Social intelligence, human intelligence and niche construction

This paper is about the evolution of hominin intelligence. I agree with defenders of the social intelligence hypothesis in thinking that externalist models of hominin intelligence are not plausible: such models cannot explain the unique cognition and cooperation explosion in our lineage, for changes in the external environment (e.g. increasing environmental unpredictability) affect many lineages. Both the social intelligence hypothesis and the social intelligence-ecological complexity hybrid I outline here are niche construction models. Hominin evolution is hominin response to selective environments that earlier hominins have made. In contrast to social intelligence models, I argue that hominins have both created and responded to a unique foraging mode; a mode that is both social in itself and which has further effects on hominin social environments. In contrast to some social intelligence models, on this view, hominin encounters with their ecological environments continue to have profound selective effects. However, though the ecological environment selects, it does not select on its own. Accidents and their consequences, differential success and failure, result from the combination of the ecological environment an agent faces and the social features that enhance some opportunities and suppress others and that exacerbate some dangers and lessen others. Individuals do not face the ecological filters on their environment alone, but with others, and with the technology, information and misinformation that their social world provides.     

Cognitive ornithology: the evolution of avian intelligence

Comparative psychologists interested in the evolution of intelligence have focused their attention on social primates, whereas birds tend to be used as models of associative learning. However, corvids and parrots, which have forebrains relatively the same size as apes, live in complex social groups and have a long developmental period before becoming independent, have demonstrated ape-like intelligence. Although, ornithologists have documented thousands of hours observing birds in their natural habitat, they have focused their attention on avian behaviour and ecology, rather than intelligence. This review discusses recent studies of avian cognition contrasting two different approaches; the anthropocentric approach and the adaptive specialization approach. It is argued that the most productive method is to combine the two approaches. This is discussed with respects to recent investigations of two supposedly unique aspects of human cognition; episodic memory and theory of mind. In reviewing the evidence for avian intelligence, corvids and parrots appear to be cognitively superior to other birds and in many cases even apes. This suggests that complex cognition has evolved in species with very different brains through a process of convergent evolution rather than shared ancestry, although the notion that birds and mammals may share common neural connectivity patterns is discussed.     

Computational intelligence approaches for pattern discovery in biological systems

Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.     

Abstraction and reformulation in artificial intelligence

This paper contributes in two ways to the aims of this special issue on abstraction. The first is to show that there are compelling reasons motivating the use of abstraction in the purely computational realm of artificial intelligence. The second is to contribute to the overall discussion of the nature of abstraction by providing examples of the abstraction processes currently used in artificial intelligence. Although each type of abstraction is specific to a somewhat narrow context, it is hoped that collectively they illustrate the richness and variety of abstraction in its fullest sense.     

Creative dynamics approach to neural intelligence

The thrust of this paper is to introduce and discuss a substantially new type of dynamical system for modelling biological behavior. The approach was motivated by an attempt to remove one of the most fundamental limitations of artificial neural networks — their rigid behavior compared with even simplest biological systems. This approach exploits a novel paradigm in nonlinear dynamics based upon the concept of terminal attractors and repellers. It was demonstrated that non-Lipschitzian dynamics based upon the failure of Lipschitz condition exhibits a new qualitative effect — a multi-choice response to periodic external excitations. Based upon this property, a substantially new class of dynamical systems — the unpredictable systems — was introduced and analyzed. These systems are represented in the form of coupled activation and learning dynamical equations whose ability to be spontaneously activated is based upon two pathological characteristics. Firstly, such systems have zero Jacobian. As a result of that, they have an infinite number of equilibrium points which occupy curves, surfaces or hypersurfaces. Secondly, at all these equilibrium points, the Lipschitz conditions fails, so the equilibrium points become terminal attractors or repellers depending upon the sign of the periodic excitation. Both of these pathological characteristics result in multi-choice response of unpredictable dynamical systems. It has been shown that the unpredictable systems can be controlled by sign strings which uniquely define the system behaviors by specifying the direction of the motions in the critical points. By changing the combinations of signs in the code strings the system can reproduce any prescribed behavior to a prescribed accuracy. That is why the unpredictable systems driven by sign strings are extremely flexible and are highly adaptable to environmental changes. It was also shown that such systems can serve as a powerful tool for temporal pattern memories and complex pattern recognition. It has been demonstrated that new architecture of neural networks based upon non-Lipschitzian dynamics can be utilized for modelling more complex patterns of behavior which can be associated with phenomenological models of creativity and neural intelligence.     

