Blockchain accelerates the adoption of emerging technologies, including Artificial Intelligence, Cloud, and IoT, by bringing in the missing element of trust required for a business to embrace these technologies at scale fully. In the next side, blockchain business networks benefit from integrating these technologies into modern blockchain platforms and applications. Blockchain and Artificial Intelligence will stand in reshaping industries. Both technologies came with immense benefits and brought their challenges for adoption. It is also fair that the hype surrounding these technologies may be unprecedented, so some may view the thought of getting these two ingredients together as brewing a modern-day version of IT pixie dust. Simultaneously, there is a logical way to think about this mash-up that is both sensible and pragmatic.

Aerial robots can enhance their safe and agile navigation in complex and cluttered environments by efficiently exploiting the information collected during a given task. In this paper, we address the learning model predictive control problem for quadrotors. We design a learning receding--horizon nonlinear control strategy directly formulated on the system nonlinear manifold configuration space SO(3)xR^3. The proposed approach exploits past successful task iterations to improve the system performance over time while respecting system dynamics and actuator constraints. We further relax its computational complexity making it compatible with real-time quadrotor control requirements. We show the effectiveness of the proposed approach in learning a minimum time control task, respecting dynamics, actuators, and environment constraints. Several experiments in simulation and real-world set-up validate the proposed approach.

The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems and also limits its full potential. In many other areas of machine learning, AutoML has shown that it is possible to automate such design choices, and AutoML has also yielded promising initial results when applied to RL. However, Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL, that naturally produce a different set of methods. As such, AutoRL has been emerging as an important area of research in RL, providing promise in a variety of applications from RNA design to playing games, such as Go. Given the diversity of methods and environments considered in RL, much of the research has been conducted in distinct subfields, ranging from meta-learning to evolution. In this survey, we seek to unify the field of AutoRL, provide a common taxonomy, discuss each area in detail and pose open problems of interest to researchers going forward.

A supercomputer in the US called'Frontier' has become the fastest in the world, beating its closest rival in Japan. Frontier, based at the US Department of Energy's Oak Ridge National Laboratory in Tennessee, is the first to achieve a level of computing known as'exascale'. Exascale refers to a system that can perform at least one quintillion operations per second – a billion billion calculations, or 1 followed by 18 zeroes. This makes Frontier more than twice as powerful than the Fugaku supercomputer in Japan, which was deemed the world's fastest supercomputer back in June 2020. Frontier will allow scientists to develop technologies for the US's energy, economic and national security, said Oak Ridge National Laboratory, and solve computational problems that were impossible to do just five years ago.

Less than a decade ago, "wait and see" arguments about climate change still circulated. "We often hear that there is a'scientific consensus' about climate change," physicist Steven E. Koonin wrote in The Wall Street Journal in 2014. "But as far as the computer models go, there isn't a useful consensus at the level of detail relevant to assessing human influences." The idea was that the world needed more data before it could respond to the threat posed by global warming--assuming such research indicated a response was even necessary. Today, outright denialism is dormant, but delay tactics have never been more in vogue.

This work is concerned with the problem of planning trajectories and assigning tasks for a Multi-Agent System (MAS) comprised of differential drive robots. We propose a multirate hierarchical control structure that employs a planner based on robust Model Predictive Control (MPC) with mixed-integer programming (MIP) encoding. The planner computes trajectories and assigns tasks for each element of the group in real-time, while also guaranteeing the communication network of the MAS to be robustly connected at all times. Additionally, we provide a data-based methodology to estimate the disturbances sets required by the robust MPC formulation. The results are demonstrated with experiments in two obstacle-filled scenarios

Huanfeng Shen, Wuhan University ([email protected]), Jane Liu, University of Toronto ([email protected]), Wenping Yuan, Sun Yat-Sen University ([email protected]), Yongguang Zhang, Nanjing University ([email protected]), Holly Croft, University of Sheffield ([email protected]), Xiaobin Guan, Wuhan University ([email protected]). The dramatic increase in anthropogenic carbon emissions over the last five decades has already led to substantial damage to our environment, including increases in extreme weatherevents, loss of biodiversity, and a rise in sea level. Carbon neutrality, i.e., net-zero anthropogenic carbon emissions, is necessary to ensure the sustainable future of human beings, and hundreds of countries have pledged to achieve this goal by mid-century. Remote sensing techniques can acquire frequent observations of the Earth with various temporal and spatial resolutions, and provide substantial information for carbon emission monitoring and carbon cycle modeling. Remote sensing observations not only can be directly applied to retrieve the atmospheric concentrations of greenhouse gases (e.g., CO2, CO, CH4, CFCs, O3, et al.), but also can be employed to investigate the carbon budget of natural ecosystems.

The collaboration will focus on creating and deploying Baker Hughes IAM solutions that use digital technologies to help improve the safety, efficiency, and emissions profile of industrial machines, field equipment, and other physical assets. Applying their individual strengths, the four companies will collaborate on Baker Hughes IAM capabilities that help optimize plant equipment, operational processes, and business operations through improved uptime, increased operational flexibility, capital planning, and energy efficiency management. The solutions will be designed for industries including oil and gas; renewable energy and thermal power generation; metals and mining; chemicals; and pulp and paper. Baker Hughes, C3 AI, Accenture and Microsoft will also explore collaborating on solutions that help achieve net-zero carbon emissions and decarbonize energy and industrial sectors, including emissions management. "This collaboration accelerates our growth strategy to provide differentiated IAM solutions that enhance our customer's industrial operations by optimizing the performance of industrial equipment and processes," said Lorenzo Simonelli, Baker Hughes chairman and CEO.

The Massachusetts Institute of Technology (MIT) researchers have found a way for drivers to avoid idling at red lights through Artificial Intelligence (AI). In a new study, MIT researchers demonstrate a machine-learning approach that can help control a fleet of autonomous vehicles as they approach a signalized intersection to help traffic go smoothly. The researchers performed simulations that not only will the approach help with traffic, but can also reduce fuel consumption and emissions will improve the average vehicle speed. This approach will deliver the best results if all cars on the road are autonomous. However, even if only 25 percent use this, it may still deliver significant results.

China has launched the world's first drone carrier, a vessel that will likely be used for military purposes. The ship, Zhu Hai Yun, can be controlled remotely to navigate autonomously in open water. Zhu Hai Yun, launched last week, has a wide deck equipped to carry dozens of unmanned vehicles including drones, unmanned ships and submersibles, reported the South China Morning Post. This also makes it a powerful platform to launch UAVs for military purposes, though Beijing claims the vehicle has been designed for marine research purposes. The drone mothership is expected to be commissioned by the end of the year after sea trials. According to the Chinese state-run Science and Technology Daily, the ship could be an "efficient tool in marine disaster prevention and mitigation, environmental monitoring and offshore wind farm maintenance besides delivering accurate marine information."