arXiv subjects

Runyu Liu

Publications and source records attributed to Runyu Liu.

3 recordsLinked to original sources

arXiv2024

The infrastructure powering IBM's Gen AI model development

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings.

cs.DC↗

arXiv2018

An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as An Example

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred and forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that, the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction.

cs.AI↗

arXiv2016

Epsilon-Near-Zero Photonics Wires for Mid-Infrared Optical Lumped Circuitry

There has been recent interest in the development of optical analogues of lumped element circuitry, where optical elements act as effective optical inductors, capacitors, and resistors. Such optical circuitry requires the photonic equivalent of electrical wires, structures able carry optical frequency signals to and from the lumped circuit elements while simultaneously maintaining signal carrier wavelengths much larger than the size of the lumped elements. Here we demonstrate the design, fabrication, and characterization of hybrid metal/doped-semiconductor 'photonic wires' operating at optical frequencies with effective indices of propagation near-zero. Our samples are characterized by polarization and angle-dependent FTIR spectroscopy and modeled by finite element methods and rigorous coupled wave analysis. We demonstrate coupling to such photonic wires from free space, and show the effective wavelength of the excited mode to be approximately an order of magnitude larger than the free-space wavelength of our light. The operational length of the photonic wires approaches twice the free space wavelength, significantly longer than what is achievable with bulk epsilon near zero materials. The novel architecture utilized in our hybrid waveguides allows for significant design flexibility by control of the semiconductor material's optical properties and the sample geometry. In addition, by utilizing a semiconductor-based architecture, our photonic wires can be designed to monolithically integrate the optical equivalents of capacitive, inductive, and resistive lumped circuit elements, as well optoelectronic sources and detectors. As such, the demonstrated photonic wires have the potential to provide a key component, and a realistic framework, for the development of optical circuitry.

physics.optics↗