Seeking PreciseProtein-like Functions from RandomHeteropolymer Ensemble and through Dimensionality Reduction

Recently, many researchers are intensely engaged in investigation on the artificial intelligence technology that recognizes, learns, inferences, and acts on external information in a wide range of fields by combining technologies of computing, big data and machine learning algorithms. The artificial intelligence technology is currently used in almost all industries, and many machine learning experts are working on integrating and standardizing various machine learning tools so that non-experts can easily apply them to their domain. The researchers are also studying an autonomous machine learning as well as ontology construction for standardizing the machine learning concepts. In this paper, we classify typical problem-solving steps for autonomous machine learning as tasks, and present a problem-solving process. We propose the modeling method of an autonomous machine learning using a process of the task execution on machine learning such as workflow. The proposed task ontology-based machine learning model de fines a task-based process grouping scheme of UML activities. And it will automatically generate and extend the machine learning models by transformation rules based on common elements and structures (relationships and processes between elements).

Recently, many researchers are intensely engaged in investigation on the artificial intelligence technology that recognizes, learns, inferences, and acts on external information in a wide range of fields by combining technologies of computing, big data and machine learning algorithms. The artificial intelligence technology is currently used in almost all industries, and many machine learning experts are working on integrating and standardizing various machine learning tools so that non-experts can easily apply them to their domain. The researchers are also studying an autonomous machine learning as well as ontology construction for standardizing the machine learning concepts. In this paper, we classify typical problem solving steps for autonomous machine learning as tasks, and present a problem solving process. We propose the modeling method of an autonomous machine learning using a processes of the task execution on machine learning such as workflow. The proposed task ontology-based machine learning model defines a task-based process grouping scheme of UML activities. And it will automatically generate and extend the machine learning models by transformation rules based on common elements and structures (relationships and processes between elements).

RAI: Rapid, Autonomous and Intelligent machine learning approach to identify fire-vulnerable bridges

The recovery of freshly fallen meteorites from tracked and triangulated meteors is critical to determining their source asteroid families. Even though our ability to locate meteorite falls continues to improve, the recovery of meteorites remains a challenge due to large search areas with terrain and vegetation obscuration. To improve the efficiency of meteorite recovery, we have tested the hypothesis that meteorites can be located using machine learning techniques and an autonomous drone. To locate meteorites autonomously, a quadcopter drone first conducts a grid survey acquiring top‐down images of the strewn field from a low altitude. The drone‐acquired images are then analyzed using a machine learning classifier to identify meteorite candidates for follow‐up examination. Here, we describe a proof‐of‐concept meteorite classifier that deploys off‐line a combination of different convolution neural networks to recognize meteorites from images taken by drones in the field. The system was implemented in a conceptual drone setup and tested in the suspected strewn field of a recent meteorite fall near Walker Lake, Nevada.

This article discusses the regulation of artificial intelligence from a Jewish perspective, with an emphasis on the regulation of machine learning and its application to autonomous vehicles and machine learning. Through the Biblical story of Adam and Eve as well as Golem legends from Jewish folklore, we derive several basic principles that underlie a Jewish perspective on the moral and legal personhood of robots and other artificially intelligent agents. We argue that religious ethics in general, and Jewish ethics in particular, show us that the dangers of granting moral personhood to robots and in particular to autonomous vehicles lie not in the fact that they lack a soul—or consciousness or feelings or interests—but because to do so weakens our own ability to develop as fully autonomous legal and moral persons. Instead, we argue that existing legal persons should continue to maintain legal control over artificial agents, while natural persons assume ultimate moral responsibility for choices made by artificial agents they employ in their service. In the final section of the article we discuss the trolley dilemma in the context of governing autonomous vehicles and sketch out an application of Jewish ethics in a case where we are asking Artificial Intelligence to make life and death decisions. Our novel contribution is two-fold; first, we bring a religious approach to the discussion of the ethics of Artificial Intelligence which has hitherto been dominated by secular Western philosophies; second, we raise the idea that artificial entities who are trained through machine learning can be ethically trained in much the same way that human are—through reading and reflecting on core religious texts. This is both a way of ensuring the ethical regulation of artificial intelligence, but also promotes other core values of regulation, such as democratic engagement and user choice.

From the Tree of Knowledge & the Golem of Prague: The Ethics of Artificial Intelligence Through Jewish Eyes

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. The ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human-computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems.

Current approaches to AI and machine learning are still fundamentally limited in comparison with autonomous learning capabilities of children. What is remarkable is not that some children become world champions in certain games or specialties: it is rather their autonomy, flexibility and efficiency at learning many everyday skills under strongly limited resources of time, computation and energy. And they do not need the intervention of an engineer for each new task (e.g. they do not need someone to provide a new task specific reward function). XX I will present a research program that has focused on computational modeling of child development and learning mechanisms in the last decade. I will discuss several developmental forces that guide exploration in large real world spaces, starting from the perspective of how algorithmic models can help us understand better how they work in humans, and in return how this opens new approaches to autonomous machine learning. XX In particular, I will discuss models of curiosity-driven autonomous learning, enabling machines to sample and explore their own goals and their own learning strategies, self-organizing a learning curriculum without any external reward or supervision. XX I will show how this has helped scientists understand better aspects of human development such as the emergence of developmental transitions between object manipulation, tool use and speech. I will also show how the use of real robotic platforms for evaluating these models has led to highly efficient unsupervised learning methods, enabling robots to discover and learn multiple skills in high-dimensions in a handful of hours. I will discuss how these techniques are now being integrated with modern deep learning methods. XX Finally, I will show how these models and techniques can be successfully applied in the domain of educational technologies, enabling to personalize sequences of exercises for human learners, while maximizing both learning efficiency and intrinsic motivation. I will illustrate this with a large-scale experiment recently performed in primary schools, enabling children of all levels to improve their skills and motivation in learning aspects of mathematics.

