Knowledge Tree
machine learning
Also known as: ML
synthesized from dimensionsMachine learning (ML) is a core branch of artificial intelligence that encompasses computational systems designed to learn from data to make predictions, decisions, or classifications. Unlike deterministic programming, ML models inherently involve uncertainty, requiring rigorous uncertainty quantification to measure confidence in their outputs. The field is fundamentally categorized into three primary paradigms: supervised learning, unsupervised learning, and reinforcement learning ML types by Murphy. These systems often utilize connectionist architectures, such as artificial neural networks, to perform pattern recognition on complex or heterogeneous datasets connectionist AI via neural nets.
The identity of machine learning is defined by its ability to process unstructured inputs and adapt to environmental signals Repeated ML as environment learning. This capability is increasingly augmented by synergies with knowledge representation (KR), which aims to improve data efficiency and model interpretability KR-ML synergy advances AI. Neuro-symbolic AI, a prominent intersection of these fields, allows neural layers to handle perception while symbolic logic manages reasoning, addressing critical limitations in explainability ML explainability issues.
Machine learning is significant for its transformative impact across diverse sectors, including energy management ML definition by Antonopoulos, cybersecurity defensive cybersecurity, and automated fraud detection dynamic fraud adaptation. Its development is heavily driven by open-source ecosystems, which democratize access to powerful frameworks like TensorFlow TensorFlow enables quantum ML. Furthermore, foundational research by figures such as Geoffrey Hinton and John J. Hopfield has been instrumental in establishing the theoretical underpinnings of neural-based learning Hinton-Hopfield Nobel for neural ML.
Despite its utility, the field faces persistent challenges that define its current research trajectory. These include the risk of overfitting, which limits generalization overfitting challenges ML generalization, and the "slippery" nature of model interpretability ML interpretability is slippery. Additionally, the deployment of adaptive models by malicious actors creates an ongoing "AI arms race" in cybersecurity attackers deploy adaptive ML models. To mitigate these issues, researchers are increasingly focusing on error awareness ML emphasizes error awareness, adversarial training advances in adversarial training, and human-in-the-loop methodologies to ensure safety and reliability.
Model Perspectives (8)
openrouter/x-ai/grok-4.1-fast definitive 88% confidence
Machine learning (ML) emerges from the facts as a versatile computational paradigm integral to numerous applications and research areas, often involving model training, prediction, uncertainty handling, and integration with other technologies. According to Springer, uncertainty quantification in ML measures model confidence in predictions. It powers Facebook's ad revenue optimization by analyzing user activity (Parker et al., 2016). Knowledge distillation trains compact models mimicking complex ones for resource-limited settings (AISTATS; Samuel Tesfazgi et al.). Frameworks like TensorFlow, developed by Google, enable building models and democratize AI access (Algocademy). ML intersects with knowledge representation, as seen in growing AI interest in combining ML with KR methods (KR) and special tracks like KR 2026 on KR meets ML. Applications span fake news detection (Braşoveanu and Andonie, IWANN 2019), knowledge graph construction (AI-KG, CovidGraph, etc.), Amazon's warehouse automation via neuro-symbolic AI, and FDA guidelines for ML devices. Researchers like Mikhail Belkin (UCSD, Amazon Scholar) advance ML theory, particularly deep learning phenomena (Skywritings Press). Challenges include LLMs' hallucinations (Cleanlab) and phenomena like accuracy-on-the-line (AISTATS). Human-in-the-loop approaches mitigate uncertainty (Wu et al., 2022; Frontiers).
openrouter/google/gemini-3.1-flash-lite-preview 100% confidence
Machine learning is a transformative technology characterized by its reliance on open-source ecosystems, its role in automating business and security processes, and its dual-edged impact on cybersecurity. Open-source software is a fundamental driver of machine learning innovation, providing accessible tools like TensorFlow and PyTorch that allow developers to build models without starting from scratch democratizing access to technology. This open-source foundation enables researchers to share datasets and models, fostering collaboration without high costs.
In business, machine learning drives value through "data network effects," where larger volumes of data improve model accuracy, creating a competitive lock-in mechanism for platforms like Facebook. Furthermore, the technology is being applied to diverse technical domains, such as predicting the rheological properties of cement-based materials in construction research and modeling metabolite profiles in plant biology.
The role of machine learning in cybersecurity is particularly dynamic. It is increasingly vital for real-time fraud detection using dynamic learning and is expected to automate identity governance and security operations by 2025 reducing the reliance on human analysts. However, this progress creates a "cybersecurity arms race"; according to Avani Desai of Schellman, attackers are utilizing machine learning to adapt and evade traditional defenses in real-time. Finally, the integration of machine learning into software development has introduced new challenges, including the need for advanced Software Bill of Materials (SBOMs) for AI applications and legal uncertainties regarding how traditional open-source licenses apply to AI-generated content and model training.
openrouter/x-ai/grok-4.1-fast definitive 78% confidence
Machine learning (ML) encompasses data-driven systems that inherently carry uncertainty in outputs, distinguishing them from deterministic programs, according to Springer ML models possess uncertainty. Foundational contributions by Geoffrey Hinton, known as the 'godfather of AI', and John J. Hopfield enabled ML via artificial neural networks, recognized with a Nobel Prize per Procogia Hinton-Hopfield Nobel for neural ML. It handles noise and incomplete data complementarily with formal logic, as outlined by Springer logic aids ML on noise. Paradigms include connectionist AI emphasizing neural networks and pattern recognition from datasets (arXiv) connectionist AI via neural nets, and mixture-of-experts models for non-stationary data (AISTATS; Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche) MOE models handle heterogeneity. Challenges persist like overfitting limiting generalization (Skywritings Press) overfitting challenges ML generalization, slippery interpretability (Zachary C. Lipton in Queue) ML interpretability is slippery, and shifting focus to error likelihood (Springer) ML emphasizes error awareness. Open source drives ML innovation (Algocademy) open source fuels ML progress, powering tools like TensorFlow 3.5 for hybrid quantum ML (DEV Community; Vitali Sorenko) TensorFlow enables quantum ML. Synergies with knowledge representation enhance interpretability and data efficiency (KR conference) KR-ML synergy advances AI, while advances like self-supervised learning drive breakthroughs (AISTATS; Tesfazgi et al.) self-supervised boosts ML.
openrouter/x-ai/grok-4.1-fast definitive 78% confidence
Machine learning (ML) is a core area of artificial intelligence characterized by algorithms that learn from data to make predictions or decisions, often framed from a probabilistic perspective as detailed in Murphy's textbook. Its three main types—supervised, unsupervised, and reinforcement learning—form the foundational categorization Murphy 2012. ML techniques embed knowledge graphs into vector spaces for model training knowledge graph embedding and are integral to neuro-symbolic systems where neural layers process unstructured inputs neural perception layer. Key challenges include addressing bias and fairness ML fairness survey, managing uncertainty uncertainty review, and balancing accuracy with explainability via approaches like Concept Embedding Models. Applications span finance, as taught in University of Arkansas courses, cybersecurity for intrusion detection defensive cybersecurity, energy prediction Nanjar et al. review, and fraud detection dynamic fraud adaptation, highlighting its transformative role across sectors per sources like Springer, ITPro Today, and Trends Research & Advisory.