Recent trends in stem cell-based therapies and applications of artificial intelligence in regenerative medicine

Stem cells are undifferentiated cells that can self-renew and differentiate into diverse types of mature and functional cells while maintaining their original identity. This profound potential of stem cells has been thoroughly investigated for its significance in regenerative medicine and has laid the foundation for cell-based therapies. Regenerative medicine is rapidly progressing in healthcare with the prospect of repair and restoration of specific organs or tissue injuries or chronic disease conditions where the body’s regenerative process is not sufficient to heal. In this review, the recent advances in stem cell-based therapies in regenerative medicine are discussed, emphasizing mesenchymal stem cell-based therapies as these cells have been extensively studied for clinical use. Recent applications of artificial intelligence algorithms in stem cell-based therapies, their limitation, and future prospects are highlighted.     

Prospects for the biology of human intelligence

Despite the ubiquitous nature of Spearman's g in mental test performance, the charge «intelligence is what intelligence tests test» has not been countered in a satisfactory way. It is proposed that there are two ways to answer this complaint. The first concerns the new hypothesis testing models in factor analysis. The second involves studying the ‘biology of intelligence’. The biology of intelligence has various meanings and four are discussed: biology as theory; biology as race and genetics; biology as neurobiology; and biology as basic psychological processes. The last of these is considered in some detail and it is found that reaction time, evoked potentials and inspection time offer bright prospects for further research on the biology of psychometric intelligence.     

Artificial intelligence in sleep analysis (ARTISANA)--modelling visual processes in sleep classification]

We describe a novel approach to the problem of automated sleep stage recognition. The ARTISANA algorithm mimics the behaviour of a human expert visually scoring sleep stages (Rechtschaffen and Kales classification). It comprises a number of interacting components that imitate the stepwise approach of the human expert, and artificial intelligence components. On the basis of parameters extracted at 1-s intervals from the signal curves, artificial neural networks recognize the incidence of typical patterns, e.g. delta activity or K complexes. This is followed by a rule interpretation stage that identifies the sleep stage with the aid of a neuro-fuzzy system while taking account of the context. Validation studies based on the records of 8 patients with obstructive sleep apnoea have confirmed the potential of this approach. Further features of the system include the transparency of the decision-taking process, and the flexibility of the option for expanding the system to cover new patterns and criteria.     

抗菌肽结构改造与人工智能研发策略

抗菌肽是一类广泛存在于生物体内的小分子肽,参与构成生物体先天免疫,可以有效抵抗病原微生物的入侵。抗菌肽具有广谱抗菌活性,且不易产生耐药性等特点,在治疗感染性疾病方面具有独特的优势,有望成为理想的抗感染药物。然而,由于部分抗菌肽尚存在稳定性差、毒性高等问题,限制了抗菌肽的广泛应用。由于人工智能算法能有效合成具有高稳定性、低毒性的抗菌肽,在探索天然抗菌肽中展现了巨大的潜力,因此本文简述了抗菌肽的抗菌机制、结构改造以及利用机器学习和深度学习等人工智能算法进行新型抗菌肽研发的优化策略,以期为抗菌肽结构优化及研发提供新思路。     

Levels and loops: the future of artificial intelligence and neuroscience

In discussing artificial intelligence and neuroscience, I will focus on two themes. The first is the universality of cycles (or loops): sets of variables that affect each other in such a way that any feed-forward account of causality and control, while informative, is misleading. The second theme is based around the observation that a computer is an intrinsically dualistic entity, with its physical set-up designed so as not to interfere with its logical set-up, which executes the computation. The brain is different. When analysed empirically at several different levels (cellular, molecular), it appears that there is no satisfactory way to separate a physical brain model (or algorithm, or representation), from a physical implementational substrate. When program and implementation are inseparable and thus interfere with each other, a dualistic point-of-view is impossible. Forced by empiricism into a monistic perspective, the brain-mind appears as neither embodied by or embedded in physical reality, but rather as identical to physical reality. This perspective has implications for the future of science and society. I will approach these from a negative point-of-view, by critiquing some of our millennial culture's popular projected futures.     

