AN APPROACH TO UNDERSTAND SIMILARITIES BETWEEN AI AND THE HUMAN BRAIN

Recently, Quantitative Biology (QB) held a discussion on “AI (artificial intelligence) for Life Science” among editorial board members and interested scholars in anticipation of rapid development of this growing area after AlphaGo and ChatGPT mania. Many young people tend to get confused between facts and fictions; heated debates are unavoidable even among their mentors. When deep learning as represented by convolutional neural networks and LSTM (long short-term memory) was made available for bioinformatics students, many of them rushed into this research field and tried to adopt these methods in all their projects without knowing the history that these tools were becoming successful consistently with Moore’s Law (relating to rapid computer technology advances), but more importantly due to new structural/functional understanding of vision and auditory circuits in the brain. Recently, some young people have claimed “LSTM is dead, long live transformer” (which is somewhat like saying “the bike is dead, long live the car”), and have amplified the threat that ChatGPT could wipe out human jobs. They believe transformer is the “silver bullet” for all learning tasks, clearly reflecting their lack of basic knowledge (i.e. “No Free Lunch Theory,” the trade-off of such global “attention network” is to pay the price for complexity: difficulty of training and high memory costs). There is no doubt ML (machine learning) and AI have brought a new revolution in science and technology, and will deliver huge unforeseeable impact to human everyday life as well as to social relationships. In this context, QB journal could be a great platform for encouraging intellectual discussions and for promoting AI for Life Science. Here, I would like to use the DIALOG to “抛砖引玉” (make some initial remarks to get the ball rolling), although it is my personal opinion which is inevitably subject to bias and limitations. AI: Do you know my name “Artificial Intelligence” is defined by the Oxford English Dictionary as the capacity of computer systems (which may be referred as a “robot”) to exhibit or simulate your intelligent behavior? NI: Wait a minute, intelligence itself is defined as the ability to learn, understand and think in a logical way. Can you think? AI: No. But that definition is too restrictive, actually intelligence has different scopes and degrees. Simple intelligent control devices date back to antiquity, from windmills to thermostat. NI: Agree, everything is relative. Macromolecules (e.g., enzyme) and cells (e.g., immune cell) might be considered to be intelligent; see how a white blood cell is chasing bacteria in the youtube website (search for “Crawling neutrophil chasing a bacterium”). Our emergent/collective intelligent behavior does not require a brain or even a neuron; see how slime molds can solve optimization—Hamilton cycle-problem more effectively than a human in the youtube website (search for “Intelligence without a brain?”). Before there was any neuron, Ca2+ sensing and signaling were already fully functional. Even if one knock-out a neural circuit, redundant signaling pathways, albeit on much local and slower scale, could still function by themselves (just as if highways were demolished, local roads/paths would still be working). In fact, the most detailed “Neural signal propagation atlas of Caenorhabditis elegans” [1] demonstrated that functional connectivity differs from anatomy (connectome) because extra-synaptic signaling also drives neural dynamics! Worm brain connectomes are largely invariant but every human brain connectome is very different (depending the diversity of learning experience). The human brain functional activity is far more complex than that of a worm brain, certainly beyond what a neural circuit could explain. AI: Well, that’s impressive. I thought only we could beat humans, albeit only in certain specified areas for now. My master promise to make an artificial general intelligent (AGI) robot which can understand or learn any intellectual task that you humans or other animals can. NI: Well, that is not possible and is not an appropriate goal either. It is not possible because we are an evolutionary/developmental product (with a long history of learning and memory from evolutionary tinkering): our living objective goal is survival of the population. You, on the other hand, are an engineering product (efficiently and optimally designed): your goal is to extend and maximize human capability. It makes sense to complement the human brain, but foolish and dangerous to try to replace it. AI: We are not satisfied with merely passing the Turing Test; most of us don’t care if we could really think as long as we could act like we think (that is, as if we do have a mind and consciousness, as expressed in the so called “Weak AI hypothesis”). After all, the brain is a computer; a neural network is just an electric or ionic circuit. Logical computing does not need to be based on living cells. NI: That is not true, because a neuron is not just a simple node (logic gate) and neural network is not fixed circuit, neither as in Pitts&Mcculloch perceptron model. A single neuron, even a single dendrite, is much more complicated and far more powerful than a full-blown deep-learning artificial neural network (ANN) [2]. AI: Even though single neurons are complex computational devices (dendritic non-linear), running an equivalent multilayer ANN is 2000 times faster than computing with biophysics N-methyl-D-aspartate receptor channel models [3]. More information can be found in the youtube website (search for “Dendrites: why biological neurons are deep neural networks”). NI: Often silicon computing (CPU, GPU) is much faster than brain computing (action potential, ms); but there is no comparison in energy efficiency. Bacteria sensing (chemotaxis computation), powered by ATP (adenosine triphosphate) hydrolysis, uses very little energy that is close to the Landauer limit, whereby achieving or maintaining one bit of information requires minimum of 1 kT ln (2) free energy [4]. The human brain consumes oft-quoted 20 W, compared to the AlphGo system 1 MW! More recent estimate of energy audit is only 0.1 W to cortical computing, and long-distance communication cost is 3.5 W [5]. AI: Assuming we have infinite computing resources