Data quality is a key challenge for machine learning and artificial intelligence. There are various ways to solve this problem, but some new and creative approaches include using machine learning to identify and correct errors in data, using neural networks to detect and correct errors automatically, and using large language models to find and correct…
Tag: machine-learning
Reinforcement learning
Reinforcement learning is a type of machine learning that is concerned with how software agents ought to take actions in an environment in order to maximize some notion of cumulative reward. The problem is formally described by the Markov decision process (MDP).RL algorithms are used in autonomous vehicles, robotics, fault detection, telecommunications, and many other…
Computer vision
Foundations of Computer Vision Historically, the first images that were purposely processed by computers were generated by 18th century scientists who were experimenting with ways of representing three-dimensional objects on two-dimensional surfaces. The field of computer vision took off in the 19th century when photography made it possible to capture images without having to draw…
Robotics
The problem of robotics is one that has been tackled by a number of different AI paradigms over the years. However, the current state of the art is still far from being able to create robots that are able to operate in unstructured environments as effectively as humans can. This is largely due to the…
Zero-shot learning
Zero-shot learning is a difficult AI problem because it requires a system to learn from and generalize to new classes of data that it has not seen before. The usual way that neural networks and machine learning systems learn is through a process of training where the system is shown a series of examples and…
One-shot learning
One-shot learning is the task of learning to recognize a new object after only a single exposure to that object. This is in contrast to most machine learning tasks, which require multiple examples of each object in order to learn to recognize it. One-shot learning is difficult because it requires the learner to generalize from…
Semi-supervised learning
Semi-supervised learning is a type of machine learning where the training data is not fully labeled. This can be a difficult problem to solve because the data is not complete and may not be consistent. There are a few different ways to approach this problem, and the best way depends on the data and the…
Active learning
The vast majority of machine learning models are trained using a static dataset; that is, the dataset is fixed and does not change over time. This can be a problem when dealing with time-sensitive data, as the model may not be able to adapt to changes in the underlying data distribution. Active learning is a…
Meta-learning
Meta-learning is a subfield of machine learning that focuses on developing algorithms that can learn from data and improve their performance over time. The goal of meta-learning is to design models that can quickly adapt to new tasks and environments. Meta-learning algorithms are typically based on deep learning models that learn from large amounts of…
Reinforcement learning
Reinforcement learning is a subfield of machine learning that deals with how software agents ought to take actions in an environment to maximize some notion of cumulative reward. Reinforcement learning algorithms have been applied successfully to problems like checkers, backgammon, and other board games, to bicycle balancing, networked control systems, robot motion planning, and protein…