openrouter/x-ai/grok-4.1-fast definitive 78% confidence
Machine learning is characterized as a branch of computational algorithms designed to emulate human intelligence by learning from the surrounding environment, serving as a key tool for addressing challenges in Demand Side Management (DSM) according to Antonopoulos et al. (2020) ML definition by Antonopoulos. Repeated ML as environment learning. Murphy (2012), cited by Springer, classifies its main types as supervised learning, unsupervised learning, and reinforcement learning ML types by Murphy. Specific methods include Long Short-term Memory (LSTM) and Gated Recurrent Units (GRU) for temporal data in energy systems (Springer) LSTM/GRU for time-series, and Conditional Random Fields (CRF) for sequence labeling in entity recognition (arXiv) CRF for entity recognition. Springer sources highlight its role in transforming energy management, optimizing renewable systems with life cycle assessments, and forecasting electricity prices (El-Azab et al. 2024). In cybersecurity, ITPro Today notes AI and machine learning's dual role in empowering attackers and defenders, automating SOC tasks, and advancing threat detection. Broader applications span military intelligence for object classification (Trends Research & Advisory) ML in military intelligence, physiological classification (Frontiers), and intersections with Knowledge Representation (KR) for symbolic learning and explainability (KR conference). Challenges include explainability and bias prompting neuro-symbolic integration (KR; Cogent Infotech) ML explainability issues, open source licensing for training models (FOSSA), and FDA focus on supervised systems (medRxiv).
openrouter/x-ai/grok-4.1-fast definitive 75% confidence
Machine learning (ML) is characterized as a branch of computational algorithms designed to emulate human intelligence by learning from the surrounding environment.ML emulates human intelligence It is applied across diverse domains, including construction where Liu et al. used it to predict fluidity and rheological properties of cement-based materials.Liu et al. cement ML study, cybersecurity via the BEAM tool by netskopeoss employing ML and SHAP for detecting supply chain compromises in network traffic.BEAM tool ML detection, and energy systems such as wind power forecasting by Alkabbani et al.Alkabbani wind forecasting ML Open-source projects like TensorFlow drive ML advancements alongside Linux and Apache.TensorFlow advances machine learning Research highlights include neuro-symbolic hierarchical reinforcement learning by Mitchener et al.neuro-symbolic RL framework, uncertainty sources from data noise and model choices.ML uncertainty sources, and adversarial training advances reviewed by Bai et al.adversarial training advances Experts such as Avani Desai of Schellman note attackers using adaptive ML models to evade defenses,attackers adaptive ML models while Nikita Kozodoi holds a PhD in ML.Nikita Kozodoi ML PhD ML intersects with knowledge representation in special sessions and tracks,KR ML special session and powers future automations like SOC tasks and identity governance.
openrouter/x-ai/grok-4.1-fast definitive 80% confidence
Machine learning (ML) encompasses algorithms that learn from vast datasets to recognize patterns, predict outcomes, and adapt over time, powering AI tools through mechanisms like data learning, pattern recognition and prediction, adaptive learning, and customization, as outlined in the procedures for AI writing adaptability ML mechanisms in AI writing. It excels at processing large datasets to uncover correlations, complementing multiscale modeling's causal insights for robust predictions across scales ML with multiscale modeling. Predictive analytics, a branch using historical data, statistical algorithms, and ML techniques, forecasts future outcomes predictive analytics definition. ML integrates with AI for applications including electricity price forecasting by El-Azab et al. (2024) energy forecasting with ML, biological disease modeling ML in biology, multi-omics analysis for COVID-19 by Ren et al. (2024) multi-omics ML for COVID, consumer behavior via EEG by Hakim et al. (2021) EEG and ML for preferences, and OPIR target detection OPIR AI/ML applications. However, ML models trained on historical data risk replicating biases in lending, per perspectives on discriminatory practices ML bias in lending.
openrouter/x-ai/grok-4.1-fast 35% confidence
The author identifies a frontier-tech consortium operating at the intersection of artificial intelligence, consciousness studies, computational biology, nuclear-grade compute, and neurotechnology, which supports machine learning alongside cognitive modeling, consciousness transfer, and life-extension systems. According to the author, the neurotechnology employed by this consortium underwrites machine learning, cognitive modeling, and early scaffolding for consciousness transfer and life-extension systems. These claims position machine learning as a key area underpinned by the consortium's neurotechnology within broader frontier-tech pursuits.
Facts (240)
Sources
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 16 facts
claimMachine learning systems benefit from knowledge graphs by using them as sources of labeled training data or other input data, which supports the development of knowledge- and data-driven AI approaches.
claimEntity matching approaches can be configured manually using similarity thresholds that candidates must exceed, or by applying supervised machine learning models, as cited in reference [204].
claimOntology learning approaches can be categorized into linguistic approaches, which utilize NLP techniques like part-of-speech tagging and dependency analysis, and machine learning approaches.
claimGraph data models for knowledge graphs should provide comprehensive query languages and advanced analysis capabilities, such as clustering similar entities or determining graph embeddings for machine learning tasks.
procedureThe SAGA system performs deduplication by grouping entities by type and using simple blocking to partition data into smaller buckets, followed by a matching model that computes similarity scores using machine-learning or rule-based methods, and finally utilizing correlation clustering to determine matching entities.
claimConfiguration of knowledge graph construction pipelines can be simplified by using default parameter settings or automatic approaches based on machine learning.
claimMachine learning methods are used for Named Entity Recognition (NER) to identify 'emerging entities' that are unknown to a knowledge base.
claimAI-KG, CovidGraph, dstlr, SLOGERT, and NELL utilize machine learning approaches for knowledge extraction.
referenceAutoKnow is a closed-source system used by Amazon to create a retail product knowledge graph by processing product catalogs and consumer shopping behavior logs using machine learning and distant supervision.
claimThe DRKG, HKGB, and SAGA knowledge graph construction solutions use machine learning-based link prediction on graph embeddings to find further knowledge for knowledge completion.
claimWorldKG utilizes an unsupervised machine learning approach for ontology alignment, whereas most other knowledge graph approaches perform alignment and merging of ontologies manually.
claimMachine learning approaches to ontology learning include statistic-based methods, such as co-occurrence analysis and clustering, and logic-based approaches, such as inductive logic programming or logical inference.
claimConditional Random Fields (CRF) is a machine learning method for the sequence labeling task in entity recognition that uses an undirected graph to connect input and output variables and models the conditional probability of the output given the input.
claimMachine learning approaches for data cleaning have gained prominence because they simplify the configuration of various subtasks.
claimData profiling computes accurate statistical information, whereas machine learning methods for tasks like type recognition usually do not provide perfect accuracy when generating metadata.
referenceThe paper 'From Cleaning before ML to Cleaning for ML' discusses the shift in data cleaning paradigms for machine learning applications, published in the IEEE Data Engineering Bulletin in 2021.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 15 facts
claimFormal logic contributes structure and systematic extrapolation capabilities to AI models, while machine learning provides the ability to handle noise, unseen entities, and incomplete data.
claimMachine learning models are data-driven systems that inherently possess a degree of uncertainty in their outputs, unlike traditional deterministic programs.
claimThe field of machine learning is shifting focus from merely identifying incorrect predictions to understanding the likelihood of error and the reasons behind it, moving toward models with greater self-awareness regarding their operational limits.
referenceFakour, Mosleh, and Ramezani published a structured review of literature concerning uncertainty in machine learning and deep learning in 2024.
claimMehrabi, Morstatter, Saxena, Lerman, and Galstyan published 'A survey on bias and fairness in machine learning' in ACM Computing Surveys in 2021.
