Articles tagged with Machine Learning
Comprehensive exploration of living organisms, biological systems, and life processes across all scales from molecules to ecosystems. Encompasses cutting-edge research in biology, genetics, molecular biology, ecology, biochemistry, microbiology, botany, zoology, evolutionary biology, genomics, and biotechnology. Investigates cellular mechanisms, organism development, genetic inheritance, biodiversity conservation, metabolic processes, protein synthesis, DNA sequencing, CRISPR gene editing, stem cell research, and the fundamental principles governing all forms of life on Earth.
Comprehensive medical research, clinical studies, and healthcare sciences focused on disease prevention, diagnosis, and treatment. Encompasses clinical medicine, public health, pharmacology, epidemiology, medical specialties, disease mechanisms, therapeutic interventions, healthcare innovation, precision medicine, telemedicine, medical devices, drug development, clinical trials, patient care, mental health, nutrition science, health policy, and the application of medical science to improve human health, wellbeing, and quality of life across diverse populations.
Comprehensive investigation of human society, behavior, relationships, and social structures through systematic research and analysis. Encompasses psychology, sociology, anthropology, economics, political science, linguistics, education, demography, communications, and social research methodologies. Examines human cognition, social interactions, cultural phenomena, economic systems, political institutions, language and communication, educational processes, population dynamics, and the complex social, cultural, economic, and political forces shaping human societies, communities, and civilizations throughout history and across the contemporary world.
Fundamental study of the non-living natural world, matter, energy, and physical phenomena governing the universe. Encompasses physics, chemistry, earth sciences, atmospheric sciences, oceanography, materials science, and the investigation of physical laws, chemical reactions, geological processes, climate systems, and planetary dynamics. Explores everything from subatomic particles and quantum mechanics to planetary systems and cosmic phenomena, including energy transformations, molecular interactions, elemental properties, weather patterns, tectonic activity, and the fundamental forces and principles underlying the physical nature of reality.
Practical application of scientific knowledge and engineering principles to solve real-world problems and develop innovative technologies. Encompasses all engineering disciplines, technology development, computer science, artificial intelligence, environmental sciences, agriculture, materials applications, energy systems, and industrial innovation. Bridges theoretical research with tangible solutions for infrastructure, manufacturing, computing, communications, transportation, construction, sustainable development, and emerging technologies that advance human capabilities, improve quality of life, and address societal challenges through scientific innovation and technological progress.
Study of the practice, culture, infrastructure, and social dimensions of science itself. Addresses how science is conducted, organized, communicated, and integrated into society. Encompasses research funding mechanisms, scientific publishing systems, peer review processes, academic ethics, science policy, research institutions, scientific collaboration networks, science education, career development, research programs, scientific methods, science communication, and the sociology of scientific discovery. Examines the human, institutional, and cultural aspects of scientific enterprise, knowledge production, and the translation of research into societal benefit.
Comprehensive study of the universe beyond Earth, encompassing celestial objects, cosmic phenomena, and space exploration. Includes astronomy, astrophysics, planetary science, cosmology, space physics, astrobiology, and space technology. Investigates stars, galaxies, planets, moons, asteroids, comets, black holes, nebulae, exoplanets, dark matter, dark energy, cosmic microwave background, stellar evolution, planetary formation, space weather, solar system dynamics, the search for extraterrestrial life, and humanity's efforts to explore, understand, and unlock the mysteries of the cosmos through observation, theory, and space missions.
Comprehensive examination of tools, techniques, methodologies, and approaches used across scientific disciplines to conduct research, collect data, and analyze results. Encompasses experimental procedures, analytical methods, measurement techniques, instrumentation, imaging technologies, spectroscopic methods, laboratory protocols, observational studies, statistical analysis, computational methods, data visualization, quality control, and methodological innovations. Addresses the practical techniques and theoretical frameworks enabling scientists to investigate phenomena, test hypotheses, gather evidence, ensure reproducibility, and generate reliable knowledge through systematic, rigorous investigation across all areas of scientific inquiry.
Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
Researchers Todd Gureckis and Douglas Markant examine the benefits of self-directed learning from a cognitive and computational perspective. They argue that this approach optimizes educational experiences by focusing on useful information and exposing learners to new sources. By understanding these processes, researchers can develop as...