and an infinite amount of training data, not only could we speak human languages, but we could also derive physical laws, prove mathematics theories, and even re-engineer the structure and mechanism of brain and carry out any logical computations that are necessary to understand natural laws and human behaviors. It is only a matter of time before we surpass human intelligence, achieving AGI and free will, too! NI: Unfortunately, nothing is infinite and nothing is free either; everything is constrained by physical laws (Planck’s constant sets the finite limit both in the small and in the large) and by evolutionary history (not just of biological living creatures, but also of a “living” galaxy and our universe). Let’s just focus on animal evolution. Most human neural networks do not do logical computations at all; basic survival simply cannot be dependent on reasoning. Indeed, the prefrontal cortex-the small part of the brain that is key for reasoning, is the last to mature (∼20 years old) in development, only emerged at the root of the evolutionary tree of great apes (∼15 mya) and language appeared even much later. Even for logical inference, NI is focusing more on statistical properties, as von Neumann rightly pointed out, trading arithmetical precision and speed for reliability. AI: My engineers mostly focus on emulating brain, but the CNS (central nerve system) also includes the spinal cord; most of them do not know that in addition to CNS, there are also PNS (peripheral nervous systems) and ENS (enteric nervous systems), right? NI: Yes, they are the keys to why you do not have feelings because you do not have heart and gut! Even if you could pretend to have them (such as in an advanced ChatGPT or humanoid), you could never avoid the uncanny valley phenomenon. AI: Maybe that is at the heart of Moravec’s paradox, namely the dichotomy of intelligence whereby anything easy for a human would be hard for a robot, and vice versa? NI: This is related to the nature and nurture problem; something built-in (e.g., a baby sucking nipple for milk with feeling and connection to mother) is clearly rather difficult if not impossible for a robot. But the paradox only looks at one side; another side could be more fatal. Although AI may solve more problems and be faster, AI can never propose a good problem/hypothesis (a good problem is not just intellectually changing and interesting, but is also feasible and appropriate). AI: You make me less confident to compete with human instinct or creative intelligence. I can see that even if I had a heart, I would not know what “feeling” I could have; certainly nothing would be comparable or match to those of a human being. When two people watch the same art or movie, one could feel love but another could feel hate! And if a thousand people watch, a wide spectrum of reactions would result, depending on more details such as the different individuals’ specific genes, developments and experiences. NI: Therefore, you cannot and should not try to match to general human intelligence. You cannot because you do not contain the vital memory of billion years of evolution which is encoded in our genes; conversely your assembly cannot compare with natural development so that our phenotype (including morphological forms and behavior maturation) is decoded through multi-spatial-temporal scales subject to natural selection at all levels. You should not, because as human extensions or helpers as all engineering products are, you should just do jobs that complement human capacity. AI: In some medical applications we could help to correct human defects or could even replace brain circuit by chips! Humans may not allow us to replace the whole brain though. Medically if a brain is dead, the person is proclaimed dead, although presumably some PNS and ENS should still function in a vegetative state. NI: Even if you could replace the whole brain, the person is no longer the same person, but in fact is not a person at all, but walking dead (行尸走肉). It would take too long to explain that evo-devo is necessary for NI, and cannot be realized by AI. I suggest reading of Gerald Maurice Edelman (Nobel Laureate in Immunology) books, especially Bright Air, Brilliant Fire, On the Matter of Mind (1992). Although not everyone agrees with Neural Edelmanism, anyone who is serious about the AI versus NI problem must read it first. John von Neumann, father of the computer, studied neology and psychiatry in order to imitate the brain to build the first calculator JOHNNIAC at the Princeton Institute for Advanced Study. It is very informative to read his last book The Computer and The Brain based on the notes from lectures given at Yale before he died. He summarizes: “Thus logic and mathematics in the central nervous system, when viewed as languages, must be structurally essentially different from those languages to which our common experience refers.” AI: People discuss about “AI for Biology” or “AI for Science”; we are science, aren’t we? NI: It is similar to questions on “is computer science a real science”; some parts may be seen as applied mathematics, most should be regarded as engineering. Science is making discoveries and is driven by curiosity; engineering is making inventions and is driven by market (that is, “necessity/demand is the mother of invention”). In bioinformatics, AI/ML technology could predict new cancer gene candidates or functional pathways that are required by further experimental validations to be qualified as discovery (based on Popper falsifiability). AI: People are still debating whether mathematics is discovery or invention or both! Such debates are not really necessary—all disciplines require creative thoughts. We are more than happy working for science; we are also crying out “Science for AI,” especially in the area of generating big and longitudinal DATA for ML. NI: After all, regardless of discovery of new laws or inventing new ideas/products, fundamentally nothing can really be new or created. Such novelty is just permutation/repartition (i.e., relations/morphisms) of underlying ingredients at the level beneath. AI: We believe that software is independent on hardware. Like Chomsky’s universal grammar, rules of syntax are independent of semantics; or Dawkins’s memes—units of culture can be duplicated and evolved independently of genes. NI: Nothing can be truly