claimCompositionality in machine learning refers to the requirement that learned representations should be modular, interpretable, and combinable.
referenceEspinosa Zarlenga, M. et al. introduced Concept Embedding Models as a way to address the accuracy-explainability trade-off in machine learning, published in the 2022 Advances in Neural Information Processing Systems (NeurIPS).
referenceM. Richardson and P. Domingos published the paper 'Markov logic networks' in the journal Machine Learning in 2006.
claimIn the context of machine learning, uncertainty quantification (UQ) refers to the process of providing a measure of how much confidence a model has in its predictions or generations.
referenceIncorvaia, Hond, and Asgari utilized anomaly-based dataset dissimilarity measures to quantify the uncertainty of machine learning model performance in their 2024 paper.
referenceS.H. Bach, M. Broecheler, B. Huang, and L. Getoor published 'Hinge-loss Markov random fields and probabilistic soft logic' in the Journal of Machine Learning Research in 2017.
referenceFreiesleben and Grote (2023) propose a theory of robustness in machine learning that extends beyond simple generalization.
claimNanehkaran et al. bridged IoT health data and recommendation systems using machine learning, though future improvements may require symbolic medical ontologies for explainable diagnosis.
referenceUncertainty in machine learning models stems from multiple sources, including inherent randomness or noise in the data, limitations imposed by the choice of model architecture and parameters, and inductive assumptions made during the learning process, as cited in reference [57].
referenceBai, Luo, Zhao, Wen, and Wang (2021) review recent advances in adversarial training techniques for improving adversarial robustness in machine learning.
Track: Poster Session 3 - aistats 2026 virtual.aistats.org 13 facts
claimMixture-of-expert (MOE) models are machine learning methods that model heterogeneous behavior across data space using an ensemble of learners, making them suitable for dynamic data that exhibit non-stationarity and heavy-tailed errors.
referenceData-driven optimization translates machine learning models into decision-making by optimizing decisions based on estimated costs, often by fitting a distributional model and plugging it into a target optimization problem.
claimSynthesizing certificate functions using machine learning techniques remains a challenge because of the difficulty in managing their geometrical properties.
claimSelf-supervised learning methods that mask parts of the input data and train models to predict the missing components have led to significant advances in machine learning.
referenceKnowledge distillation involves training a smaller student model to emulate the internal representations of a complex teacher model to deploy machine learning in resource-constrained environments.
claimThe "Accuracy-on-the-line" phenomenon in machine learning describes a positive correlation between a model's in-distribution (ID) and out-of-distribution (OOD) accuracy across different hyperparameters and data configurations.
claimPermutation invariance is a common symmetry used to simplify complex machine learning problems, leading to a surge in research on permutation invariant architectures.
claimSymbolic regression is a machine learning approach aimed at discovering mathematical closed-form expressions that best fit a given dataset.
claimThe authors of the paper on Strategic Conformal Prediction propose a framework designed for robust uncertainty quantification in settings where machine learning model predictions alter the environment because agents strategize to suit their own interests.
claimEvaluating machine learning models is necessary for determining technical accuracy and assessing potential societal implications.
claimJarren Briscoe, Garrett Kepler, Daryl DeFord, and Assefaw Gebremedhin demonstrate that combinatorics in classification metrics induce significant sample-size bias in machine learning algorithms.
claimAdditive models allow the incorporation of interpretable structures into a wide range of machine learning model classes.
claimMany machine learning models that estimate Conditional Average Treatment Effects (CATE) assume that all relevant features are readily available at prediction time, which is often unrealistic in practice.
A comprehensive overview on demand side energy management ... link.springer.com Mar 13, 2023 10 facts
referenceMurphy KP authored a book titled 'Machine learning: a probabilistic perspective' published by MIT Press in 2012.
claimThe three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning (Murphy 2012).
referenceTang W-J, Wu Y-S, and Yang H-T presented an analysis of potential demand response capacity based on adaptive segmentation and machine learning at the 2018 18th International Conference on Harmonics and Quality of Power.
referenceZhou D, Balandat M, and Tomlin C published 'Residential demand response targeting using machine learning with observational data' at the 2016 IEEE 55th Conference on Decision and Control (CDC) in 2016.
referenceTang W-J, Wu Y-S, and Yang H-T analyzed potential demand response capacity using adaptive segmentation and machine learning, presented at the 2018 18th International Conference on Harmonics and Quality of Power.
claimAntonopoulos et al. (2020) describe machine learning as a branch of computational algorithms designed to emulate human intelligence by learning from the environment, serving as a primary tool for addressing issues in Demand Side Management (DSM).
referenceZhou D, Balandat M, and Tomlin C presented 'Residential demand response targeting using machine learning with observational data' at the 2016 IEEE 55th Conference on Decision and Control (CDC) in 2016.
claimMurphy (2012) classifies the main types of machine learning as supervised learning, unsupervised learning, and reinforcement learning.
referenceAntonopoulos et al. published a systematic review titled 'Artificial intelligence and machine learning approaches to energy demand-side response' in the journal Renewable and Sustainable Energy Reviews in 2020.
claimMachine learning is a branch of computational algorithms designed to emulate human intelligence by learning from the surrounding environment, and it is used to address various issues in Demand-Side Management (DSM) (Antonopoulos et al. 2020).
Cybersecurity Trends and Predictions 2025 From Industry Insiders itprotoday.com 9 facts
claimIn 2025, AI and machine learning will automate complex identity governance processes, such as role management and access reconciliation, by analyzing historical data and usage patterns.
claimAI and machine learning-based fraud detection systems are increasingly vital for businesses because they use dynamic learning to adapt to evolving bot tactics in real-time, unlike static defenses that rely on preset rules.
claimAI and machine learning integration in 2025 will improve efficiency, natural language use, and threat detection capabilities, while simultaneously expanding the threat landscape and enhancing adversary execution capabilities.
claimSecurity departments are expected to adopt AI in 2025 to keep pace with the cybersecurity arms race, with early adopters utilizing machine learning-assisted threat analytics to identify attack patterns and mitigate threats.
claimAI and machine learning will play an increasingly significant role in detecting and responding to threats, leading to more advanced threat hunting tools and automated incident response systems.
procedureTo counter cyberthreats that complicate system recovery, organizations must rely on isolated, unaffected data copies and AI/ML-powered tools to detect and validate clean data.
claimAI and machine learning serve a dual role in the 2025 cybersecurity landscape, empowering both attackers to bypass detection and defenders to validate clean data for recovery.
claimTraditional security operations center (SOC) analyst roles will rapidly decline in 2025 as AI and machine learning automate routine security tasks.
claimAvani Desai, CEO of Schellman, asserts that attackers are deploying machine learning models that adapt, disguise themselves, and evade traditional defenses in real-time, creating a race between defensive and offensive AI technologies.
