Articles tagged with Machine Learning
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.