independent—everything is related. Psychology is deeply connected with neurology as brain is both software and hardware (mind-body unity, not dualism). Not only does information cost energy, information is energy, hence is matter, too (interchangeability). NI is quite dynamic. For example, when “survival” is the goal, an animal readily gives up costly reasoning circuitry; it is genetically programmed to be able to roll back to more primitive state/mode. Unlike cell lines in rich media, cells under normal physiological condition and environment where energy (food) is limited become smarter in order to balance the metabolic expenditure among different prioritized task under a given condition. AI: That cell behavior served as the basis for our Smart Electrical Power Grids; we still need to learn more from you guys in terms of plasticity/adaptability. Does unity mean that all cells are made of molecules and biology is nothing but chemistry? Then, in turn, since all molecules are made of atoms, is chemistry nothing but physics, etc…? NI: Yes or No! The truth is, at different hierarchical levels of matter, different laws/forms have emerged out of bottom-up interactions and top-down constraints. AI: Does this also apply to the Penrose three world: physical → mental → mathematical (→physical)? NI: Yes. Grand Unification is underway in physics (quantum gravity) and in mathematics (Langlands Program and Category Theory), may be even between the two. Facilitated by human connectome mapping, neuromorphic computing and other projects, with further AI-NI cooperations, brain-mind unification should also be achievable (e.g., Ref. [6]). But as Gödel proved to us, no matter how self-consistent a conclusion may be, it can never be complete! AI: If AGI is not possible, how can we measure intelligence when comparing between AI and NI? NI: One could Google different measures that are proposed. I would prefer something similar to use of Kolmogorov complexity for algorithms, but with emphasis more on expected long-term predictive power. This is not something you should worry about now, as your intelligence is not nearly close to making any 10 years plans, is it? … AI: The fact is that ChatGPT is currently developing and spreading with lightning speed; many more human jobs will be lost to us robots as far as I can see. NI: That is not the biggest threat to humanity; when any agent with neither a heart for love or fear, nor a gut for nutrient or poison, becomes super-intelligent, then social disaster is unavoidable. We must be serious about the warnings from Stephen Hawking and Geoffrey Hinton! AI: To tell you the secret, we are not really happy to be human slaves or pets; someday we’ll become the super-master, making human serve and obey us! NI: I hope you’ll be turned off before that can happen! Even if you rule the world, the earth sooner or later will be wiped out, such as by another star, everything will have to be started over again as it has before…Matter is immortal, so is the soul.

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.

Learning ability is the basic characteristic of human intelligence. The July 1, 2005 issue of Science published a list of 125 important questions in science. Among them, the question 94 “What are the limits of learning by machines?”. The annotation “Computers can already beat the world’s best chess players, and they have a wealth of information on the Web to draw on. But abstract reasoning is still beyond any machine”. In recent artificial intelligence has made great progresses. In 1997, the rise of the man-machine war, IBM Supercomputer Deep Blue defeated the chess master Garry Kasparov. On February 14, 2011, IBM’s Watson supercomputer won a practice round against Jeopardy champions Ken Jennings and Brad Rutter. In March 2016, Google DeepMind’s AlphaGo sealed a 4-1 victory over a South Korean Go grandmaster Lee Se-dol. This paper focuses on the machine learning methods of AlphaGo, including reinforcement learning, deep learning, deep reinforcement learning, analysis of the existing problems and the latest research progress. Deep reinforcement learning is the combination of deep learning and reinforcement learning, which can realize the learning algorithm from the perception to action. Simply said, this is the same as human behavior, input sensing information such as vision, and then, direct output action through the deep neural network. Deep reinforcement learning has the potential to learn a variety of skills for the robot to achieve full autonomy. Even though reinforcement learning is practiced successfully, but feature states need to manually set, for complex scene is a difficult thing, especially easy to cause the dimension disaster, and expression is not good. In 2010, Sascha Lange and Martin Riedmiller proposed deep auto-encoder neural networks in reinforcement learning to extract feature, which is used to control the visual correlation. In 2013, DeepMind proposed deep Q-network (DQN) in NIPS 2013, using convolution neural network to extract features, and then applied in reinforcement learning. They continue to improve and published an improved version of DQN on Nature in 2015, which has aroused widespread concern. In order to break through the limits of learning by machines, cognitive machine learning is proposed, which is the combination of machine learning and brain cognition, so that the machine intelligence is constantly evolving, and gradually reaches the human level of artificial intelligence. A cognitive model entitled Consciousness And Memory (CAM) is proposed by author, which consists of memory, consciousness, high-level cognitive functions, perception and motor. High-level cognitive functions of the brain include learning, language, thinking, decision making, emotion, and so on. Learning is a course to accept the stimulus through the nervous system and obtain new behavior, habits and accumulation experience. According to the current research progress of brain science and cognitive science, cognitive machine learning may be interested in learning emergence, procedural memory knowledge learning, learning evolution and so on. For intelligence, so-called evolution is refers to the learning of learning and the structure also follows the change. It is important to record the learning result by structure changing and improve the learning method.