Artificial intelligence in pest insect monitoring

Abstract Global problems of hunger and malnutrition induced us to introduce a new tool for semi‐automated pest insect identification and monitoring: an artificial neural network system. Multilayer perceptrons, an artificial intelligence method, seem to be efficient for this purpose. We evaluated 101 European economically important thrips (Thysanoptera) species: extrapolation of the verification test data indicated 95% reliability at least for some taxa analysed. Mainly quantitative morphometric characters, such as head, clavus, wing, ovipositor length and width, formed the input variable computation set in a Trajan neural network simulator. The technique may be combined with digital image analysis.     

Imaging and artificial intelligence for progression of age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications.     

Analysis and forecasting of airborne pollen–induced symptoms with the aid of computational intelligence methods

Allergies due to airborne pollen affect a considerable percentage of Europeans; thus, the provision of health-related information services concerning pollen-induced symptoms can improve the overall quality of life. In this paper, we demonstrate the development of personalized, health-related, quality-of-life information services by adopting a data-driven approach. The data we use consist of allergic symptoms reported by people as well as detailed pollen count information of the most allergenic taxa. We apply computational intelligence methods in order to analyze symptoms, identify possible interrelationships with several pollen taxa and develop models that associate pollen count levels with allergic symptoms on a personal level. The results for the case of Austria show that this approach can lead to accurate personalized symptom forecasting models; we report an average correlation coefficient of r = 0.70 for a sample of 102 users of the Patients Hayfever Diary. We conclude that some of these models could serve as the basis for personalized health information services.     

Artificial intelligence in molecular de novo design: Integration with experiment

In this mini review, we capture the latest progress of applying artificial intelligence (AI) techniques based on deep learning architectures to molecular de novo design with a focus on integration with experimental validation. We will cover the progress and experimental validation of novel generative algorithms, the validation of QSAR models and how AI-based molecular de novo design is starting to become connected with chemistry automation. While progress has been made in the last few years, it is still early days. The experimental validations conducted thus far should be considered proof-of-principle, providing confidence that the field is moving in the right direction.     

Animal learning and intelligence

The ability to learn is common to most animal species: the need to exploit past experience being obviously extremely important for survival, many animals have evolved ways of coping with it. Although the complexity of learning needed for optimal survival may be different in different species, the basic mechanisms appear to be fairly constant even in phylogenetically distant ones. This homogeneity across species in learning mechanisms is in some ways surprising in view of the large phylogenetic differences and of the considerable variability not only in the general plan of their bodily structures, but also, more specifically, in their neural organization and in their behavioral adaptations. One possible explanation is that animals have acquired learning very precociously, and that the original and basic mechanisms have proved so efficient and faultproof as to be preserved from then on without any significant modification. Most researchers of the subject seem to accept the equation «intelligence=learning capability», operationally very useful because it leads to a variety of formal tests. Some researchers, stressing that behavior is subject to the same evolutionary principles as any other character of the organism and acknowledging some problems in the accepted laws of learning, have tried to find a satisfactory answer to the question of animal intelligence by attempting a synthesis between the concepts of animal learning psychology and those of ethology. To some extent, dissatisfaction with established learning theories originated within the theories themselves: the study of phenomena such as autoshaping, selective attention, preferential learning of some responses amongst the many possible, conditioned learning of taste aversions, etc. Further difficulties for conditioning theories arose from the discovery of ethological phenomena. Other researchers have attempted to check the hypothesis that animals possess cognition. A number of complex experimental situtations have been devised to this purpose, but the results still are far from conclusive.     

