AI and Machine Learning have revolutionized our world today. From healthcare to automobiles, from sports to banking, almost all the sectors are realizing the power that AI brings with itself to deliver better solutions. The field of Satellite Imagery is also undergoing massive development, and machine learning solutions have provided some positive results. ML algorithms have proved to be quite a boon for analyzing satellite imagery. However, accessing the data becomes a bottleneck sometimes. This is primarily due to the massive size of the satellite images and the fact that analyzing them requires some amount of domain expertise.

Recent researches on robotics have shown significant improvement, spanning from algorithms, mechanics to hardware architectures. Robotics, including manipulators, legged robots, drones, and autonomous vehicles, are now widely applied in diverse scenarios. However, the high computation and data complexity of robotic algorithms pose great challenges to its applications. On the one hand, CPU platform is flexible to handle multiple robotic tasks. GPU platform has higher computational capacities and easy-touse development frameworks, so they have been widely adopted in several applications. On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios. With specialized designed hardware logic and algorithm kernels, FPGA-based accelerators can surpass CPU and GPU in performance and energy efficiency. In this paper, we give an overview of previous work on FPGA-based robotic accelerators covering different stages of the robotic system pipeline. An analysis of software and hardware optimization techniques and main technical issues is presented, along with some commercial and space applications, to serve as a guide for future work. Therefore, the computation and storage complexity, as well as real-time and power constraints of the robotic system, Over the last decade, we have seen significant progress hinders its wide application in latency-critical or power-limited in the development of robotics, spanning from algorithms, scenarios [13]. Various robotic systems, like Therefore, it is essential to choose a proper compute platform manipulators, legged robots, unmanned aerial vehicles, selfdriving for the robotic system. CPU and GPU are two widely cars have been designed for search and rescue [1], [2], used commercial compute platforms. CPU is designed to exploration [3], [4], package delivery [5], entertainment [6], handle a wide range of tasks quickly and is often used to [7] and more applications and scenarios. These robots are develop novel algorithms. A typical CPU can achieve 10-on the rise of demonstrating their full potential. Take drones, 100 GFLOPS with below 1GOP/J power efficiency [14]. In a type of aerial robots, for example, the number of drones contrast, GPU is designed with thousands of processor cores has grown by 2.83x between 2015 and 2019 based on the running simultaneously, which enable massive parallelism. The typical GPU can perform up to 10 TOPS performance and registered number has reached 1.32 million in 2019, and the become a good candidate for high-performance scenarios. Recently, FFA expects this number will come to 1.59 billion by 2024.

Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class. If run using wind or solar power, the system also provides an efficient way to store electricity from these renewable but intermittent sources. "Using clean electricity to convert CO2 into ethylene, which has a $60 billion global market, can improve the economics of both carbon capture and clean energy storage," says Professor Ted Sargent, one of the senior authors on a new paper published today in Nature.

We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. The resulting models represent dynamical systems with varying (i.e., \emph{liquid}) time-constants coupled to their hidden state, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations, and give rise to improved performance on time-series prediction tasks. To demonstrate these properties, we first take a theoretical approach to find bounds over their dynamics, and compute their expressive power by the \emph{trajectory length} measure in a latent trajectory space. We then conduct a series of time-series prediction experiments to manifest the approximation capability of Liquid Time-Constant Networks (LTCs) compared to modern RNNs. Code and data are available at https://github.com/raminmh/liquid_time_constant_networks

The Mayflower is one-step closer to sailing from England to Plymouth – but this time it will be without a crew. Powered artificial intelligence, the autonomous ship is set to start trials in England and will be unveiled on September 16 in honor of the 400th anniversary the original vessel made its journey in 1620. The Mayflower will undergo several trips and missions over the next six months before it makes the more than 3,000 mile expedition across the Atlantic. The robot craft was set to embark on the journey next week, but has been delayed until April 2021 due to the coronavirus pandemic. The Mayflower Autonomous Ship (MAS), first revealed in 2017, is powered completely by reusable energy, mainly solar power, and made in partnership with University of Plymouth, autonomous craft specialists MSubs and public charity Promare which promotes marine research and exploration throughout the world.