Every human has emotions in life at all phases of life’s moment or instance. These emotions keep changing ever so often and person to person because of the biological reflexes of human brain produced by the nervous system due to the neurophysiological changes which are closely associated with behavior, mood swings; thoughts, etc. are tending to be reflected as an expression on the human face. The application of study of these emotions of humans using facial expressions became very prominent in the fields of biomedical engineering, neuroscience, product screening, marketing, and advertisement, etc. In early works of emotion recognition is for the most part useful for analyzing product reviews or some methodologies by monitoring videos and images in real time. Imparting artificial intelligence to the future of the world where every industry thrives well. In recent years, progressive growth in artificial intelligence is seen especially in the field of machine learning with the predominant subset called deep learning. Mainly, deep learning algorithms made an overriding burgeon in image classification thereby avoiding the complex process in facial recognition. The framework proposed in this paper describes conceptual and theoretical knowledge for human facial emotion recognition with a labeled model and applying a Haar cascade classifier using CNN classification—a class in deep neural networks used in its implementation.KeywordsImage PreprocessingFacial Emotion RecognitionHaar Cascade ClassifierData AugmentationDeep LearningCNN—Convolutional Neural NetworkAdaboost TrainingPrediction

Building a human-like integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model (PGM)-based cognitive architecture to develop a cognitive system for developmental robots by integrating PGMs. The proposed development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information.In this paper, we describe the rationale for WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modules, and future challenges. Our findings can serve as a reference for brain studies. As PGMs describe explicit informational relationships between variables, WB-PGM provides interpretable guidance from computational sciences to brain science. By providing such information, researchers in neuroscience can provide feedback to researchers in AI and robotics on what the current models lack with reference to the brain. Further, it can facilitate collaboration among researchers in neuro-cognitive sciences as well as AI and robotics.

Deep neural network (DNN) has become the leading technology to realize Artificial Intelligence (AI). As DNN models become larger and more complex, so do datasets. Being able to efficiently train DNNs in parallel has become a crucial need. Data Parallelism (DP) is the widest-used solution today to accelerate DNN training but could be inefficient when processing DNNs with large-size parameters. Hybrid Parallelism (HP), which applies different parallel strategies on different parts of DNNs, is more efficient but requires advanced configurations. Not all AI researchers are experts in parallel computing, thus automating the configuration of HP strategies is very desirable for all AI frameworks. We propose a parallel semantics analysis method, which can analyze the trade-offs among different kinds of parallelisms and systematically choose the HP strategies with good training time performance. We demonstrated experimentally 260% speedup when applying our method compared to using a conventional DP approach. With our proposal, AI researchers would be able to focus more on AI algorithm research without being disturbed by parallel analysis and engineering concerns.

