network that can learn and make intelligent decisions on its own and to process large numbers of features makes deep learning very powerful when dealing with unstructured data.

Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

Emotion is intrinsic to humans and consequently, emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data on platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration), and more. In Addition, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user's emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a difficult problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.