The material in this document aligns with SCSC-153B which was released February 2022. During the development of ”Safety Assurance Objectives for Autonomous Systems” a number of comparisons were performed other draft guidance documents produced for safety assurance of Autonomous Systems (AS) and Machine Learning (ML). These comparisons were performed to ensure that either the assurance objectives in SCSC-153B matched elements of the alternative guidelines, or, where there were omissions, these were justified when considering the purpose and scope of SCSC-153B. Each comparison differs, depending on the content, structure, and scope of the alternative document. The individual approaches are described in more detail for each appendix. The material has been removed from subsequent editions of SCSC-153B as confidence in the content of that document has been established. However, informal activities within the SASWG will still continue to scan for relevant AS and ML assurance issues, and review new standards and guidelines to ensure validity and currency of the SCSC-153X objectives. The material presented here will not be maintained for any uplifts of the comparison documents or approaches.

Global food security remains fundamentally dependent on agricultural systems. Escalating demographic pressures have intensified demands on food production networks, compelling a shift from traditional agrarian practices toward technologically sophisticated methodologies under the Agriculture 4.0 paradigm. Optimizing the benefits of this transformation requires resolving implementation constraints across technological and socio-economic dimensions. This empirical investigation advances the Agriculture 4.0 discourse through systematic analysis of emergent digital farming innovations. Employing PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standardized protocols, we conducted a decade-long assessment of peer-reviewed crop production research. From 148 methodologically selected publications, we evaluated technology adoption patterns across three analytical vectors: service typology, implementation maturity, and production system classification. Key findings demonstrate concentrated scholarly focus on autonomous robotics, IoT architectures, and machine learning implementations. Production environment analysis revealed disproportionate research attention: open-field systems represented 69% of examined cases, while controlled-environment agriculture constituted 31%. Most applications (71%) remain at prototype development stages. The study further codifies critical digitization barriers through multidimensional taxonomy, offering both a comprehensive assessment of current technological integration and a foundation for strategic advancement in precision agriculture.

In today’s world, there is an increasing demand for environmental monitoring, surveillance, and oceanographic research, which poses challenges in improving energy efficiency and data transfer reliability in Acoustic Sensor Networks. Existing methods face hurdles due to limited energy resources and unreliable data transmission. We propose a Reliable and Energy-Efficient Framework with Sink Mobility (REEFSM) to address these issues. This framework optimizes energy consumption and enhances data reliability by incorporating advanced energy management strategies such as adaptive duty cycling and efficient data transmission mechanisms by minimizing forwarding nodes. Simulation results demonstrate that REEFSM reduces energy consumption by up to 43% and increases data reliability by 35% compared to protocols like EERBCR and DEADS. REEFSM ensures zero dead nodes, minimizes packet drops, and maintains high data accuracy throughout the simulation. This research outcome provides a sustainable and reliable solution for energy-efficient data collection in underwater environments. The future research directions, including integrating autonomous path planning, energy harvesting, and machine learning techniques, hold great potential for further advancements in the field.

Continual locomotion learning faces four challenges: incomprehensibility, sample inefficiency, lack of knowledge exploitation, and catastrophic forgetting. Thus, this work introduces growable online locomotion learning under multicondition (GOLLUM), which exploits the interpretability feature to address the aforementioned challenges. GOLLUM has two dimensions of interpretability: layer-wise interpretability for neural control function encoding and column-wise interpretability for robot skill encoding. With this interpretable control structure, GOLLUM utilizes neurogenesis to unsupervisely increment columns (ring-like networks); each column is trained separately to encode and maintain a specific primary robot skill. GOLLUM also transfers the parameters to new skills and supplements the learned combination of acquired skills through another neural mapping layer added (layer-wise) with online supplementary learning. On a physical hexapod robot, GOLLUM successfully acquired multiple locomotion skills (e.g., walking, slope climbing, and bouncing) autonomously and continuously within an hour using a simple reward function. Furthermore, it demonstrated the capability of combining previous learned skills to facilitate the learning process of new skills while preventing catastrophic forgetting. Compared to state-of-the-art locomotion learning approaches, GOLLUM is the only approach that addresses the four challenges above mentioned without human intervention. It also emphasizes the potential exploitation of interpretability to achieve autonomous lifelong learning machines.

Continual and Autonomous Machine Learning

Cognitive linguistics is an emerging discipline that studies the cognitive properties of natural languages and the cognitive models of languages in computational linguistics, cognitive computing, and computational intelligence. This paper presents the theoretical framework of cognitive linguistics in order to systematically formalize the syntaxes and grammars of natural languages. An abstract language model of cognitive linguistics is created at the top level. Based on it, the cognitive structures of languages at the levels of lexis, phrase, clauses, sentence, paragraph, and essay are formally modeled from the bottom up. Using contemporary denotational mathematics, the deductive grammar of English is formally modeled and rigorously analyzed. The basic research provides support for a wide range of applications in computational linguistics, cognitive informatics, online text processing, web search engines, machine language comprehension, autonomous machine learning, smart cell phones, semantic computing, and computing with words.