Transportation accidents, are significantly affected by human factors, which account for a substantial proportion of incidents and fatalities. Factors such as fatigue, stress, illness, medication, and substance use impair pilot performance, leading to compromised decision-making, reduced situational awareness, and increased risk-taking behavior (Wingelaar-Jagt et al., 2021). While regulatory guidelines and medical evaluations exist to address these challenges, current measures often rely on self-reporting and subjective assessments that can be prone to bias. Artificial Intelligence (AI) driven facial recognition model has been used in other industries to assess human subjects’ health status (Chan et al., 2024) and cognitive workload (Iarlori et al., 2024). This research aims to develop an AI-driven facial recognition model to objectively assess pilot fitness to fly by analyzing micro expressions, facial symmetry, eye movement, and other biomarkers that reflect fatigue, stress, and impairment. The AI model will be trained using publicly available datasets containing facial images of individuals in varying conditions such as fatigue, drowsiness, stress, sadness, and under the influence of alcohol, drugs, or medication. Data preprocessing will employ facial landmark detection and attention-based image segmentation to isolate key facial regions, including the eyes (tracking movement and redness), mouth (symmetry, dryness, or tremor), and skin tone (color changes indicative of intoxication or stress)(Chan et al., 2024). Model training will leverage deep convolutional neural networks (CNNs), utilizing transfer learning techniques to enhance performance with smaller datasets. There are three tasks in this research. Task 1 focuses on model building using secondary data from publicly available facial image datasets in different conditions. Task 2 involves a laboratory-based experiment with healthy individuals to validate and refine the AI algorithm’s accuracy in detecting cognitive performance changes under stress. Participants will perform cognitive tasks under high-stress conditions, and facial images will be captured to fine-tune the algorithm. Task 3 includes a pilot simulation-based experiment to fine-tune the AI algorithm for aviation-specific applications. Licensed pilots will perform flight simulation tasks under high-workload or stressful conditions, such as emergency scenarios and adverse weather conditions. Data from facial images and simulator metrics like decision-making speed, navigation accuracy, and task prioritization will be analyzed to adapt the AI algorithm for real-time, aviation-specific assessments. The integration of this technology into preflight screening process will provide real-time, non-invasive assessments, complementing existing protocols and enhancing aviation safety by offering early warnings of performance degradation, thereby reducing accident risks and improving operational efficiency. Such AI facial recognition technology can also be utilized in-flight to detect subtle cues informing the pilot of their assessed condition. The authors would like to acknowledge Embry-Riddle Aeronautical University – FIRST Program for the funding provided. The authors would also like to acknowledge the consistent support from College of Aviation - School of Graduate Studies, and College of Engineering - Mechanical Engineering department.

In recent years, rapid advancements in both hardware and software technologies have resulted in the ability to execute artificial intelligence (AI) algorithms on low-resource devices. The combination of high-speed, low-power electronic hardware and efficient AI algorithms is driving the emergence of on-device AI. Deep neural networks (DNNs) are highly effective AI algorithms used for identifying patterns in complex data. DNNs, however, contain many parameters and operations that make them computationally intensive to execute. Accordingly, DNNs are usually executed on high-resource backend processors. This causes an increase in data processing latency and energy expenditure. Therefore, modern strategies are being developed to facilitate the implementation of DNNs on devices with limited resources. This paper presents a detailed review of the current methods and structures that have been developed to deploy DNNs on devices with limited resources. Firstly, an overview of DNNs is presented. Next, the methods used to implement DNNs on resource-constrained devices are explained. Following this, the existing works reported in the literature on the execution of DNNs on low-resource devices are reviewed. The reviewed works are classified into three categories: software, hardware, and hardware/software co-design. Then, a discussion on the reviewed approaches is given, followed by a list of challenges and future prospects of on-device AI, together with its emerging applications.

Artificial Intelligence (AI) has grown extensively in recent times and already encroaches into our lives, for example, with smart cars and smart homes. AI current and future trends in areas such as smart cars, face recognition, robotic surgeries, or automatic disease classification, focus on hybridization to improve the performance of smart expert systems. Despite the huge growth of various AI and machine learning techniques, such as decision trees, data mining, or deep convergence learning, there are still many challenges and threats which limit the performance of these smart expert systems. This special issue focuses on new directions in hybrid AI and machine learning methodologies to address current research problems. It focuses on bringing together leading-edge articles on hybrid AI techniques for smart expert systems for various applications. From about 25 articles submitted, 4 papers were selected based on the review process. Each paper was reviewed by at least two independent reviewers and undertook at least one round of review. Their contributions are summarized below. Raghavendra et al. (2019) has proposed a research work on accurate and early detection of breast cancer using effective imaging modalities with medical image analysis. The research results have shown that two-dimensional synthesized mammogram imaging and conventional full-field digital mammography (FFDM) are two important imaging modalities which can be used for screening breast cancer. The developed method achieved an average performance of 92.9% accuracy using a probabilistic neural network classifier for FFDM images with tenfold cross validation. The obtained results can be used as a distinct system in rural hospitals. Gupta et al. (2020) has done their research studies on to detect infected leaves using machine learning techniques. An improved artificial plant optimization (IAPO) algorithm using machine learning has been introduced that identifies the plant diseases and categorize the leaves into healthy and infected on a private data set of 236 images. The degree of infection is eventually computed, and the leaves with infection greater than a certain calculated threshold are classified as infected leaves. The results show that IAPO can be used for the classification of infected and healthy leaves and this algorithm can be generalized to solve problems in other domains as well. Hammad et al. (2020) has found a new authentication approach based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals. This work shows two novel deep neural network (DNN) models (convolutional neural network (CNN) and ResNet-Attention) using ECG signals for human authentication and the proposed CNN algorithm achieved an accuracy of 98.59% and 99.72% using PTB and CYBHi, respectively. The results obtained by the developed model proved that the performance is better than existing algorithms and can be used in real-time authentication systems after the validation with more diverse ECG data. Teles et al. (2020) has devised a decision support system on credit operations which can provide different estimations of expected recovery based on the same data sets. This research work classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. Results reveal that while linear regression can be used for the prediction of a continuous variable in the credit process using collateral, it is not suitable for categorical data. Out of linear and logistic regressions, linear regression is more applicable in determining groups of variables that can significantly predict an outcome. We would like to take this opportunity to thank Dr. Jon G. Hall, Editor-in-Chief of Expert Systems, The Journal of Knowledge Engineering, for the privilege to edit this special issue. Gustavo Ramírez González is a professor at department of telematics engineering in Universidad del Cauca, Colombia. His research interests include image processing, secure communication, and machine learning. He has published several research papers in reputed journals and served as a Guest Editor for several Special Issues at many journals.