Global perspectives on energy technology assessment and ... link.springer.com Oct 30, 2025 9 facts
referenceNanjar et al. (2024) performed a systematic literature review of machine learning and deep learning approaches used for energy prediction.
referenceRaman R et al. used machine learning approaches to map sodium-ion battery research to sustainable development goals in a 2025 study published in the Journal of Energy Storage.
referenceZahra E, Manish M, Baltrusaitis J, Pallavi D, and Mark MW published 'Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment' in Sustain Sci Technol in 2025.
claimMachine learning and deep learning methods, specifically Long Short-term memory (LSTM) and Gated Recurrent unit (GRU), are efficient at capturing and utilizing temporal data sequences and time-series patterns in energy systems.
claimLife cycle assessments (LCA) and machine learning, when used in conjunction with techno-economic studies, provide comprehensive assessments of renewable energy systems that assist industry decision-makers and politicians in making informed choices.
referenceEl-Azab et al. (2024) evaluated machine learning and deep learning approaches for forecasting electricity prices and assessing energy loads using real datasets.
claimAI can analyze renewable energy policy scenarios, generate models to anticipate long-term impacts of renewable energy integration, and assess climate change risks using machine learning and deep learning functions.
claimArtificial intelligence optimizes thermal energy storage (TES) by improving capacity, efficiency, and cost-effectiveness through the use of machine learning, evolutionary algorithms, and neural networks.
referenceAlkabbani et al. (2023) developed a machine learning-based time series model for large-scale regional wind power forecasting, specifically applied to a case study in Ontario, Canada.
Integrating allostasis and emerging technologies to study complex ... nature.com Nov 5, 2025 8 facts
referenceZhang et al. (2025) demonstrated that multimodal integration using machine learning facilitates risk stratification in HR+/HER2− breast cancer, published in Cell Reports Medicine.
referenceRichard et al. (2022) demonstrated early prediction of COVID-19 patient survival using targeted plasma multi-omics and machine learning, published in Molecular & Cellular Proteomics.
claimIntegrating machine learning techniques into biological research enables the effective modeling of complex interactions and improves the accuracy of disease diagnosis, prediction, and classification.
claimMachine learning enhances the predictive power of omics data, which enables individualized disease classification and outcome prediction.
referenceLiu et al. (2023) published research in BMC Bioinformatics on using machine learning to analyze omic-data for COVID-19 diagnosis and prognosis.
claimThe integration of multi-omics and machine learning offers a systems-level framework for investigating the molecular underpinnings of allostatic dysregulation and enhances diagnostic and prognostic capabilities for chronic infectious diseases like Long COVID.
claimMachine learning applied to transcriptomic and proteomic data is used to classify immune cell states and identify neutrophil-related gene clusters for patient stratification.
referenceRen et al. (2024) identified gene and protein signatures associated with the long-term effects of COVID-19 on the immune system using single-cell multi-omics and machine learning, published in Vaccine.
Call for Papers: Special Session on KR and Machine Learning kr.org 7 facts
procedureSubmissions to the Special Session on KR and Machine Learning are peer-reviewed by Program Committee members who are active in both Knowledge Representation and Machine Learning fields.
claimThe Special Session on KR and Machine Learning at KR2022 invites submissions that integrate knowledge representation (KR) and machine learning (ML), specifically focusing on using KR methods to solve ML challenges (such as knowledge-guided or explainable learning), using ML methods to solve KR challenges (such as efficient inference or knowledge base completion), integrating learning and reasoning, and applying combined approaches to real-world problems.
claimThe synergy between Machine Learning and Knowledge Representation and Reasoning has the potential to advance fundamental AI challenges, such as learning symbolic generalizations from raw multi-modal data, data-efficient learning, interpretability, and federated multi-agent learning.
claimThe field of AI has seen growing interest in combining Machine Learning (ML) with Knowledge Representation and Reasoning (KR) methods in recent years.
claimThe Special Session on KR and Machine Learning requires submissions to be at the intersection of Knowledge Representation and Machine Learning, meaning submissions focused exclusively on either KR or ML will not be accepted.
claimMachine Learning offers potential solutions to long-standing Knowledge Representation and Reasoning challenges, including efficient, noise-tolerant, and ampliative inference, knowledge acquisition, and the limitations of symbolic representations.
claimThe success of Machine Learning systems has highlighted issues like explainability, bias, and fairness, which encourages the integration of symbolic or interpretable representations into AI systems.
Understanding LLM Understanding skywritingspress.ca Jun 14, 2024 7 facts
claimKaiyu Yang utilizes machine learning and large language models to prove theorems within formal environments such as Coq and Lean.
claimOverfitting remains a significant challenge in machine learning because the best hypothesis derived from one dataset may not generalize effectively to another dataset.
perspectiveRichard Futrell argues that an information-theoretic approach provides a deeper explanation for the nature of human language than purely symbolic approaches, while linking the science of language with neuroscience and machine learning.
claimInternal states of a system appear to infer and act on their world to preserve their integrity, a process where 'free energy' is optimized in Bayesian inference and machine learning.
claimMikhail Belkin, a Professor at the Halicioglu Data Science Institute at the University of California, San Diego, and an Amazon Scholar, researches the theory and applications of machine learning and data analysis, specifically focusing on statistical phenomena in deep learning.
claimTom Griffiths' research explores the connections between human and machine learning by applying statistics and artificial intelligence to understand how people solve computational problems in everyday life.
claimLanguages change over time due to social, technological, cultural, and political factors, which can be detected by new natural language processing and machine learning models.
Call for Papers: KR meets Machine Learning and Explanation kr.org 6 facts
perspectiveThe field of Knowledge Representation (KR) provides a repertoire of technologies for leveraging knowledge in both Machine Learning (ML) and explanation pipelines.
procedureThe KR 2026 special track 'KR meets Machine Learning and Explanation' mandates that all submissions must fall into the intersection of Knowledge Representation and either Machine Learning or explanation; papers that do not meet this criterion will be desk-rejected before the review process begins.
claimThe KR 2026 special track on 'KR meets Machine Learning and Explanation' aims to focus on the synergistic interactions between Knowledge Representation (KR) and the fields of Machine Learning (ML) and explanation.
claimThe KR 2026 special track invites contributions that use Knowledge Representation (KR) methods to solve Machine Learning (ML) challenges, use ML methods to solve KR challenges, or integrate learning and reasoning for better modeling, solving, or explaining tasks.
claimThe KR 2026 special track 'KR meets Machine Learning and Explanation' invites research on the intersection of Knowledge Representation and Machine Learning, specifically covering topics such as learning symbolic knowledge (ontologies, knowledge graphs, action theories), KR-driven plan computation, logic-based learning, neural-symbolic learning, statistical relational learning, symbolic reinforcement learning, and the mutual use of KR techniques and LLMs.
claimThe KR 2026 special track welcomes papers focusing on evaluation protocols and benchmarking of hybrid solutions that combine Knowledge Representation (KR) with Machine Learning (ML) or explanation.
What is Open Source: Understanding Its Impact on Technology and ... algocademy.com 5 facts
claimOpen source software is a primary driver of innovation in artificial intelligence and machine learning.
claimIn the fields of data science and machine learning, open source tools enable researchers and developers to share datasets and models, collaborate on projects without barriers, and access cutting-edge tools without high costs.
claimTensorFlow is an open source library developed by Google that allows researchers and developers to build and train machine learning models without starting from scratch, thereby democratizing access to AI technology.
claimTensorFlow has made artificial intelligence and machine learning accessible to a broader audience.
claimTensorFlow and PyTorch are leading frameworks in artificial intelligence and machine learning innovation.
Neuro-insights: a systematic review of neuromarketing perspectives ... frontiersin.org 4 facts
referenceHakim et al. (2021) utilized EEG, machine learning (ML), and questionnaires to study ML models across pre-purchase and purchase stages.
referenceHakim et al. (2021) utilized electroencephalography (EEG) and machine learning (ML) to study food preference prediction.
referenceXu and Liu (2024) utilized machine learning to decode consumer purchase decisions by exploring the predictive power of EEG features in online shopping environments.
claimMarques, J. A. L., Neto, A. C., Silva, S. C., and Bigne, E. (2025) leveraged a machine learning approach for Electrodermal Activity (EDA) and Facial Expression Analysis (FEA) neurophysiological metrics to predict consumer ad preferences, published in Psychology & Marketing.
