|
- 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
- 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
- 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
- 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
- Machine Learning for Dynamical Systems - IBM Research
Machine learning and dynamic systems can be combined to explore the intersection of their common mathematical features In one direction, machine learning algorithms can be employed to infer nonlinear operators governing dynamical systems from data, with the goal of improving computational requirements for the simulation of very large and
- 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
- 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
|
|
|