This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated vehicles. We present a typical processing pipeline, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilization, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modeling and anomaly detection. Our main goal is to guide traffic analysts to develop their own custom-built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL-based algorithms proposed for each step. We also review existing open-source tools and public datasets that can help train DL models. To be more specific, we review exemplary traffic problems and mentioned requires steps for each problem. Besides, we investigate connections to the closely related research areas of drivers' cognition evaluation, Crowd-sourcing-based monitoring systems, Edge Computing in roadside infrastructures, Automated Driving Systems (ADS)-equipped vehicles, and highlight the missing gaps. Finally, we review commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems.

Reinforcement learning has witnessed recent applications to a variety of tasks in quantum programming. The underlying assumption is that those tasks could be modeled as Markov Decision Processes (MDPs). Here, we investigate the feasibility of this assumption by exploring its consequences for two fundamental tasks in quantum programming: state preparation and gate compilation. By forming discrete MDPs, focusing exclusively on the single-qubit case (both with and without noise), we solve for the optimal policy exactly through policy iteration. We find optimal paths that correspond to the shortest possible sequence of gates to prepare a state, or compile a gate, up to some target accuracy. As an example, we find sequences of $H$ and $T$ gates with length as small as $11$ producing $\sim 99\%$ fidelity for states of the form $(HT)^{n} |0\rangle$ with values as large as $n=10^{10}$. In the presence of gate noise, we demonstrate how the optimal policy adapts to the effects of noisy gates in order to achieve a higher state fidelity. Our work shows that one can meaningfully impose a discrete, stochastic and Markovian nature to a continuous, deterministic and non-Markovian quantum evolution, and provides theoretical insight into why reinforcement learning may be successfully used to find optimally short gate sequences in quantum programming.

Tensegrity robots, composed of rigid rods and flexible cables, are difficult to accurately model and control given the presence of complex dynamics and high number of DoFs. Differentiable physics engines have been recently proposed as a data-driven approach for model identification of such complex robotic systems. These engines are often executed at a high-frequency to achieve accurate simulation. Ground truth trajectories for training differentiable engines, however, are not typically available at such high frequencies due to limitations of real-world sensors. The present work focuses on this frequency mismatch, which impacts the modeling accuracy. We proposed a recurrent structure for a differentiable physics engine of tensegrity robots, which can be trained effectively even with low-frequency trajectories. To train this new recurrent engine in a robust way, this work introduces relative to prior work: (i) a new implicit integration scheme, (ii) a progressive training pipeline, and (iii) a differentiable collision checker. A model of NASA's icosahedron SUPERballBot on MuJoCo is used as the ground truth system to collect training data. Simulated experiments show that once the recurrent differentiable engine has been trained given the low-frequency trajectories from MuJoCo, it is able to match the behavior of MuJoCo's system. The criterion for success is whether a locomotion strategy learned using the differentiable engine can be transferred back to the ground-truth system and result in a similar motion. Notably, the amount of ground truth data needed to train the differentiable engine, such that the policy is transferable to the ground truth system, is 1% of the data needed to train the policy directly on the ground-truth system.

In this third part of my series on the future of work, I want to deal with the impact of automation, in particular robots and artificial intelligence (AI) on jobs. I have covered this issue of the relationship between human labour and machines before, including robots and AI. But is there anything new that we can find after the COVID slump? The leading American mainstream expert on the impact of automation on future jobs is Daron Acemoglu, Institute Professor at MIT. In testimony to the US Congress, Acemoglu started by reminding Congress that automation was not a recent phenomenon.

The NHS plans to use drones to fly chemotherapy drugs to cancer patients in England to avoid the need for long journeys to collect them. The devices will transport doses from Portsmouth to the Isle of Wight in a trial that, if successful, will lead to drones being used for similar drops elsewhere. They will take 30 minutes to travel across the Solent, which will save patients on the island a three to four-hour round trip by ferry or hovercraft. On Tuesday, Amanda Pritchard, NHS England's chief executive, unveiled the move to help mark the 74th anniversary of the health service's creation by the postwar Labour government. "Delivering chemo by drone is another extraordinary development for cancer patients and shows how the NHS will stop at nothing to ensure people get the treatment they need as promptly as possible, while also cutting costs and carbon emissions," she said.

Darktrace has enhanced its flagship AI cybersecurity platform with 70 additional machine learning models and over 80 new features. The Cambridge-based firm was founded by mathematicians and cyber defense experts in 2013 and uses self-learning AI to protect enterprises across all industry sectors. Machine learning is used to make thousands of "micro-level" decisions in the background as part of Darktrace's autonomous response technology called Antigena. Antigena has been improved with 70 new machine learning models to bolster its ability to autonomously neutralise attacks in real-time. "The hallmark of a great AI solution is the ability to surpass automation to seamlessly blend into users' everyday work rhythm," said Jack Stockdale OBE, CTO of Darktrace.

In brief Numerous people start to believe they're interacting with something sentient when they talk to AI chatbots, according to the CEO of Replika, an app that allows users to design their own virtual companions. People can customize how their chatbots look and pay for extra features like certain personality traits on Replika. Millions have downloaded the app and many chat regularly to their made-up bots. Some even begin to think their digital pals are real entities that are sentient. "We're not talking about crazy people or people who are hallucinating or having delusions," the company's founder and CEO, Eugenia Kuyda, told Reuters.

China has reportedly created an artificial intelligence (AI) system that can assess the loyalty of Communist Party members. According to Didi Tang, a reporter for the Times in Beijing, the system has been developed by researchers at Hefei Comprehensive National Science Centre. It can analyse facial expressions and brain waves of Communist Party members to determine how receptive they are to'thought education'. Tang says the technology was detailed in an article that was uploaded to the internet on July 1 and deleted shortly afterwards. The artificial intelligence (AI) system can check the loyalty of Communist Party members.

Many have heralded artificial intelligence as a force-multiplier for defence and intelligence capabilities. Do you want armed autonomous vehicles to comply with legal and ethical obligations as set out in the Royal Australian Navy's robotics, autonomous systems and AI strategy? Do you want to more effectively analyse intelligence to predict what an adversary will do next? And AI's proponents are right--it could, and likely will, do all of those things, but not yet. Its ability to spot patterns, compute figures and calculate optimum solutions on an'if X happens then do Y' basis is now unmatched by any human being.

IEEE International Conference on Robotics and Automation (ICRA) has given many opportunities over the years for researchers, industries, students and the enthusiasts to network and collaborate. In a similar fashion, this year in 2022, there were great number of opportunities to involve and engage as well including networking events. A week before the conference, IEEE Robotics and Automation Society, Women in Engineering (RAS WiE) organized a free virtual event for the enthusiasts from the robotics research field to learn and discuss the aspects of Becoming a Plenary/Keynote Speaker in an International Robotics Conference. Three extraordinary robotics researchers, Dr. Vandi Verma, NASA Jet Propulsion Laboratory, USA, Dr. Katherine Kuchenbecker, Director, Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Germany and Prof. Lydia Kavraki, Greek-American computer scientist, the Noah Harding Professor of Computer Science, a professor of bioengineering, electrical and computer engineering, and mechanical engineering, Rice University discussed their career paths, opportunities and difficulties they've faced along their journey as a woman in engineering, mentoring, STEM promotion and work-life balance. The panelists also shared their invaluable personal experience and discussed the importance of learning together. There were a lot of in-depth discussions duing the workshop.