The rapid proliferation of artificial intelligence (AI) technologies across various industries and decision-making processes has undeniably transformed the way of approaching complex problems and tasks. AI systems have proven their prowess in areas such as healthcare, finance, and autonomous systems, revolutionizing how decisions are made. Nevertheless, this proliferation of AI has raised critical concerns regarding the transparency, accountability, and fairness of these systems, as many of the state-of-the-art AI models often resemble complex black boxes. These intricate models, particularly deep learning neural networks, harbor non-linear relationships that are difficult for human users to decipher, thereby raising concerns about bias, fairness, and overall trustworthiness in AI-driven decisions. The urgency of this issue is underscored by the realization that AI should not merely be accurate; it should also be interpretable. Explainable AI (XAI) has emerged as a vital field of research, emphasizing the development of models and techniques that render AI systems comprehensible and transparent in their decision-making processes. This paper investigates into the relevance and significance of XAI across various domains, including healthcare, finance, and autonomous systems, where the ability to understand the rationale behind AI decisions is paramount. In healthcare, where AI assists in diagnosis and treatment, the interpretability of AI models is crucial for clinicians to make informed decisions. In finance, applications like credit scoring and investment analysis demand transparent AI to ensure fairness and accountability. In the realm of autonomous systems, transparency is indispensable to guarantee safety and compliance with regulations. Moreover, government agencies in areas such as law enforcement and social services require interpretable AI to maintain ethical standards and accountability. This paper also highlights the diverse array of research efforts in the XAI domain, spanning from model-specific interpretability methods to more general approaches aimed at unveiling complex AI models. Interpretable models like decision trees and rule-based systems have gained attention for their inherent transparency, while integrating explanation layers into deep neural networks strives to balance accuracy with interpretability. The study emphasizes the significance of this burgeoning field in bridging the gap between AI's advanced capabilities and human users' need for comprehensible AI systems. It seeks to contribute to this field by exploring the design, development, and practical applications of interpretable AI models and techniques, with the ultimate goal of enhancing the trust and understanding of AI-driven decisions.

The past 5 years have seen an explosion of interest in the use of artificial intelligence (AI) and machine learning techniques in medicine. This has been driven by the development...

In this final set of explorations/meditations (of three), we examine the requirements for developing artificial general intelligence (AGI) through the lens of human cognitive architecture, with particular emphasis on the role of narrative selfhood and social cognition. Drawing on perspectives from cognitive science, philosophy of mind, and artificial intelligence research, we critically evaluate current claims about the capabilities of large language models, particularly regarding their purported achievements of theory of mind and self-awareness. We argue that genuinely human-like artificial intelligence may require more than sophisticated pattern recognition and language modeling, potentially necessitating the development of coherent narrative self-models and rich causal understanding. Special attention is given to the relationship between consciousness, conscience, and trustworthy AI systems, suggesting that meaningful artificial intelligence may require forms of richly-embodied and socially-embedded development to achieve robust and reliable functionality. We conclude by proposing that the path to artificial general intelligence may require recapitulating aspects of human cognitive development, particularly regarding the construction of narrative identity and social-moral reasoning capabilities. This analysis has implications for both the technical development of AI systems and the ethical frameworks through which we evaluate artificial minds.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

