Collaborating Authors


Real-Time Machine Learning


Imagine this scenario: You have an app that uses machine learning and you want the app to learn from your user's data in real-time. That means as new user data is generated, your app is able to make predictions and perform training on the incoming data-stream to improve itself automatically. How would you go about building this? Take some time to stare at this chart, it's an example of this pipeline. That text data is being streamed in real-time using a software product called "Apache Kafka" to a model.

EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles Artificial Intelligence

Edge computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data processing and content sharing among vehicles and terrestrial edge computing (TEC) infrastructures (e.g., 5G base stations and roadside units) with little or no human intervention, plays a key role in the intelligent transportation systems. However, EC-IoV is heavily dependent on the connections and interactions between vehicles and TEC infrastructures, thus will break down in some remote areas where TEC infrastructures are unavailable (e.g., desert, isolated islands and disaster-stricken areas). Driven by the ubiquitous connections and global-area coverage, space-air-ground integrated networks (SAGINs) efficiently support seamless coverage and efficient resource management, represent the next frontier for edge computing. In light of this, we first review the state-of-the-art edge computing research for SAGINs in this article. After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas. The main objective of the framework is to minimize the task completion time and satellite resource usage. To this end, a pre-classification scheme is presented to reduce the size of action space, and a deep imitation learning (DIL) driven offloading and caching algorithm is proposed to achieve real-time decision making. Simulation results show the effectiveness of our proposed scheme. At last, we also discuss some technology challenges and future directions.

A Fast Edge-Based Synchronizer for Tasks in Real-Time Artificial Intelligence Applications Artificial Intelligence

Real-time artificial intelligence (AI) applications mapped onto edge computing need to perform data capture, process data, and device actuation within given bounds while using the available devices. Task synchronization across the devices is an important problem that affects the timely progress of an AI application by determining the quality of the captured data, time to process the data, and the quality of actuation. In this paper, we develop a fast edge-based synchronization scheme that can time align the execution of input-output tasks as well compute tasks. The primary idea of the fast synchronizer is to cluster the devices into groups that are highly synchronized in their task executions and statically determine few synchronization points using a game-theoretic solver. The cluster of devices use a late notification protocol to select the best point among the pre-computed synchronization points to reach a time aligned task execution as quickly as possible. We evaluate the performance of our synchronization scheme using trace-driven simulations and we compare the performance with existing distributed synchronization schemes for real-time AI application tasks. We implement our synchronization scheme and compare its training accuracy and training time with other parameter server synchronization frameworks.

LiveMap: Real-Time Dynamic Map in Automotive Edge Computing Artificial Intelligence

Autonomous driving needs various line-of-sight sensors to perceive surroundings that could be impaired under diverse environment uncertainties such as visual occlusion and extreme weather. To improve driving safety, we explore to wirelessly share perception information among connected vehicles within automotive edge computing networks. Sharing massive perception data in real time, however, is challenging under dynamic networking conditions and varying computation workloads. In this paper, we propose LiveMap, a real-time dynamic map, that detects, matches, and tracks objects on the road with crowdsourcing data from connected vehicles in sub-second. We develop the data plane of LiveMap that efficiently processes individual vehicle data with object detection, projection, feature extraction, object matching, and effectively integrates objects from multiple vehicles with object combination. We design the control plane of LiveMap that allows adaptive offloading of vehicle computations, and develop an intelligent vehicle scheduling and offloading algorithm to reduce the offloading latency of vehicles based on deep reinforcement learning (DRL) techniques. We implement LiveMap on a small-scale testbed and develop a large-scale network simulator. We evaluate the performance of LiveMap with both experiments and simulations, and the results show LiveMap reduces 34.1% average latency than the baseline solution.

