IBM Quantum systems can now measure and reset a qubit in the middle of a circuit execution.
At AAAI, our team presented two new multilingual research techniques that enable AI to understand different languages while only trained on one.
Our Zurich-based team of researchers has just managed to efficiently guide visible light through a silicon wire – an important milestone towards faster, more efficient integrated circuits. Our low-loss silicon waveguide could enable new photonic chip designs for applications that rely on visible light, and could lead to more efficient lasers and modulators used in telecoms.
PAGs play a vital role in the manufacturing of computer chips. They are also one of several classes of chemical compounds that have recently come under enhanced scrutiny from environmental regulators. Researchers have been racing to create more sustainable ones – but the traditional process of discovering new materials is too slow, too costly, and too risky. So IBM researchers have turned to AI for help – and created new PAGs much, much faster, paving the way to the era of Accelerated Discovery.
Our team has developed an AI that verifies other AIs’ ‘fairness’ by generating a set of counterfactual text samples and testing machine learning systems without supervision.
Quantum phase estimation serves as a core building block of many other quantum algorithms due to its potential to provide exponential speedups.
Software reliant on this nascent technology, one rooted in the physical laws of matter at the smallest scales, could soon revolutionize computing forever.
In a recent paper introduced at the 2021 AAAI Conference on Artificial Intelligence (AAAI), we describe an AI that trades off ‘exploration’ of the world with ‘exploitation’ of its action strategy to maximize rewards. In Reinforcement Learning, an AI gets a reward – such as a bag of gold behind a locked door in a video game – every time it reaches specific desirable states. We have greatly improved this exploration vs exploitation tradeoff using additional commonsense knowledge – in the form of crowdsourced text. Our work could lead to better mapping and navigation applications, and to a new generation of interactive assistive agents able to reason like humans.
We use AI to automatically break down the overall application by representing application code as graphs. Our AI relies on Graph Representation Learning – a popular method in deep learning. Graphs are a natural representation for software and applications. We translated the application to a graph where the programs become nodes. Their relationships with other programs become edges and determine the boundary to separate the nodes of common business functionality.
Heike Riel's recent appointment as an APS Fellow attests her leadership in science and technology. While many distinguished physicists are part of the APS, only a handful are elected to the fellowship — and even fewer still are female. So when Riel learned last fall that she had been selected, she was deeply touched. “It’s truly an honor and I am humbled to have received this recognition from one of the most highly respected organizations for professionals in physics,” she says. “I am very grateful for my colleagues as well as the teams and institutions that have supported me along the way.”
To help the developers that update legacy applications, our team has created Mono2Micro (monolith-to-microservice) – an AI assistant that modernizes legacy applications to help move them to the cloud as microservices. Our tool simplifies and speeds up the often error-prone “application refactoring” process of partitioning and preserving the original semantics of the legacy, monolith applications.