Advances in technology have made many household appliances more energy efficient, and even given outdated old ones some energy-saving smarts, but addressing the power usage of each individual device across the home is still a tall order. Researchers at Cornell University have been working on more of a one-size-fits-all solution, developing a vibration-sensing device that can keep tabs on appliance usage through machine learning and lasers. The team points to smart homes of the future as its inspiration for developing the VibroSense device, imagining scenarios where the house itself knows when a washing machine has completed its cycle, when a microwave has finished heating food or a faucet is dripping. While replacing each appliance with smart versions or attaching specific sensors to them could be one way to tackle this, the Cornell team sees a more efficient way forward. "In order to have a smart home at this point, you'd need each device to be smart, which is not realistic; or you'd need to install separate sensors on each device or in each area," says Cheng Zhang, assistant professor of information science and senior author of the study.

For quantum optical technologies to become more practical, there is a need for large-scale integration of quantum photonic circuits on chips. This integration calls for scaling up key building blocks of these circuits – sources of particles of light – produced by single quantum optical emitters. Purdue University engineers created a new machine learning-assisted method that could make quantum photonic circuit development more efficient by rapidly preselecting these solid-state quantum emitters. The work is published in the journal Advanced Quantum Technologies. Researchers around the world have been exploring different ways to fabricate identical quantum sources by "transplanting" nanostructures containing single quantum optical emitters into conventional photonic chips.

With cloud computing IT services and resources can be uploaded to and retrieved from the Internet as opposed to direct connection to a server. Files can be kept on cloud-based storage systems rather than on local storage devices. According to IndustryWeek, a distributed computing paradigm edge computing brings computer data storage closer to the location where it is needed. In contrast to cloud computing, edge computing refers to decentralized data processing at the edge of the network, according to Klaus Schwab, founder and executive chairman of the World Economic Forum. The IIoT requires more of an edge-plus-cloud architecture rather than one based on purely centralized cloud; in order to transform productivity, products and services in the industrial world.

Lightning was a factor in many of these fires. But past blazes, including the 2018 Camp Fire that destroyed the town of Paradise, Calif., were started by faulty transmission equipment. In that case, a worn piece of metal that holds power lines, known as a C-hook, broke and dropped a high-voltage electric line that ignited that fire. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. In June, PG&E Corp., parent company of Pacific Gas and Electric Co., pleaded guilty to 84 counts of involuntary manslaughter for its role in sparking that fire.

