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- Dictionarymachine learning
noun
- 1. the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data: "the application of machine learning to biological databases has increased"
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Feb 20, 2024 · Machine Learning. 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.
Since then, machine learning for structured data has become one of the major research areas in data mining and machine learning. Proud of our successes, we are actively tackling the frontiers in machine learning and data mining, and applying the results to the real world, taking full advantage of our merit of proximity to advanced companies and markets in Japan.
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.
Today's AI is narrow. Applying trained models to new challenges requires an immense amount of new data training, and time. We need AI that combines different forms of knowledge, unpacks causal relationships, and learns new things on its own. In short, AI must have fluid intelligence— and that's exactly what our AI research teams are building.
We’re working on ways to model user intents using machine learning methods and rule-based dialog flow to enhance the field of conversational AI. We’re building on frameworks such as Watson Assistant, DialogFlow, and lex to help ensure deep learning models are applicable for use in enterprise settings.
Aug 24, 2022 · In vertical federated learning, the data are complementary; movie and book reviews, for example, are combined to predict someone’s music preferences. Finally, in federated transfer learning, a pre-trained foundation model designed to perform one task, like detecting cars, is trained on another dataset to do something else, like identify cats.
Snap Machine Learning (Snap ML in short) is a library for training and scoring traditional machine learning models. Such traditional models power most of today's machine learning applications in business and are very popular among practitioners as well (see the 2019 Kaggle survey for details). Snap ML has been designed to address some of the ...
Oct 12, 2021 · Neuro-symbolic AI. 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.
The Machine Learning for Drug Development and Causal Inference group is developing machine learning models for innovative drug discovery technologies and bringing them to fruition for IBM clients. Our researchers believe that drug discovery can benefit from technologies that learn from the rich clinical, omics, and molecular data being collected nowadays in large quantities.
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.
