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
Snap machine learning - IBM Research Optimizing Machine Learning Accelerate popular Machine Learning algorithms through system awareness, and hardware software differentiation Develop novel Machine Learning algorithms with best-in-class accuracy for business-focused applications AI in Business – Challenges Snap Machine Learning (Snap ML in short) is a library for training and scoring traditional machine learning models Such
Introducing AI Fairness 360 - IBM Research We are pleased to announce AI Fairness 360 (AIF360), a comprehensive open-source toolkit of metrics to check for unwanted bias in datasets and machine learning models, and state-of-the-art algorithms to mitigate such bias We invite you to use it and contribute to it to help engender trust in AI and make the world more equitable for all
Blog - IBM Research The IBM Research blog is the home for stories told by the researchers, scientists, and engineers inventing What’s Next in science and technology
Quantum Machine Learning - IBM Research Quantum Machine Learning 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
Neuro-symbolic AI - IBM Research We see Neuro-symbolic AI as a pathway to achieve artificial general intelligence By augmenting and combining the strengths of statistical AI, like machine learning, with the capabilities of human-like symbolic knowledge and reasoning, we’re aiming to create a revolution in AI, rather than an evolution
When Machine Learning meets Dynamical Systems: Theory and Applications . . . 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 As ML-based dynamical models are implemented in real
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
Publications - IBM Research Browse our catalog of recent publications authored by IBM researchers This works shows why IBM is one of the most important contributors to modern computing