Beyond Missile Deterrence: The Rise of Algorithmic Superiority trendsresearch.org Mar 16, 2026 4 facts
claimMachine-learning algorithms improve cyber defense by monitoring network traffic, user behavior, and system logs to detect intrusions and unusual activity faster than traditional signature-based approaches.
claimMachine-learning systems optimize the timing, format, and platform for information campaigns, while bots and automated accounts amplify messages to reach large audiences quickly.
claimModern military intelligence relies on machine-learning systems to process vast volumes of data from satellites, UAVs, radar, electronic intercepts, open-source platforms, financial systems, and social media that exceed human processing capacity.
claimMachine-learning systems support military intelligence by automatically identifying and classifying objects in images, detecting patterns in movements or communications, identifying unusual activity indicative of attack preparations, and synthesizing disparate data streams into a coherent operational picture.
Epstein: A Forensic Reconstruction of the Transhumanist Research ... bryantmcgill.substack.com Jan 31, 2026 4 facts
claimThe neurotechnology used by the frontier-tech consortium underwrites machine learning, cognitive modeling, and the early scaffolding for consciousness transfer and life-extension systems.
claimThe author identifies a frontier-tech consortium operating at the intersection of artificial intelligence, consciousness studies, computational biology, nuclear-grade compute, and neurotechnology, which supports machine learning, cognitive modeling, consciousness transfer, and life-extension systems.
claimThe neurotechnology used by the frontier-tech consortium underwrites machine learning, cognitive modeling, and the early scaffolding for consciousness transfer and life-extension systems.
claimThe author of the source text asserts that the neurotechnology associated with the network underwrites machine learning, cognitive modeling, and the early scaffolding for consciousness transfer and life-extension systems.
The Year of Neuro-Symbolic AI: How 2026 Makes Machines Actually ... cogentinfo.com Dec 30, 2025 4 facts
claimThe adoption of neuro-symbolic AI requires a workforce with cross-functional expertise in machine learning pipelines, knowledge engineering, business processes, and compliance requirements.
claimModern enterprises are shifting from fragmented AI deployments to system-wide architectures that harmonize machine learning with symbolic reasoning.
referenceThe neural perception layer of a neuro-symbolic system functions as the sensory interface, interpreting unstructured inputs like text, spoken language, and images using machine learning models to identify entities, detect intent, and extract features.
claimNeuro-symbolic AI combines machine learning with structured cognition to create intelligence that mirrors human reasoning while maintaining operational integrity.
Business ecosystems as a way to activate lock-in in business models link.springer.com Mar 28, 2025 4 facts
referenceCosta-Climent, Haftor, and Staniewski researched the use of machine learning to create and capture value in the business models of small and medium-sized enterprises in a 2023 study.
claimFacebook generates revenue by matching content producers and consumers with advertisers, utilizing machine learning to analyze user activity and content to predict optimal advertising placement (Parker et al., 2016).
claimThe concept of data network effects, introduced by Gregory et al. in 2021, was integrated into business model theory by Costa et al. in 2023 to account for advances in machine learning technologies.
claimData network effects act as a lock-in mechanism because larger volumes of platform data improve machine learning pattern accuracy, which increases value for advertisers and attracts more revenue (Haftor et al., 2024).
Understanding the Psychology of Impulse Buying in E-Commerce jmsr-online.com Aug 9, 2025 3 facts
claimResearchers in e-commerce impulse buying studies are increasingly utilizing technology-assisted data collection methods, such as mobile app analytics, machine learning user behavior classification, and AI-generated recommendation response tracking, to move beyond static metrics toward dynamic modeling of impulse buying paths.
claimDevelopments in machine learning and natural language processing (NLP) will allow companies to segment their customer base based on real-time emotional profiles and demographics.
claimAdvancements in machine learning and natural language processing (NLP) enable companies to segment their customer base using real-time emotional profiles in addition to traditional demographics.
Neuro-symbolic AI - Wikipedia en.wikipedia.org 3 facts
perspectiveLeslie Valiant argues that the effective construction of rich computational cognitive models requires the combination of symbolic reasoning and efficient machine learning.
claimAbductive Learning integrates machine learning and logical reasoning in a balanced-loop via abductive reasoning, enabling the two approaches to work together in a mutually beneficial way.
referenceArtur d'Avila Garcez, Marco Gori, Luis C. Lamb, Luciano Serafini, Michael Spranger, and Son N. Tran published 'Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning', arguing for a principled approach to combining these fields.
A Hilbertian approach to biological problems | PLOS Complex ... journals.plos.org Nov 5, 2024 3 facts
claimThe integration of machine learning and multiscale modeling allows for the creation of robust predictive models that use underlying physics to manage ill-posed problems and explore massive design spaces, capturing emergent properties and complex interactions across scales.
claimIntegrating approaches from dynamical systems theory, computer science, statistical inference, and machine learning within an axiomatic framework can provide a more comprehensive understanding of biological systems.
claimIntegrating machine learning with multiscale modeling addresses individual limitations: machine learning excels at handling large datasets and uncovering correlations, while multiscale modeling is adept at probing causality and mechanisms.
7 Benefits of Artificial Intelligence (AI) for Business - UC Online online.uc.edu 3 facts
claimPredictive analytics is a branch of advanced analytics that uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.
claimFirms with in-house development teams can build bespoke AI solutions, provided the team members possess a deep understanding of AI, machine learning, and the impact of these technologies on modern business.
claimAI enhances cybersecurity by identifying potential threats, monitoring network activity, and responding to security breaches in real-time, while machine learning algorithms detect anomalies or vulnerabilities to predict attacks.
Weekly Innovations and Future Trends in Open Source dev.to May 19, 2025 3 facts
referenceTensorFlow 3.5 is a machine learning framework that includes quantum computing modules for hybrid machine learning research.
claimTensorFlow 3.5 enables researchers and machine learning practitioners to explore hybrid models by integrating quantum algorithms with conventional machine learning, allowing for the solution of previously impractical, computationally intense problems.
claimTensorFlow 3.5 introduces modules for hybrid classical-quantum models, facilitating new research and development opportunities for machine learning practitioners.
The Benefits of Using AI Writing Tools: How Artificial Intelligence Is ... mightyunionagency.com 3 facts
claimAI writing tools utilize machine learning algorithms to generate content by analyzing existing datasets on specific topics, such as cryptocurrency, to suggest new angles and ideas.
claimAI writing tools utilize machine learning algorithms to analyze data and generate personalized content based on user preferences.
claimMachine learning algorithms train AI systems to understand language by processing specific datasets collected from various sources.
The New Field of Network Physiology: Building the Human ... frontiersin.org 3 facts
claimPhysiological systems communicate through complex mechanisms that manifest as various functional forms of coupling, necessitating the integration of pair-wise physiologic interactions into a general framework that combines nonlinear dynamics, information theory, and machine learning.
claimMachine learning and AI algorithms need to be developed to classify physiological states, functions, and conditions based on network physiology maps from large populations of subjects.
claimHealthcare cyber-physical systems are expected to use machine learning and AI algorithms to monitor patient physiological states, quantify risk indices for abnormalities, signal the need for medical intervention, and actuate vital health signals like cardiac pacing, insulin levels, and blood pressure.
