Artificial Intelligence hasn't quite arrived in the project management sphere yet, but it's on its way. Gartner forecasts that 80 per cent of project management roles will be eliminated by 2030 as AI takes on traditional project management functions such as data collection, tracking and reporting. The same report highlights that programme and portfolio management (PPM) software is behind the times, and AI-enabled PPM is only just beginning to surface in the market. However, while some tasks will inevitably be automated, it opens up other opportunities for project managers. It's important to know the difference between how AI-enabled automation can change project management and how AI-enabled insights from massive databases can make a difference.
Tiny robots made using pollen could one day be used to clean contaminated water. Waste water from some factories contains mercury, a metal that can cause illness if consumed. There are techniques to remove mercury in water treatment plants, but they are time consuming and expensive. Martin Pumera at the University of Chemistry and Technology, Prague, in the Czech Republic, and his colleagues are working on a low-cost alternative.
The adoption of artificial intelligence and machine learning technologies has never been more critical. Due to COVID-19, many organizations need to find a new way of working. Ensuring production rates are reliable, if not increased, while limiting the number of personnel - in some cases down to 50%. Many asset heavy industries, such as water, transportation & energy are considered critical infrastructure. Every effort needs to be made to maintain these.
Artificial intelligence technologies can be used to help buildings and spaces track their waste in real-time and engage users by nudging them to correctly sort their waste. According to a study by the World Bank, 98% of the world's waste is sent to landfills, dumped into oceans or being incinerated, even though a high majority of daily consumables are recyclable. This is primarily due to the high level of contaminants found in recyclables, making previously clean material practically unrecyclable and financially unmarketable. In Toronto, for every percentage point decreased in contaminated waste can create up to $1 million in recycling cost savings every year, which can be attributed to the management and sorting costs incurred by the waste hauling and collection companies. Intuitive is a Canadian company which seeks to achieve zero waste through their AI solution, Oscar.
Water management issues are at the center of environmental debates taking place across the globe. Irrational distribution, leakages, contamination, and overuse of groundwater are some of the biggest challenges associated with the water management industry. Today, industry leaders are exploring AI development services for water management systems to mitigate the water crisis using AI and IoT devices. Together, these technologies provide effective mechanisms to monitor water quality, detect leakages, analyze demand, and streamline global water management. This blog post explores and highlights some AI use cases for the diverse water industry.
Rust is a multi-paradigm language with a focus on memory safety. It aims to be systems programming oriented, allowing fine-grained memory management without garbage collection but also without tedious and error-prone manual memory allocations and deallocations. It achieves this goal by means of its ownership system (mostly related to variable aliasing). At any point of a Rust program, the compiler tracks how many variables refer to a given data, and enforces a set of rules which enable automatic memory management, memory safety and data-race free programs. The language also focuses on performance, with powerful compilation optimizations and language constructs that allows writing zero-cost abstraction code.
The "Innovations in Renewable Energy Generation, Desalination, Artificial Intelligence, LEDs, and Vaccines" report has been added to ResearchAndMarkets.com's offering. This edition of the Inside R&D TechVision Opportunity Engine (TOE) features an innovation for enhancing digital imaging in deep learning and an innovation based on using novel receptors for mitigating vector borne diseases. The TOE also provides intelligence on the efficient conversion of carbon dioxide in to value added products and the use of passive solar power for desalination. The TOE also features innovations based on the use of sustainable materials for oil water separation and environment friendly materials that can be used in the construction industry. The TOE additionally provides insights on numerous AI-based solutions for detection of cyber attacks, accurate assessment of diseases, and for the improvement of haptic feedback during telerobotic surgeries.
Multiclass classification problems are those where a label must be predicted, but there are more than two labels that may be predicted. These are challenging predictive modeling problems because a sufficiently representative number of examples of each class is required for a model to learn the problem. It is made challenging when the number of examples in each class is imbalanced, or skewed toward one or a few of the classes with very few examples of other classes. Problems of this type are referred to as imbalanced multiclass classification problems and they require both the careful design of an evaluation metric and test harness and choice of machine learning models. The E.coli protein localization sites dataset is a standard dataset for exploring the challenge of imbalanced multiclass classification.
Due to the growing amount of data from in-situ sensors in wastewater systems, it becomes necessary to automatically identify abnormal behaviours and ensure high data quality. This paper proposes an anomaly detection method based on a deep autoencoder for in-situ wastewater systems monitoring data. The autoencoder architecture is based on 1D Convolutional Neural Network (CNN) layers where the convolutions are performed over the inputs across the temporal axis of the data. Anomaly detection is then performed based on the reconstruction error of the decoding stage. The approach is validated on multivariate time series from in-sewer process monitoring data. We discuss the results and the challenge of labelling anomalies in complex time series. We suggest that our proposed approach can support the domain experts in the identification of anomalies.