IBM's quantum systems powered 46 non-IBM presentations in order to help discover new algorithms, simulate condensed matter and many-body systems, explore the frontiers of quantum mechanics and particle physics, and push the field of quantum information science forward overall.
IBM Research has partnered with Red Hat to bring iter8 into Kiali. Iter8 lets developers automate the progressive rollout of new microservice versions. From Kiali, developers can launch these rollouts interactively, watch their progress while iter8 shifts user traffic to the best microservice version, gain real-time insights into how competing versions (two or more) perform, and uncover trends on service metrics across versions.
We're excited to announce the IBM Quantum Awards: Open Science Prize, an award totaling $100,000 for any person or team who can devise an open source solution to two important challenges at the forefront of quantum computing based on superconducting qubits: reducing gate errors, and measuring graph state fidelity.
What impact do measures such as shelter-in-place, mask wearing, and social distancing have on the number of COVID-19 cases? How do the COVID-19 quarantine measures that have been implemented by North American countries compare to South American countries? These are just a few questions about the wide range of non-pharmaceutical interventions (NPIs) that have been applied by governments, globally.
IBM Research recently announced the community edition of a framework for federated learning.
No organization is safe against cybercrime. Recent studies have shown that these crimes will cost the world well over $5 trillion a year by 2024. Cyber attackers breach corporate networks using a myriad of techniques, with application vulnerabilities corresponding to 25% of all exploitable attack vectors. More disturbing is that these attacks can go unnoticed […]
IBM Research launches new AI Experiments hub featuring prototypes of tools and resources that will unleash the power of AI.
Here I describe an approach to efficiently train deep learning models on machine learning cloud platforms (e.g., IBM Watson Machine Learning) when the training dataset consists of a large number of small files (e.g., JPEG format) and is stored in an object store like IBM Cloud Object Storage (COS). As an example, I train a […]
In a previous post we explained how to write a probabilistic model using Edward and run it on the IBM Watson Machine Learning (WML) platform. In this post, we discuss the same example written in Pyro, a deep probabilistic programming language built on top of PyTorch. Deep probabilistic programming languages (DPPLs) such as Edward and […]
Edward is a deep probabilistic programming language (DPPL), that is, a language for specifying both deep neural networks and probabilistic models. DPPLs draw upon programming languages, Bayesian statistics, and deep learning to ease the development of powerful AI applications. Probabilistic languages let the user express a probabilistic model as a program with an intuitive formalism […]