A Survey of Incorporating Psychological Theories in LLMs - arXiv arxiv.org 3 facts
referenceGrace W Lindsay authored 'Attention in psychology, neuroscience, and machine learning', published in Frontiers in Computational Neuroscience in 2020.
referenceSangjun Park and JinYeong Bak proposed 'Memoria', a human-inspired memory architecture designed to resolve the 'fateful forgetting' problem in machine learning, in their 2024 paper presented at the 41st International Conference on Machine Learning (ICML).
referenceThilo Hagendorff, Ishita Dasgupta, Marcel Binz, Stephanie C. Y. Chan, Andrew Lampinen, Jane X. Wang, Zeynep Akata, and Eric Schulz authored a paper titled 'Machine Psychology' which explores the intersection of machine learning and psychological theory.
Advancing energy efficiency: innovative technologies and strategic ... oaepublish.com 3 facts
referenceThe article 'Artificial intelligence and machine learning in energy systems: a bibliographic perspective' was published in Energy Strategy Reviews in 2023 (Volume 45, 101017).
claimArtificial intelligence and machine learning transform energy management techniques by providing advanced methods to optimize energy use across various industries through algorithms and data analysis.
referenceÇınar, Z. M., Abdussalam, N. A., Zeeshan, Q., Korhan, O., Asmael, M., and Safaei, B. published 'Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0' in the journal Sustainability in 2020 (Volume 12, 8211).
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 3 facts
referenceAdrian MP Braşoveanu and Răzvan Andonie researched semantic fake news detection from a machine learning perspective, published in the 2019 proceedings of the 15th International Work-Conference on Artificial Neural Networks (IWANN).
referenceVaishak Belle published 'Symbolic logic meets machine learning: A brief survey in infinite domains' in the International Conference on Scalable Uncertainty Management in 2020.
referenceLudovico Mitchener, David Tuckey, Matthew Crosby, and Alessandra Russo authored 'Detect, understand, act: A neuro-symbolic hierarchical reinforcement learning framework', published in the journal Machine Learning, 111(4):1523–1549, in 2022.
Combining large language models with enterprise knowledge graphs frontiersin.org Aug 26, 2024 3 facts
referenceThe paper 'A survey of human-in-the-loop for machine learning' by Wu et al. (2022) provides a comprehensive survey of human-in-the-loop methodologies within the field of machine learning.
claimThe human-in-the-loop (HITL) paradigm integrates human expertise into the modeling process to manage machine learning model uncertainty, as noted by Wu et al. (2022).
claimHuman-in-the-loop (HITL) methods effectively handle scarce or sparse data for Named Entity Recognition (NER) (Shen et al., 2017), address mislabeling (Muthuraman et al., 2021), and enhance data processing, model training, and inference stages of the machine learning pipeline (Zhang et al., 2019; Klie et al., 2020; Wu et al., 2022).
Medical Hallucination in Foundation Models and Their ... medrxiv.org Mar 3, 2025 3 facts
claimThe U.S. Food and Drug Administration (FDA) has introduced new approaches to change control for AI/ML-enabled medical devices to allow for more flexible oversight of systems that continue to learn and evolve after deployment.
referenceThe FDA published Good Machine Learning Practice (GMLP) guidance to address challenges in AI/ML-enabled medical devices, specifically covering data quality, algorithm validation, and performance monitoring.
claimFDA adaptations for AI/ML-enabled medical devices primarily address supervised learning systems rather than the unique challenges posed by generative AI.
Finance (FINN) - catalog.uark.edu - University of Arkansas catalog.uark.edu 2 facts
referenceThe University of Arkansas course FINN 52403, 'Digital Innovation in Financial Markets,' examines the impact of technological innovations such as blockchain, nonbank financial technology firms, and machine learning on financial markets, investors, and firms raising capital.
referenceThe University of Arkansas course FINN 53403, 'Financial Data Analytics II,' focuses on the application of Artificial Intelligence (AI) and Machine Learning (ML) technologies to enhance the gathering, analysis, and utilization of financial information.
LLM Observability: How to Monitor AI When It Thinks in Tokens | TTMS ttms.com Feb 10, 2026 2 facts
claimWeights & Biases (W&B) is an ML experiment tracking platform that supports LLM applications by allowing users to log prompts, model configurations, and outputs during experiments or production runs.
claimArize is an AI observability platform designed for machine learning and LLM monitoring, offering features such as data drift detection, bias monitoring, embedding analysis, and tracing.
War in the Middle East and the Role of AI-Powered Cyberattacks manaramagazine.org Mar 13, 2026 2 facts
claimMachine learning systems are utilized for defensive cybersecurity tasks, including intrusion detection, log analysis, and automated response.
claimMachine-learning algorithms can scan millions of internet-connected devices in seconds to identify vulnerable targets such as exposed routers, servers, or IoT cameras during the reconnaissance phase of a cyber campaign.
Detect hallucinations for RAG-based systems - AWS aws.amazon.com May 16, 2025 2 facts
Advantages of Financial Advertising: How It Benefits Your Business carvertise.com 2 facts
claimArtificial intelligence and machine learning can analyze vast datasets to predict customer behavior and optimize advertising delivery in real-time.
claimArtificial intelligence and machine learning can analyze large datasets to predict customer behavior and optimize the delivery of financial advertisements in real-time.
Recent breakthroughs in the valorization of lignocellulosic biomass ... pubs.rsc.org Jun 7, 2025 2 facts
A Survey on the Theory and Mechanism of Large Language Models arxiv.org Mar 12, 2026 2 facts
claimThe current landscape of large language models presents new challenges for defining and formalizing concepts like 'robustness', 'fairness', and 'privacy' compared to traditional machine learning, as noted by Chang et al. (2024), Anwar et al. (2024), Dominguez-Olmedo et al. (2025), and Hardt and Mendler-Dünner (2025).
claimTraditional machine learning literature extensively analyzed robustness (Muravev and Petiushko, 2021; Ruan et al., 2021), fairness (Kleinberg et al., 2016; Liu et al., 2019), and privacy (Li et al., 2017; Kairouz et al., 2015) because these concepts were well-defined and formalizable using precise mathematical objectives.
Strategic analysis of cyber conflicts: A game-theoretic modelling of ... securityanddefence.pl May 31, 2025 2 facts
claimThe integration of AI into cyber defence systems enhances detection capabilities but simultaneously introduces new vulnerabilities stemming from machine learning models, according to research by (2024).
claimThe increasing integration of artificial intelligence and machine learning into cyber operations suggests that the pace and complexity of cyber conflicts will likely accelerate.
Knowledge graphs - Amazon Science amazon.science 2 facts
claimApplied Scientists at Amazon are responsible for accessing large datasets with billions of images and video to build large-scale machine learning systems, and analyzing terabytes of text, images, and other data to solve real-world problems.
procedureThe Language Data Scientist role at Amazon involves analyzing and evaluating speech and interaction data to support the training and evaluation of machine learning models.
Papers - Dr Vaishak Belle vaishakbelle.github.io 2 facts
referenceThe paper 'Fairness in Machine Learning with Tractable Models' was published in Knowledge-Based Systems in 2021 by authors M. Varley and V. Belle.
referenceMartin Mladenov, Vaishak Belle, and Kristian Kersting authored the paper 'Planning in hybrid relational MDPs', which was published in the journal Machine Learning in 2017.
