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  • 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
  • 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
  • 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 . . .
    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
  • 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
  • What is retrieval-augmented generation (RAG)? - IBM Research
    RAG is an AI framework for retrieving facts to ground LLMs on the most accurate information and to give users insight into AI’s decision making process


















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