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…
Natural language processing
Natural language processing (NLP) is one of the most difficult problems in artificial intelligence (AI). The challenge lies in the fact that language is a complex system with numerous rules and exceptions. In order to create a system that can accurately process natural language, we need to have a deep understanding of how language works….
Generative models
Deep Learning is a type of artificial intelligence that is based on learning data representations, instead of specific rules. Deep Learning algorithms are able to learn complex patterns in data and can then generate new data that is similar to the data that was used to train the algorithm. Deep Learning is particularly well suited…
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…
Lifelong learning
Lifelong learning is a difficult challenge for AI systems because it requires the system to be able to identify and exploit new patterns in data in order to improve its performance. There are several approaches that have been proposed to tackle the lifelong learning challenge, including representation learning, meta-learning, and neural architecture search. Each of…
Unsupervised learning
The problem of unsupervised learning is that there is no easy way to tell a machine what the right answer is. This is in contrast to supervised learning, where we have a dataset with known correct labels. In unsupervised learning, the machine must find structure in data on its own. There are many ways to…