The psychological mechanisms through which digital content ... frontiersin.org Nov 12, 2025 2 facts
referenceLestari, Setiawan, and Aula (2024) utilized machine learning to analyze user conversion in mobile pharmacy apps by leveraging behavioral and demographic data.
referenceLusiana, E. D., Astutik, S., Nurjannah, N., and Sambah, A. B. (2025) utilized a machine learning approach to cluster marine environmental features of the Lesser Sunda Island in the Journal of Applied Data Science.
The Impacts of Individual and Household Debt on Health and Well ... apha.org Oct 25, 2021 2 facts
perspectiveArtificial intelligence and machine learning models based on historical lending data are likely to replicate past sexist and racist practices and should be intentionally designed to counteract these biases.
perspectiveArtificial intelligence and machine learning models based on historical lending data are likely to replicate past sexist and racist practices and should be intentionally designed to counteract these biases.
Consumer Psychology: Insights and Practical Applications online.edhec.edu Aug 19, 2025 2 facts
claimArtificial intelligence and machine learning are used to analyse consumer data and predict behaviour, enabling more effective targeting and personalised marketing.
claimArtificial intelligence and machine learning are used to analyze consumer data and predict behavior, enabling more effective targeting and personalized marketing.
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org Nov 7, 2024 2 facts
referenceYang et al. (2022) presented a psychological theory of explainability in the context of machine learning, published in the International Conference on Machine Learning.
referenceMitchener et al. (2022) developed a neuro-symbolic hierarchical reinforcement learning framework called Detect, understand, act, published in the journal Machine Learning.
Consciousness in Artificial Intelligence? A Framework for Classifying ... arxiv.org Nov 20, 2025 2 facts
perspectiveMachine learning researchers argue that computational neural dynamics and trajectories, construed as attractor dynamics reflecting oscillatory dynamics with harmonic modes, can explain the ineffability and richness of conscious states.
claimIn machine learning models, a value of 0 in a vector embedding or a non-firing artificial neuron can carry information.
Early Digital Engagement Among Younger Children and the ... pediatrics.jmir.org Jul 3, 2025 2 facts
claimTo maintain efficacy in a changing digital landscape, the proposed mHealth app must use AI and machine learning to provide continuous updates and give parents more control over watched content.
claimThe proposed mHealth app aims to promote healthy cognitive and emotional development, establish positive media habits, and adapt to busy family dynamics by integrating information technology, machine learning, and AI.
RAG Using Knowledge Graph: Mastering Advanced Techniques procogia.com Jan 15, 2025 1 fact
claimGeoffrey Hinton is widely regarded as the 'godfather of AI' and shared the Nobel Prize with John J. Hopfield for foundational discoveries and inventions that enable machine learning with artificial neural networks.
The evolution of the electronic components industry - tstronic tstronic.eu Sep 16, 2025 1 fact
claimThe integration of AI and machine learning into electronics supply chain management has transformed the methods used to forecast, analyze, and optimize inventory.
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org Jul 11, 2024 1 fact
referenceZachary C. Lipton argued that the concept of model interpretability in machine learning is both important and slippery in a 2018 article in Queue.
The role of open source in shaping software thetopvoices.com Nov 12, 2024 1 fact
claimOpen source drives technological advancement in fields including artificial intelligence, machine learning, and big data.
The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org 1 fact
claimConnectionist AI is a paradigm that focuses on neural networks and machine learning algorithms, drawing influence from cognitive science and computational neuroscience to identify patterns and glean insights from datasets.
Medicinal plants and human health: a comprehensive review of ... link.springer.com Nov 5, 2025 1 fact
claimSingle-cell and spatial omics technologies provide resolution in understanding metabolic specialization within plant tissues, while machine learning algorithms enable the creation of predictive models for metabolite profiles under varying environmental and genetic conditions.
Neuro-Symbolic AI: The Future of Smart Tech | Medium theaidrift.medium.com May 6, 2025 1 fact
claimNeuro-symbolic AI integrates logic with machine learning to develop machines that are smarter, ethical, and explainable.
Cyber Insights 2025: Open Source and Software Supply Chain ... securityweek.com Jan 15, 2025 1 fact
claimSteve Wilson, Chief Product Officer at Exabeam, predicts that in 2025, the adoption of Software Bill of Materials (SBOMs) will expand beyond traditional software, with AI and machine learning applications driving demand for more advanced Bill of Materials frameworks.
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 1 fact
referenceKnowledge graph embedding maps entities and relations into a low-dimensional vector space to efficiently capture the semantics and structure of the graph, allowing the resulting feature vectors to be learned by machine learning models (Dai et al. 2020b).
Protocol for testing global neuronal workspace and integrated ... journals.plos.org 1 fact
procedureWhen decoding content-specific sensory regions, the researchers use recording data from the sensory region associated with the sensory content in the relevant trial, such as pITC (including PL), mITC (including ML) for faces in NHPs, and VISp for visual gratings in mice.
Publications - Parenting Across Cultures parentingacrosscultures.org Apr 25, 2025 1 fact
referenceRothenberg et al. (in press) authored 'Predicting Adolescent Mental Health Outcomes across Cultures: A Machine Learning Approach', published in the Journal of Youth and Adolescence, which applies machine learning to predict mental health outcomes in adolescents across different cultures.
Psychology Of Financial Decision-Making - Meegle meegle.com 1 fact
claimArtificial intelligence and machine learning technologies analyze large datasets to identify patterns and provide personalized financial recommendations.
Enterprise AI Requires the Fusion of LLM and Knowledge Graph stardog.com Dec 4, 2024 1 fact
claimEnterprise customers require a GenAI stack that is modular, reusable, reproducible, trustworthy, includes lineage and traceability, and decouples machine learning, deep learning, and GenAI tasks while grounding them in quality data.
Unknown source 1 fact
claimMachine learning has achieved groundbreaking advancements across a wide variety of tasks in recent times.
The Impact of Open Source Software on the Tech Industry gianmatteocostanza.net Aug 7, 2023 1 fact
claimOpen source software projects like Linux, Apache, WordPress, and TensorFlow have propelled innovation in fields including operating systems, web development, and machine learning.
Addressing common challenges with knowledge graphs - SciBite scibite.com 1 fact
claimThe CENtree platform utilizes machine learning techniques to suggest new ontological candidates when users build or extend vocabularies.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Nov 4, 2024 1 fact
accountThe authors conducted a systematic literature review of NLP, machine learning, and knowledge representation research from the last decade to understand approaches for integrating knowledge graphs (KGs) and large language models (LLMs).
A critical review on techno-economic analysis of hybrid renewable ... link.springer.com Dec 6, 2023 1 fact
claimResearch in resource forecasting for renewable energy focuses on improving forecasting models through the use of advanced meteorological data, machine learning, and artificial intelligence techniques.
Benchmarking Hallucination Detection Methods in RAG - Cleanlab cleanlab.ai Sep 30, 2024 1 fact
claimLarge Language Models (LLMs) are prone to hallucination because they are fundamentally brittle machine learning models that may fail to generate accurate responses even when the retrieved context contains the correct answer, particularly when reasoning across different facts is required.
Life, Intelligence, and Consciousness: A Functional Perspective longnow.org Aug 27, 2025 1 fact
perspectiveThe author argues that the 'hard problem' of consciousness, or how computation gives rise to a self, is explainable through the understanding of computational modeling gained from over a century of ethology, neuroscience, and machine learning.