Distinct neurocognitive strategies for comprehensions of human and artificial intelligence

Although humans have inevitably interacted with both human and artificial intelligence in real life situations, it is unknown whether the human brain engages homologous neurocognitive strategies to cope with both forms of intelligence. To investigate this, we scanned subjects, using functional MRI, while they inferred the reasoning processes conducted by human agents or by computers. We found that the inference of reasoning processes conducted by human agents but not by computers induced increased activity in the precuneus but decreased activity in the ventral medial prefrontal cortex and enhanced functional connectivity between the two brain areas. The findings provide evidence for distinct neurocognitive strategies of taking others' perspective and inhibiting the process referenced to the self that are specific to the comprehension of human intelligence.     

Artificial intelligence for compound pharmacokinetics prediction

Optimisation of compound pharmacokinetics (PK) is an integral part of drug discovery and development. Animal in vivo PK data as well as human and animal in vitro systems are routinely utilised to evaluate PK in humans. In recent years machine learning and artificial intelligence (AI) emerged as a major tool for modelling of in vivo animal and human PK, enabling prediction from chemical structure early in drug discovery, and therefore offering opportunities to guide the design and prioritisation of molecules based on relevant in vivo properties and, ultimately, predicting human PK at the point of design. This review presents recent advances in machine learning and AI models for in vivo animal and human PK for small-molecule compounds as well as some examples for antibody therapeutics.     

Artificial intelligence for carbon emissions using system of systems theory

The impact of artificial intelligence (AI) on the environment is the subject of discourse, with arguments for both positive and negative effects. There is a fine line between AI for good and AI for environmental degradation. Today, companies want to seize the benefits of AI, which distinctively involves reducing the company's carbon footprint. However, AI's carbon emissions differ as per the techniques involved in training it. As the saying goes, a coin always has two sides. Therefore, it cannot be denied that AI can be an effective tool for combating climate change, but its role in contributing to carbon emissions cannot be ignored. Multiple studies indicate that AI could be the game-changer in staving off anthropogenic climatic changes due to the deterioration of the environment and global warming. This double-edged relationship and interdependency of AI and carbon emissions are represented through a system of systems (SoS) approach. SoS states that a plan is created through multiple smaller systems, creating complexity in the design and vice versa. A complex system can be assumed as the world in general, where two individual independent systems AI and carbon emissions, when in interaction, create a complex complementary and contradictory relation, adding to the convolution of the system. This connection is demonstrated by conducting a network analysis and calculating the carbon emissions of six machine learning (ML) algorithms and deep learning (DL) models with different datasets but the same hyperparameters on a carbon emission calculator created through AI algorithms. The primary idea of this study is to encourage the AI society to create efficient AI models that may be used without compromising environmental issues. The focus should be on practicing sustainable AI, that is, sustainability from data collection to model deployment, throughout the lifecycle of AI.     

Quantitative microscopy and artificial intelligence: some philosophical reflections

The interdisciplinary field of quantitative microscopy (computer-aided microscopy) and artificial image understanding systems is explored, with an emphasis on the philosophical aspects of pathology and artificial intelligence. Three methodological problems of traditional diagnostic pathology are identified: those of validity, variability and organisation. Quantitative microscopy is a potential research strategy for solving these problems. In practice, however, the quantitative microscopy program is handicapped by the difficulty of building artificial image-understanding systems. We discuss the segmentation problem in image understanding, and four general strategies, three cognitivistic and one connectionistic, are reviewed.     

Enzymes and theoretical biology: sketch of an informational perspective of the cell.

In the theoretical scenarios of biology, new insights can be gained by the introduction of information-processing and artificial intelligence concepts, helping to organize the explanation of the many intra- and inter-cellular phenomena that molecular biology is accumulating. Enzymes contain some of the immediate clues; the whole informational processing of prokaryotic cells is another central subject of search. Additionally, prolonging the informational perspective of the cell, a significant parallel can be drawn between informational processes in biological, social and artificial intelligence systems. A more tangible definition of biological complexity and biological intelligence emerges.