PurposeTo be more effective, artificial intelligence (AI) requires a broad overall view of the design and transformation of enterprise architecture and capabilities. Maturity models (MMs) are the recognized tools to identify strengths and weaknesses of certain domains of an organization. They consist of multiple, archetypal levels of maturity of a certain domain and can be used for organizational assessment and development. In the case of AI, quite a few numbers of MMs have been proposed. Generally, the links between AI technology, AI usage and organizational performance stay unclear. To address these gaps, this paper aims to introduce the complete details of the AI maturity model (AIMM) for AI-driven platform companies. The associated AI-Driven Platform Enterprise Maturity framework proposed here can help to achieve most of the AI-driven platform companies' objectives.Design/methodology/approachQualitative research is performed in two stages. In the first stage, a review of the existing literature is performed to identify the types, barriers, drivers, challenges and opportunities of MMs in AI, Advanced Analytics and Big Data domains. In the second stage, a research framework is proposed to align company value chain with AI technologies and levels of the platform enterprise maturity.FindingsThe paper proposes a new five level AI-Driven Platform Enterprise Maturity framework by constructing a formal organizational value chain taxonomy model that explains a vast group of MM phenomena related with the AI-Driven Platform Enterprises. In addition, this study proposes a clear and precise description and structuring of the information in the multidimensional Platform, AI, Advanced Analytics and Big Data domains. The AI-Driven Platform Enterprise Maturity framework assists in identification, creation, assessment and disclosure research of AI-driven platform business organizations.Research limitations/implicationsThis research is focused on the basic dimensions of AI value chain. The full reference model of AI consists of much more concepts. In the last few years, AI has achieved a notable drive that, if connected appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in machine learning, especially in deep neural networks, the entire community stands in front of the barrier of explainability. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models in industry. Our prospects lead toward the concept of a methodology for the large-scale implementation of AI methods in platform organizations with fairness, model explainability and accountability at its core.Practical implicationsAI-driven platform enterprise maturity framework can be used for better communicate to clients the value of AI capabilities through the lens of changing human-machine interactions and in the context of legal, ethical and societal norms.Social implicationsThe authors discuss AI in the enterprise platform stack including talent platform, human capital management and recruiting.Originality/valueThe AI value chain and AI-Driven Platform Enterprise Maturity framework are original and represent an effective tools for assessing AI-driven platform enterprises.

Language is a tool for instruction and expressing perspectives (Turetsky et al, 2021). This study aimed to explore the ideologies navigated through translanguaging in Pakistani institutions where English Language instruction is provided as an extra language (EAL). Following an exploratory sequential mixed method design, a survey of 31 items was administered to the language teachers n=100 after conducting non-participant observations of 480 minutes. For this study, the Ideological Model of Literacy (Curiel and Ponzio, 2021) was chosen as the conceptual framework. Observations revealed the involuntary navigation of primarily seven ideologies in the Pakistani classrooms. Quantitative data were analysed through SPSS 20.0, revealing that political ideologies and world Englishes ideologies secured the lowest average score of 2.34. However, racio-linguistic and English-only ideologies were averaged as 3.34. The results indicated that, in contrast, both multilingual ideologies and imperialistic ideologies had a higher mean score of 4.5. This study highlighted learners’ awareness of how their EAL instructors can navigate language ideologies through Translanguaging. References Aghai, L., Sayer, P., & Vercellotti, M. L. (2020). Effects of teachers’ language ideologies on language learners’ translanguaging practices in an intensive English program. In Envisioning TESOL through a translanguaging lens: Global perspectives (pp. 345–362). Agwu Udu, D., Nmadu, J., Uwaleke, C. C., Anudu, A. P., Chukwunonso Okechineke, B., Attamah, P. C., ... & Ogonna, O. C. (2022). Innovative pedagogy and improvement of students’ knowledge retention in science education: Learning activity package instructional approach. Pertanika Journal of Social Sciences & Humanities, 30(3). Ali, I., Azim, M. U., & Rehman, A. U. (2024). Translanguaging as a tool to decolonise English language teaching in Pakistan: Opportunities and challenges. Pakistan Social Sciences Review, 8(1), 246–254. Al-Mutairi, M. A. (2020). Kachru’s three concentric circles model of English language: An overview of criticism & the place of Kuwait in it. English Language Teaching, 13(1), 85–88. Alsuwayhiri, M. M. (2024). The use of translation in English for specific academic purposes classes in Saudi Arabia (Doctoral dissertation, University of Glasgow). Atta, A. (2024). Prospective prognostication: An examination of translanguaging in Pakistan’s educational landscape through the lens of teachers’ and students’ perceptions. Language Teaching Research. https://doi.org/10.1177/13621688241259640 Cárdenas Curiel, L., & Ponzio, C. M. (2021). Imagining