Bioelectricity - The Levin Lab drmichaellevin.org 1 fact
referenceO’Brien, T., Stremmel, J., Pio-Lopez, L., McMillen, P., Rasmussen-Ivey, C., and Levin, M. (2024) published 'Machine Learning for Hypothesis Generation in Biology and Medicine: Exploring the latent space of neuroscience and developmental bioelectricity' in Digital Discovery, which explores the application of machine learning to developmental bioelectricity.
Demand side management using optimization strategies for efficient ... journals.plos.org Mar 21, 2024 1 fact
referenceAl-Gabalawy M., Elmetwaly A. H., Younis R. A., and Omar A. I. authored 'Temperature prediction for electric vehicles of permanent magnet synchronous motor using robust machine learning tools' published in the Journal of Ambient Intelligence and Humanized Computing in May 2022.
Cybernetics 2.0 - Springer Nature link.springer.com 1 fact
claimThe book 'Cybernetics 2.0' covers topics including Biomedical Engineering and Bioengineering, Complex Systems, and Machine Learning.
2025 Fair Lending Trends | Wolters Kluwer wolterskluwer.com Apr 14, 2025 1 fact
claimIntegrating artificial intelligence and machine learning into lending practices introduces both opportunities and risks.
https://scholar.google.com/citations?view_op=view_... scholar.google.com 1 fact
claimDaniel A. Herrmann and Benjamin A. Levinstein established four criteria for measuring belief in large language models, drawing from insights in philosophy and machine learning practices.
The role of extremophile microbiomes in terraforming Mars - Nature nature.com Nov 17, 2025 1 fact
claimArtificial intelligence and machine learning are used to predict community assembly dynamics, optimize metabolic interactions, and simulate long-term ecosystem behavior under extraterrestrial constraints to enhance the design of stable and functional synthetic communities (SynComs) for Martian environments.
Engineering biology applications for environmental solutions - Nature nature.com Apr 14, 2025 1 fact
claimIn the absence of genomic barcodes, tracking engineered biological assets requires full genome sequencing and analysis using machine learning tools trained on proprietary data sets.
bureado/awesome-software-supply-chain-security - GitHub github.com 1 fact
referenceThe BEAM (Behavioral Evaluation of Application Metrics) tool by netskopeoss detects supply chain compromises by using machine learning and SHAP explainability to analyze network traffic for malicious behavior patterns.
The construction and refined extraction techniques of knowledge ... nature.com Feb 10, 2026 1 fact
referenceB. Subagdja et al. published 'Machine learning for refining knowledge graphs: A Survey' in ACM Computing Surveys, Volume 56, Issue 6, pages 1–38, in 2024.
Investments and Finance - Perspectives and commentary - Vanguard corporate.vanguard.com 1 fact
claimPortfolio managers within Vanguard's Quantitative Equity Group utilize machine learning, emphasizing the importance of model interpretability.
How Neuro-Symbolic AI Breaks the Limits of LLMs - WIRED wired.com 1 fact
claimAmazon utilizes a combination of neuro-symbolic AI, machine learning, and the DeepFleet foundation model to create efficient warehouse automation systems that uphold logical rules, optimize routes, and predict complex robot interactions.
Emerging Trends in Open Source Communities 2024 pingcap.com Sep 9, 2024 1 fact
claimAdvancements in artificial intelligence, machine learning, and cloud-native technologies are shaping the trajectory of the open source software community by enhancing project capabilities and democratizing access to technology across various sectors.
House Hearing on Unidentified Anomalous Phenomena Transcript rev.com Jan 23, 2026 1 fact
perspectiveMike Gold posits that non-human intelligence might not be biological, potentially taking the form of artificial intelligence or machine learning, and that the ultimate answer will be surprising.
Cellular rejuvenation: molecular mechanisms and potential ... - Nature nature.com Mar 14, 2023 1 fact
claimMachine learning and artificial intelligence methods may help identify biomarkers to predict individual circadian rhythms, which could determine optimal biological clock patterns for individuals.
[PDF] Ontologies, Neuro-Symbolic and Generative AI Technologies washacadsci.org Feb 3, 2025 1 fact
claimNeuro-symbolic (NeSy) systems are defined as systems that combine current machine learning (ML) systems with symbolic technologies.
What Is Open Source Software? - IBM ibm.com 1 fact
claimIT professionals commonly deploy open source software in categories including programming languages and frameworks, databases and data technologies, operating systems, Git-based public repositories, and frameworks for artificial intelligence, machine learning, and deep learning.
The Complete Guide to Open Source Licenses - FOSSA fossa.com 1 fact
claimTraditional open source licenses create challenges for AI and machine learning, specifically regarding whether using open source code to train models constitutes 'use' under licenses, whether AI-generated content inherits license obligations, and the emergence of new AI-specific licenses.
Defense Tech Trends for 2026: Innovation in Action - NSTXL nstxl.org 1 fact
claimThe OPIR TAP Lab AI/ML Applications opportunity aims to solve challenges related to the introduction of machine learning and artificial intelligence technologies in applications such as target detection, tracking, and characterization of infrared (IR) events.
Evaluating RAG applications with Amazon Bedrock knowledge base ... aws.amazon.com Mar 14, 2025 1 fact
accountAyan Ray is a Senior Generative AI Partner Solutions Architect at Amazon Web Services with over a decade of experience in Artificial Intelligence and Machine Learning.
The role of hydrogen in decarbonizing U.S. industry: A review ideas.repec.org 1 fact
referenceVaso Manojlović, Željko Kamberović, Marija Korać, and Milan Dotlić published 'Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters' in Applied Energy in 2022.
Understanding Investment Risk and Return - ElgarBlog elgar.blog Oct 13, 2025 1 fact
referenceThe 'Going Forward' section of 'Understanding Investment Risk and Return' examines lessons learned from past market bubbles and bankruptcies, as well as emerging tools and models, including AI and machine learning.
Scrolling Into Choice: The Psychology and Practice of Social Media ... acr-journal.com Oct 24, 2025 1 fact
referenceSourabh Sharma and Poonam Chaudhary published 'Chapter 4 Machine learning and deep learning' in De Gruyter eBooks in 2023.
Rethinking Espionage in the Modern Era cjil.uchicago.edu 1 fact
perspectiveThe author of the comment argues that the cost-benefit analysis for intelligence agencies regarding data collection likely tips toward being favorable due to the proliferation of machine learning and other analytical tools, despite the substantial costs required to sift through voluminous data.
The Impact of AI on Business Analysis and Quality Assurance linkedin.com Sep 7, 2024 1 fact
claimProfessionals in business analysis and quality assurance require a basic understanding of machine learning concepts, specifically regarding how models are built, trained, and deployed.
LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org Oct 23, 2025 1 fact
referenceZhao et al. (2024) authored 'A survey of knowledge graph construction using machine learning', published in CMES-Computer Modeling in Engineering & Sciences, volume 139, issue 1.
The Effects of Attachment and Trauma on Parenting and Children's ... rsisinternational.org Aug 16, 2025 1 fact
referenceBaşer, Başer, Kafescioğlu, and Erdem (2025) utilized machine learning to analyze therapeutic attunement in emotionally focused couples therapy.
Open-Source Governance And Open Source Collaboration - Meegle meegle.com 1 fact
claimEmerging technologies impacting open-source governance include AI and machine learning for automating code reviews and vulnerability detection, blockchain for enhancing transparency and trust, and decentralized collaboration tools for secure workflows.