Decision-Making in the era of AI


The term AI has been around for nearly 70 years, but it is only nowadays that AI is profoundly affecting and changing the way we live, work, interact and play. AI is on an unstoppable journey toward revolutionizing and disrupting diverse industries, and business leaders are embracing this trend with enthusiasm . So, why haven't we seen the rise of AI much earlier and is AI here to stay this time? Or is AI yet another new technology riding the hype wave? How can AI help us become better decision-makers? Many researchers agree with Larry Tesler (a computer scientist who invented copy-paste while working at Xerox and worked on human-machine interaction) that "AI is anything that hasn't been done yet!".

Appliance-Level Monitoring with Micro-Moment Smart Plugs Artificial Intelligence

Human population are striving against energy-related issues that not only affects society and the development of the world, but also causes global warming. A variety of broad approaches have been developed by both industry and the research community. However, there is an ever increasing need for comprehensive, end-to-end solutions aimed at transforming human behavior rather than device metrics and benchmarks. In this paper, a micro-moment-based smart plug system is proposed as part of a larger multi-appliance energy efficiency program. The smart plug, which includes two sub-units: the power consumption unit and environmental monitoring unit collect energy consumption of appliances along with contextual information, such as temperature, humidity, luminosity and room occupancy respectively. The plug also allows home automation capability. With the accompanying mobile application, end-users can visualize energy consumption data along with ambient environmental information. Current implementation results show that the proposed system delivers cost-effective deployment while maintaining adequate computation and wireless performance.

On AI And Ad Tech: 3 Questions For IBM's Bob Lord


IBM has been banging the drum for years about the role artificial intelligence can play to support everything from cancer treatment to retail personalization. More recently, though, IBM has started to prioritize practical advertising applications of its cognitive computing system, Watson. Last month, IBM brought its AI to ad tech through partnerships with Xandr, Magnite, Nielsen, MediaMath, LiveRamp and Beeswax. The move followed a steady stream of Watson Advertising announcements involving AI in advertising. In January, IBM built Advertising Accelerator, a tool that helps predict the best ads to run and tests creative versions in real time during a campaign.

Driving business opportunities at the Edge - TechHQ


Edge computing presents organizations with a significant leap in business opportunity. Much has been written about the benefits of the Internet of Things (IoT), but it is now clear that these benefits can only be truly realized with Edge computing. Limiting your organization to only adopting central cloud computing simply won't support your future IoT needs. Today, every organization needs to be a digital organization, powered by data, running in a multi-cloud world. Recognizing that multi-cloud actually begins at the point of data creation – the Edge – the value in the future is in combining Edge computing with IoT.

Citi Ventures is shifting from artificial intelligence to artificial enlightenment


Vanessa Colella is Citi's chief innovation officer and leads the Citi Ventures and Citi Productivity teams. Colella's goal is to accelerate and discover new sources of value by championing innovation so that Citi can compete more effectively in a world of technological, behavioral, and societal change. The Citi Ventures team drives innovation by exploring, incubating, and investing in new ideas and partnering with category-defining startups to help people, business and communities thrive. The Citi Productivity team works to transform the employee experience by leveraging the power of process simplification, operating model redesign, and new technologies to help Citi increase efficiency and effectiveness. Before assuming the role of Chief Innovation Officer, Colella led venture investing and D10X for Citi Ventures, and previously ran marketing for Citi's North American Consumer Bank.

Review: Kinetica analyzes billions of rows in real time


In 2009, the future founders of Kinetica came up empty when trying to find an existing database that could give the United States Army Intelligence and Security Command (INSCOM) at Fort Belvoir (Virginia) the ability to track millions of different signals in real time to evaluate national security threats. So they built a new database from the ground up, centered on massive parallelization combining the power of the GPU and CPU to explore and visualize data in space and time. By 2014 they were attracting other customers, and in 2016 they incorporated as Kinetica. The current version of this database is the heart of Kinetica 7, now expanded in scope to be the Kinetica Active Analytics Platform. The platform combines historical and streaming data analytics, location intelligence, and machine learning in a high-performance, cloud-ready package.