The Institute for Robotic Process Automation & Artificial Intelligence defines RPA as follows, “Robotic process automation (RPA) is the application of technology that allows employees in a company to configure computer software or a bot to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses, and communicating with other digital systems.” In simple terms, RPA is the automation of repetitive, rule-based manual tasks (performed on windows) by the use of automation agents that can run attended or unattended without making any errors. RPA Segments: RPA is segmented into three major categories: 1. Attended RPA: In Attended RPA, the bot resides within the user’s machine & invoked by the user as per their need. Attended RPA is most prominently used by customer-facing functions such as customer service. This kind of RPA requires user intervention to make decisions or update based on conditions. Let us look at some scenarios where Attended RPA can be used: Making Decisions: A customer logs a trouble ticket for a solution to an issue within a software. There may be multiple ways to solve the problem. Basis the decision taken by the team, one can decide the particular RPA bot that needs to be run and trigger it manually Providing Input: While filling a CRM application, if a customer leaves a few fields blank by mistake, the RPA bot can stop and ask for those inputs to proceed further 2. Unattended RPA: Unattended RPA involves bots that perform tasks in batches based on automatic/timed triggers. There is absolutely no human intervention, and the bots run automatically to execute the tasks end to end. One of the scenarios that involve Unattended RPA is: Claim Settlement: Insurance claims validation and processing from analysis to updating for eligibility for a claim 3. Hybrid RPA: Hybrid RPA is a combination of both attended and unattended RPA. Hybrid RPA involves both attended and unattended bots working along with a human effort to achieve the business objective more effectively. It is generally touted as the most effective method of automation. It involves a fully integrated platform for intelligent automation that promises clear visibility, accountability, and governance across the process automation from start to completion. One of the examples of Hybrid RPA is: Ticket Solution: Updating tickets in the system based on errors and sending emails. Post the ticket log, ensuring a reply to the customer basis the resolutions/status Unit of automation – BOTS Bots are the automation agents that can be created by using various tools like Automation Anywhere, UI Path, Microsoft Power Automate, etc. These bots can run on from the local setup or can also be deployed on the cloud. Bots consist of the following two components that execute process automation: 1. Bot Program: The interface provided by RPA tools are claimed to be no-code-automation and can be done in a very short time. Some tools provide methods to create specific bots using programming languages like C#, Visual Basic, and Python. 2. Interaction with System: Bots interact with Windows/web and remote applications based on the functionality created and provide the desired output. Monitoring and Controlling the Deployed Bots Most of the RPA tools (Automation Anywhere, UI Path, Microsoft Power Automate, etc.) provide a control panel or a command module that can monitor the health of the bots. This process can help track statistics such as RPA Bot success rate and Bot failure reasons to take corrective actions. Let us look at some of the features of the Command Module of the RPA tools: 1. User Management: This panel provides the user the accessibility to control the bot functionality. There can be various roles assigned to different users within the tool, such as Admin, Developer, Sales, and Customer, to govern the bot. 2. Data Analytics: The Command center can process the data and run analytics on the data. The insights generated through analytics provide intelligence to the management and aide in making critical decisions. 3. Exceptions Reporting: It can capture the anomalies in the defined rule-based process, and then the bot can be modified to handle that specific scenario making it easy to catch exceptions in the system. RPA Use-cases in the Industry: RPA technology integrates smoothly with the existing IT infrastructure within an organization and does not require any large installations. Companies do not need to make significant investments to automate essential processes. Some of the everyday use cases in the industry around automating processes through RPA are: 1. Business processes: RPA can help in automating business processes such as Customer/Employee boarding, Invoice & Quotes processing, updating CRM, etc. 2. Web Tasks: Tasks such as logging into a website and performing operations to generate reports. Feed data into a system from sources like MS Excel, Mail, databases, etc. 3. Data Transfer: Transfer of data and automatic pull of relevant data from email or filled-in forms. This ensures that all departments across the organization can access current and correct data. 4. Web Data Extraction: RPA is very efficient in tasks that involve web scraping and navigating through pages. Web Data Extraction has applications such as resume screening, records validation, etc. 5. IT and System Administration Tasks: Tasks such as a regular check of software for bugs on employee systems and distribution of software for different employees. RPA can help in running a periodic audit within an organization to save repetitive efforts. 6. Data Backup and File Management: RPA can quickly help achieve automatic data backup on cloud or databases system logs. It can be used to organize files based on specific rules and, thus, help in organized record keeping. 7. Job Scheduling: Schedule jobs to run based on various triggers like time, mail, file available in a folder, etc. 8. Batch Data Processing: Activities such as restart and recovery, integration with security systems, sending alerts, classification of service types, etc. can be easily managed via RPA. 9. Automated Software Testing: RPA can help to automate manual testing processes, maintain the highest product quality, increase productivity, and free up QA testers to work on strategic projects. The Future of RPA: From Intelligent Automation to Hyper-Automation: RPA becomes more interesting with the addition of the capability and scope of hyper-automation. Gartner defines Hyper-automation as, “Hyper-automation deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans. Hyper-automation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.). It involves a combination of tools, including robotic process automation (RPA), intelligent business management software (iBPMS), and AI, with a goal of increasingly AI-driven decision making.” Some areas of hyper-automation include: IDP (Intelligent document processing) where the bots can utilize ML and OCR to process unstructured data and learn patterns in the data Supervised Machine learning modes can be used to support decision making Virtual Assistant, smart speakers, and chatbot-integration with RPA Conclusion: Even though RPA software can be found across all industries, significant adopters include insurance companies, banks, and telecom companies, and utility companies. RPA tools should consist of role-based security capability to ensure action specific permissions. They should also offer configuration as well as customization of encryption capabilities for securing certain data types. References: https://www.uipath.com/company/rpa-analyst-reports/2020-gartner-rpa-hyperautomation-predictions https://www.ibm.com/in-en/products/robotic-process-automation https://www2.deloitte.com/us/en/pages/operations/articles/global-robotic-process-automation-report.html https://research.aimultiple.com/what-is-robotic-process-automation/ https://www.processexcellencenetwork.com/rpa-artificial-intelligence/reports/intelligent-automation-rpa-and-ai-report-2020 https://imagine.automationanywhere.com/presentations/the-intelligent-automation-journey-at-sprint/ https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020/ (This blog was originally published here)