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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
  • 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
  • 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
  • Artificial Intelligence - IBM Research
    AI for Code AI for Supply Chain AI Testing Automated AI Causality Computer Vision Conversational AI Explainable AI Fairness, Accountability, Transparency Foundation Models Generative AI Granite Human-Centered AI Knowledge and Reasoning Machine Learning Natural Language Processing Neuro-symbolic AI Speech Trustworthy AI Trustworthy Generation
  • 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
  • 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
  • New analog AI chip design uses much less power for AI tasks
    It’s possible to build analog AI chips that can handle natural-language AI tasks with estimated 14 times more energy efficiency
  • 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





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