Artificial Intelligence
Pursuing computing advances to create intelligent machines that complement human reasoning to augment and enrich our experience and competencies.
Highlights
Register to watch Harnessing High-fidelity Simulation for Autonomous Systems Through AirSim
Read more about Register to watch Harnessing High-fidelity Simulation for Autonomous Systems Through AirSim
A path to personalization: Using ML to subtype patients receiving digital mental health interventions
Read more about A path to personalization: Using ML to subtype patients receiving digital mental health interventions
Register to watch Transparency and Intelligibility Throughout the Machine Learning Life Cycle
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6,031 results found
Publication
Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions
to come soon.
Publication
How Incidental are the Incidents? Characterizing and Prioritizing Incidents for Large-Scale Online Service Systems
[Experience paper] Although tremendous efforts have been devoted to the quality assurance of online service systems, in reality, these systems still come across many incidents (i.e., unplanned interruptions and outages), which can decrease user satisfaction or cause economic loss. To better understand the characteristics of incidents and improve the incident management process, we perform the first large-scale empirical analysis of incidents collected from 18 real-world online service systems in a multinational technology company M. Surprisingly,…
Publication
Leveraging Structured Metadata for Improving Question Answering on the Web
We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models significantly outperform baseline models, which do not incorporate metadata.…
Publication
Identifying Linked Incidents in Large-scale Online Service Systems
In large-scale online service systems, incidents occur frequently due to a variety of causes, from updates of software and hardware to changes in operation environment. These incidents could significantly degrade system’s availability and customers’ satisfaction. Some incidents are linked because they are duplicate or inter-related. The linked incidents can greatly help on-call engineers find mitigation solutions and identify the root causes. In this work, we investigate the incidents and their links in a real-world incident…
Publication
Efficient Incident Identification from Multi-dimensional Issue Reports via Meta-heuristic Search
In large-scale cloud systems, unplanned service interruptions and outages may cause severe degradation of service availability. Such incidents can occur in a bursty manner, which will in turn deteriorate user satisfaction. Identifying incidents rapidly and accurately is critical to the cloud system operation and maintenance. In industrial practice, incidents are typically detected by analyzing the issue reports generated over time by monitoring cloud services. Identifying incidents in a large number of issue reports is quite…
Event
Accessible Computer Science Education Fall Workshop
The Accessible Learning Fall Workshop will be three intensive days of talks, discussions and planning for new research dedicated to improving learning experiences for people with disabilities. At this event, we will build a research driven coalition of civic, academic, and industrial leaders and practitioners to envision what evidence-driven tools, services and ecosystems could be built for making learning more accessible. Together we will develop research and action plans to conduct real world research and…
Publication
Estimating GPU Memory Consumption of Deep Learning Models
Deep learning (DL) has been increasingly adopted by a variety of software-intensive systems. Developers mainly use GPUs to accelerate the training, testing, and deployment of DL models. However, the GPU memory consumed by a DL model is often unknown to them before the DL job executes. Therefore, an improper choice of neural architecture or hyperparameters can cause such a job to run out of the limited GPU memory and fail. Our recent empirical study has…
Publication
Enhancing the Interoperability between Deep Learning Frameworks by Model Conversion
Deep learning (DL) has become one of the most successful machine learning techniques. To achieve the optimal development result, there are emerging requirements on the interoperability between DL frameworks that the trained model files and training/serving programs can be re-utilized. Faithful model conversion is a promising technology to enhance the framework interoperability in which a source model is transformed into the semantic equivalent in another target framework format. However, several major challenges need to be…
Publication
Towards Intelligent Incident Management: Why We Need it and How We Make it
The management of cloud service incidents (unplanned interruptions or outages of a service/product) greatly affects customer satisfaction and business revenue. After years of efforts, cloud enterprises are able to solve most incidents automatically and timely. However, in practice, we still observe critical service incidents that occurred in an unexpected manner and orchestrated diagnosis workflow failed to mitigate them. In order to accelerate the understanding of unprecedented incidents and provide actionable recommendations, modern incident management system…
Publication
Analyzing Information Leakage of Updates to Natural Language Models
To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models. We show that a differential analysis of language model snapshots before and after an update can reveal a surprising amount of detailed information about changes in the training data. We propose two new metrics—differential score and differential rank—for analyzing the leakage due to updates of natural language models. We perform leakage analysis using these…