Machine Learning - IBM Research
2024年2月20日 · 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 - IBM Research
2021年7月12日 · 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. Our work
What is federated learning? - IBM Research
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.
Artificial Intelligence - IBM Research
2024年12月20日 · We’re inventing what’s next in AI research. Explore our recent work, access unique toolkits, and discover the breadth of topics that matter to us.
Neuro-symbolic AI - IBM Research
2021年10月12日 · 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.
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 retrieval-augmented generation? - IBM Research
As the name suggests, RAG has two phases: retrieval and content generation. In the retrieval phase, algorithms search for and retrieve snippets of information relevant to the user’s prompt or question.
Systematic literature review: Quantum machine learning and its ...
Nonetheless, there are other fields like machine learning, finance, or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a review of the literature published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms used in quantum machine learning and their applications.
Practical Machine Learning for Developing Countries - IBM Research
Methods such as transfer learning will not fully solve the problem either due to bias in pre-training datasets that do not reflect real test cases in developing countries as well as the prohibitive cost of fine-tuning these large models.
Quantum kernels can solve machine learning problems that are …
2021年7月12日 · Machine learning is the theme of this year's Qiskit Global Summer School, focusing specifically on the quantum kernel estimation algorithm plus its strengths and limitations. We hope to continue building and disseminating quantum computing research for the wider quantum community, and hope that we can develop a quantum ecosystem that can generate more research, together with the help and ...