Collaborating Authors


Artificial Intelligence to Support UAVs in Healthcare


Unmanned aerial vehicles (UAVs), or simply drones, are used in a plethora of civil applications due to their ease of deployment, low maintenance cost, high mobility, and ability to hover. A main advantage of drones is that, in contrast to other vehicles, they are not restricted to traveling over a road network and thus, can swiftly move over disperse locations. Such vehicles are utilized for many applications such as the real-time monitoring of road traffic, civil infrastructure inspection, wireless coverage, delivery of goods, security and surveillance, precision agriculture, and healthcare. Regarding the latter, drones can be utilized in natural disaster relief, as search and rescue units, as transfer units, and to support telemedicine. For drones to be efficient in such applications, their scheduled and coordinated flying is crucial. Moreover, given that drones typically use an electric motor and store the required energy in batteries, their scheduled charging is crucial to maximizing their availability.Controlling drones demands efficient algorithms that can solve problems that involve a large number of heterogeneous entities (e.g., drones’ owners), each one having its own goals, needs, and incentives (e.g., amount of goods to transport), while they operate in highly dynamic environments (e.g., variable number of drones) and having to deal with a number of uncertainties (e.g., future requests, emergency situations). In this context, artificial intelligence (AI) techniq...

Super-successful AI Investment Technologies Will Likely Never Be Publicly Available


It's tempting to see AI as a solution to building a super-success investment engine. After all, if AI can solve text-to-speech or self-driving cars or landing rockets vertically, couldn't an artificially intelligent investing engine with access to all stock market, economy, weather, and trends data vastly outpace human investors and guarantee massive returns? And won't we be able to simply ask Alexa to buy a stock that's going to triple in value in six months? Well, never say never, but it's unlikely. One is that investment AI engines are returning benefits right now, but not Everest-sized performance that will blow your financial socks off and make you fire your investment advisor.

An easier way to teach robots new skills


MIT researchers have developed a system that enables a robot to learn a new pick-and-place task based on only a handful of human examples. This could allow a human to reprogram a robot to grasp never-before-seen objects, presented in random poses, in about 15 minutes. With e-commerce orders pouring in, a warehouse robot picks mugs off a shelf and places them into boxes for shipping. Everything is humming along, until the warehouse processes a change and the robot must now grasp taller, narrower mugs that are stored upside down. Reprogramming that robot involves hand-labeling thousands of images that show it how to grasp these new mugs, then training the system all over again.

Modern Computing: A Short History, 1945-2022


Inspired by A New History of Modern Computing by Thomas Haigh and Paul E. Ceruzzi. But the selection of key events in the journey from ENIAC to Tesla, from Data Processing to Big Data, is mine. This was the first computer made by Apple Computers Inc, which became one of the fastest growing ... [ ] companies in history, launching a number of innovative and influential computer hardware and software products. Most home computer users in the 1970s were hobbyists who designed and assembled their own machines. The Apple I, devised in a bedroom by Steve Wozniak, Steven Jobs and Ron Wayne, was a basic circuit board to which enthusiasts would add display units and keyboards. April 1945 John von Neumann's "First Draft of a Report on the EDVAC," often called the founding document of modern computing, defines "the stored program concept." July 1945 Vannevar Bush publishes "As We May Think," in which he envisions the "Memex," a memory extension device serving as a large personal repository of information that could be instantly retrieved through associative links.

Explorations in Cyber-Physical Systems Education

Communications of the ACM

The field of CPS draws from several areas in computer science, electrical engineering, and other engineering disciplines, including computer architecture, embedded systems, programming languages, software engineering, real-time systems, operating systems and networking, formal methods, algorithms, computation theory, control theory, signal processing, robotics, sensors and actuators, and computer security. Similarly, over the past 14 years, we have had students from computer science, electrical and computer engineering, mechanical engineering, civil engineering, and even bioengineering. Integrating this bewildering diversity of subject areas into a coherent whole for students with such a wide breadth of backgrounds has been a challenge we had to overcome. One approach would have been to not attempt such an integration. Instead, we could have opted for a collection of courses that together cover all the key areas in CPS.

Commerce's BIS Can Help Stop China's Quest For AI Dominance


AI-enabled cameras capture schoolchildren in China's Xinjiang region. Cameras identify and track ... [ ] Uighurs and other ethnic minorities face in the heavily-policed region. Artificial intelligence (AI) is a key technologies of the future – and not just because of civilian applications like ecommerce, self-driving cars, and online search and personal assistants. AI will transform militaries through innovations in intelligence, surveillance, reconnaissance, logistics, command and control capabilities, weapons systems, and so on. Last year a landmark U.S. National Security Commission on Artificial Intelligence (NSCAI) report found, "The ability of a machine to perceive, evaluate, and act more quickly and accurately than a human represents a competitive advantage in any field--civilian or military."

What intelligent workload balancing means for RPA - Information Age


The relatively new concept of intelligent workload balancing is an important one to consider when operating RPA, because it determines whether tasks are more suitable for human employees or their digital colleagues. With this in mind, five industry experts identify particular ways in which this can be applied to this space. Firstly, intelligent workload balancing can be used to check that bots can adhere to rules set up by the company. "The ability to automatically decide if an activity requires human intervention or if it can be performed by a bot is usually called'intelligent workload balancing'," said Sathya Srinivasan, vice-president, solutions consulting (Partners) at Appian. "The intelligence comes from the business rules that determine who is the best candidate to complete the work – human or bot. If human, which department, group, experience level or management is best to handle this case, and if bot, what does it take to bring a bot in, how flexible can a bot cater to different types of requests. Chris Duddridge, area vice president and managing director UKI at UiPath, explores the link between compliance and robotic process automation. "To be truly effective, a bot must be able to work across a wide set of parameters.

Council Post: Three Ways AI Is Impacting The Automobile Industry


Wendy Gonzalez is the CEO of Sama, the provider of accurate data for ambitious AI. Autonomous cars are as intrinsic to visions of the future as holograms and space travel. Since the birth of science fiction, the automobile has been seen as the final frontier of technological innovation. However, when we look around at our cities today, cars can often seem stuck in the past. The reality is that the vision for the automotive industry has far exceeded the pace of its progress.

Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle

Journal of Artificial Intelligence Research

This paper addresses modeling and simulating pedestrian trajectories when interacting with an autonomous vehicle in a shared space. Most pedestrian–vehicle interaction models are not suitable for predicting individual trajectories. Data-driven models yield accurate predictions but lack generalizability to new scenarios, usually do not run in real time and produce results that are poorly explainable. Current expert models do not deal with the diversity of possible pedestrian interactions with the vehicle in a shared space and lack microscopic validation. We propose an expert pedestrian model that combines the social force model and a new decision model for anticipating pedestrian–vehicle interactions. The proposed model integrates different observed pedestrian behaviors, as well as the behaviors of the social groups of pedestrians, in diverse interaction scenarios with a car. We calibrate the model by fitting the parameters values on a training set. We validate the model and evaluate its predictive potential through qualitative and quantitative comparisons with ground truth trajectories. The proposed model reproduces observed behaviors that have not been replicated by the social force model and outperforms the social force model at predicting pedestrian behavior around the vehicle on the used dataset. The model generates explainable and real-time trajectory predictions. Additional evaluation on a new dataset shows that the model generalizes well to new scenarios and can be applied to an autonomous vehicle embedded prediction.

Data Distribution Shifts and Monitoring


Note: This note is a work-in-progress, created for the course CS 329S: Machine Learning Systems Design (Stanford, 2022). For the fully developed text, see th...