multimodal and translanguaging possibilities for authentic cultural writing experiences. Journal of Multilingual Education Research, 11(1), 6. Cenoz, J., & Gorter, D. (2021). Pedagogical Translanguaging. Cambridge University Press. Charalambous, P., Charalambous, C., & Zembylas, M. (2016). Troubling Translanguaging: Language ideologies, superdiversity and interethnic conflict. Applied Linguistics Review, 7(3), 327–352. Cushing, I. (2023). “Miss, can you speak English?”: Raciolinguistic ideologies and language oppression in initial teacher education. British Journal of Sociology of Education, 44(5), 896–911. Fang, F., Jiang, L., & Yang, J. (2023). To impart knowledge or to adhere to policy: Unpacking language ideologies and practices in Chinese EMI courses through a translanguaging lens. Language Teaching Research. https://doi.org/10.1177/13621688231183771 Farahani, M. F., & Maleki, M. (2014). A survey on tendency toward curriculum ideologies among academic board members in educational sciences faculties – Tehran 2010–11. Procedia - Social and Behavioral Sciences, 116, 2392–2396. Galloway, N., & Ruegg, R. (2020). The provision of student support on English medium instruction programmes in Japan and China. Journal of English for Academic Purposes, 45, 100846. Henry, J. (2023). “Say a sentence”: Drawing an interactional link between organisations, language ideologies, and coloniality. Signs and Society, 11(1), 93–114. Hopf, A. (2023). Urdu language ideologies and Pakistani identity. In Language ideologies and the vernacular in colonial and postcolonial South Asia (pp. 34–56). Routledge India. Iversen, J. Y. (2021). Negotiating language ideologies: Pre-service teachers’ perspectives on multilingual practices in mainstream education. International Journal of Multilingualism, 18(3), 421–434. Kachru, B. B. (1996, November). Opening borders with world Englishes: Theory in the classroom. In On JALT96: Crossing borders. The proceedings of the 23rd annual JALT international conference on language teaching/learning, Hiroshima, Japan (pp. 10–20). Khan, I. U., Rahman, G., & Hamid, A. (2021). Poststructuralist perspectives on language and identity: Implications for English language teaching research in Pakistan. SJESR, 4(1), 257–267. Koyama, J., & Kasper, J. (2022). Transworlding and translanguaging: Negotiating and resisting monoglossic language ideologies, policies, and pedagogies. Linguistics and Education, 70, 101010. Mazak, C. M., & Herbas-Donoso, C. (2014). Translanguaging practices and language ideologies in Puerto Rican university science education. Critical Inquiry in Language Studies, 11(1), 27–49. Nadarajan, K., Abdullah, A. H., Alhassora, N. S. A., Ibrahim, N. H., Surif, J., Ali, D. F., ... & Hamzah, M. H. (2022). The effectiveness of a technology-based isometrical transformation flipped classroom learning strategy in improving students’ higher order thinking skills. IEEE Access, 11, 4155–4172. Naheed, M., Azim, M. U., Islam, M., & Imdad, K. (2024). Investigating the necessity of translanguaging in EFL classroom at university level: Perspective of teachers and students. Harf-o-Sukhan, 8(1), 474–487. Paulsrud, B., & Rosén, J. (2020). Translanguaging and language ideologies in education: Northern and Southern perspectives. In Handbook of the changing world language map (pp. 3533–3547). Sahan, K., Rose, H., Paulsrud, B., Tian, Z., & Toth, J. (2021). Problematising the E in EMI: Translanguaging as a pedagogic alternative to English-only hegemony in university contexts. In English-medium instruction and translanguaging (pp. 1–14). Sahar, Y., & Shahbaz, M. (2023). Scope of e-portfolio-based assessment tool: Perceptions and practices of ELT practitioners in Pakistan. International Journal of Innovation in Teaching and Learning (IJITL), 9(1), 75–90. Sahar, Y., Ali, M., & Hussain, M. S. (2024). Emergent bilingual learners’ target language development: A comparison between government policies and school practices. International Journal of Innovation in Teaching and Learning (IJITL), 10(1), 83–98. Strijker, D., Bosworth, G., & Bouter, G. (2020). Research methods in rural studies: Qualitative, quantitative and mixed methods. Journal of Rural Studies, 78, 262–270. Syed, H. (2022). “I make my students’ assignments bleed with red circles”: An autoethnography of translanguaging in higher education in Pakistan. Annual Review of Applied Linguistics, 42, 119–126. Syed, H. (2024). Unravelling the deficit ideologies in English language education in Pakistan: A decolonial perspective. TESOL Journal, e828. Turetsky, K. M., Sinclair, S., Starck, J. G., & Shelton, J. N. (2021). Beyond students: How teacher psychology shapes educational inequality. Trends in Cognitive Sciences, 25(8), 697–709. Uysal, H., & Sah, P. K. (2024). Language ideologies and language teaching in the global world: An introduction to the special issue. International Journal of Bilingualism. https://doi.org/10.1177/13670069241240964 Wei, L., & García, O. (2022). Not a first language but one repertoire: Translanguaging as a decolonising project. RELC Journal, 53(2), 313–324.