AEMASE is a cognitive software application that updates its knowledge of experts' performance in real-time, automatically evaluating student performance and reducing overall training costs. The system will be used to train Navy personnel on various aircraft, including the H-60 helicopter and E-2C Hawkeye aircraft.
Researchers at Georgia Tech identified key question types that facilitate human-robot learning, including feature queries, which were preferred by both human volunteers and robot learners. The study aims to improve the teaching of robots by understanding human learning mechanisms and developing more effective active learning strategies.
Researchers at the University of Bristol used musical features and machine learning algorithms to predict song hits in the UK singles chart. They found that danceability increased in popularity from the late 1970s and that slower styles, such as ballads, were more likely to become hits in the 1980s.
Researchers at Oregon State University have developed a new system that combines computer vision, machine learning, and automated planning to improve operations in various industries. The system is based on analyzing football plays and can be applied to tasks such as factory efficiency, airport operation, and nursing care.
Researchers have developed a computational tool that can determine whether faces are attractive, threatening or dominant with high accuracy. The tool uses machine learning techniques to analyze facial characteristics and was tested on a set of synthetic images, achieving accuracies of up to 96%.
Researchers at Harvard University's Neuromotor Control Lab found that motion-referenced learning, where the brain learns from actual movements rather than intended actions, can improve learning efficiency. This approach may lead to more effective neurological rehabilitation for individuals with stroke or other motor disorders.
Researchers at Tel Aviv University have developed an algorithm that enables computers to anticipate the future and make more efficient decisions. The algorithm, funded by Google, aims to minimize 'regret' in computer decision-making by analyzing variables and adapting to situations.
Researchers at EPFL developed a brain-computer interface that learns to recognize users' mental intentions, allowing for multitasking and reducing fatigue. The system uses statistical analysis and probability theory to distinguish between commands and enable users to control devices over longer periods.
Researchers created a machine-learning model called DiaTM that learns vernacular terms for health problems and symptoms, improving medical website performance. The system achieves a 25% improvement in nDCG, a scientific term referring to the relevance of information retrieval.
Researchers studied infant-mother interactions to develop a baby robot capable of learning social skills. They found that babies and mothers create patterns in their play, which becomes more stable with age.
A study found that nearly a quarter of the variability in achievement among video gamers can be predicted by measuring specific brain structures. Players with larger nucleus accumbens, caudate nucleus, and putamen performed better in training periods.
A Carnegie Mellon researcher notes that data-mining techniques are increasingly being applied to personal activities and movements, raising concerns about privacy. Technical means can help limit these risks, but a public discussion about data collection, ownership, and privacy is also necessary.
Researchers develop algorithms that allow end users to ask computers why they made mistakes, read their responses, and explain why those were errors. This 'meaningful' interaction enables computers to customize themselves to users and perform better in the future.
The new science of learning emphasizes computational, social, and brain-based approaches to understanding human learning. Key findings include the importance of machine learning, social interaction, and empathy in learning, which are now being applied to develop personalized teaching tools.
Research highlights three principles: learning is computational, social interaction underpins early learning, and brain circuits linking perception and action support learning across the life span. Social interaction with humans, particularly parents or tutors, plays a crucial role in early learning.
Researchers at UC San Diego used machine learning to empower their Einstein robot to learn realistic facial expressions, improving the process of teaching robots to make lifelike faces. The team discovered that the model learned to automatically compensate for missing servos and can now make facial expressions it had never encountered.
The Pittsburgh Science of Learning Center (PSLC) will continue its research on how people learn with a renewed $25 million NSF grant. The PSLC conducts experiments in over 50 classrooms across the US to understand learning styles and habits.
Researchers aim to develop new computational models of visual system learning and uncover mechanisms that explain the learning process in neural circuits. The project seeks to understand the role of feedback connections in the visual cortex during learning.
The University of Washington is leading a $6.25M project to develop an electronic Sherlock Holmes system for complex data analysis in the military. The system will integrate various types of sensor data to predict behavior and make decisions, addressing the challenge of handling high degrees of complexity and uncertainty.
The NSF has launched three Science of Learning Centers to study the foundations of learning across various situations, from cellular to complex processes engaging different brain areas. The centers will support interdisciplinary research and develop new methods for improving human learning and developing intelligent machines.
A NASA grant is supporting a two-year study at Florida Tech to develop algorithms that can learn from historical data and detect potential problems with a space shuttle component. The research aims to improve the efficiency of monitoring systems, reducing the time and effort required to extract knowledge from experts.