Machine Learning - IBM Research Machine learning uses data to teach AI systems to imitate the way that humans learn They can find the signal in the noise of big data, helping businesses improve their operations We’ve been in the field since since the beginning: IBMer Arthur Samuel even coined the term “Machine Learning” back in 1959
Quantum Machine Learning: An Interplay Between Quantum Computing and . . . Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning It seeks to revolutionize machine learning by harnessing the unique capabilities of quantum mechanics and employs machine learning techniques to advance quantum computing research This paper presents an overview of quantum computing for the machine learning
What are foundation models? - IBM Research What makes these new systems foundation models is that they, as the name suggests, can be the foundation for many applications of the AI model Using self-supervised learning and transfer learning, the model can apply information it’s learnt about one situation to another
DeepTools: A Full-Stack Machine Learning Compiler for the IBM Spyre . . . The rapid growth of machine learning applications has placed unprecedented pressure on conventional computing platforms, exposing the limitations of general-purpose processors in delivering the performance, efficiency, and scalability required by modern AI workloads Addressing these demands has led to a surge in domain-specialized hardware designs and programming paradigms that rethink how
Introducing AI Fairness 360 - IBM Research Machine learning models are increasingly used to inform high-stakes decisions about people Although machine learning, by its very nature, is always a form of statistical discrimination, the discrimination becomes objectionable when it places certain privileged groups at systematic advantage and certain unprivileged groups at systematic disadvantage Bias in training data, due to either
What is AI inferencing? - IBM Research Part of the Linux Foundation, PyTorch is a machine-learning framework that ties together software and hardware to let users run AI workloads in the hybrid cloud One of PyTorch’s key advantages is that it can run AI models on any hardware backend: GPUs, TPUs, IBM AIUs, and traditional CPUs
Quantum Machine Learning for minimal omics datasets with large feature . . . Quantum Machine Learning for minimal omics datasets with large feature space using embeddings and feature selection techniques Abstract Despite the amount of omics data generated in the last decade, the low data regime remains a significant challenge in healthcare, particularly in clinical trials and the study of rare diseases
When Machine Learning meets Dynamical Systems: Theory and Applications . . . Abstract The recent wave of using machine learning to analyze and manipulate real-world systems has inspired many research topics in the joint interface of machine learning and dynamical systems However, the real world applications are diverse and complex with vulnerabilities such as simulation divergence or violation of certain prior knowledge
Quantum Machine Learning - IBM Research We now know that quantum computers have the potential to boost the performance of machine learning systems, and may eventually power efforts in fields from drug discovery to fraud detection We’re doing foundational research in quantum ML to power tomorrow’s smart quantum algorithms
Snap machine learning - IBM Research A library that provides high-speed training of popular machine learning models on modern CPU GPU computing systems