HMRC has confirmed it uses artificial intelligence (AI) to monitor social media posts as part of criminal investigations into suspected tax cheats. It said the tech would not replace "human decision-making" and was subject to legal oversight. "Greater use of AI will enable our staff to spend less time on admin and more time helping taxpayers, as well as better target fraud and evasion to bring in more money for public services," it said in a statement. However, experts warn there are risks with using AI in this way.

On the southern tip of Malaysia lies the state of Johor, renowned for its beaches and mountainous jungle. But Johor has a new boom industry: data centers to power generative AI, with Microsoft committing more than 2 billion on just such a data center. For the tech giants, electricity has become the new oil. A state-of-the-art AI data center might need 90 MW, enough to power tens of thousands of American homes. With AI applications proliferating, from chatbots to AI agents, needs are growing.

Multitask learning for improved scour detection: A dynamic wave tank study Simon M. Brealy, Aidan J. Hughes, Tina A. Dardeno, Lawrence A. Bull, Robin S. Mills, Nikolaos Dervilis, Keith Worden Bayesian hierarchical models help reduce uncertainty of foundation model parameters in populations of wind-turbines Reduced foundation parameter uncertainty aids detection of anomalies in dynamic behaviour during operation Future design of turbines may also be improved through reducing the likelihood and severity of fatigue damage Abstract Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of nominally-identical wind-turbine structures. However, benign variations exist among members, such as geometry, sea-bed conditions and temperature differences. These factors could influence structural properties and therefore the dynamic response, making it more difficult to detect structural problems via traditional SHM techniques. This paper explores the use of a Bayesian hierarchical model as a means of multitask learning, to infer foundation stiffness distribution parameters at both population and local levels. To do this, observations of natural frequency from populations of structures were first generated from both numerical and experimental models. These observations were then used in a partially-pooled Bayesian hierarchical model in tandem with surrogate FE models of the structures to infer foundation stiffness parameters.

Scour around bridge piers is a critical challenge for infrastructures around the world. In the absence of analytical models and due to the complexity of the scour process, it is difficult for current empirical methods to achieve accurate predictions. In this paper, we exploit the power of deep learning algorithms to forecast the scour depth variations around bridge piers based on historical sensor monitoring data, including riverbed elevation, flow elevation, and flow velocity. We investigated the performance of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models for real-time scour forecasting using data collected from bridges in Alaska and Oregon from 2006 to 2021. The LSTM models achieved mean absolute error (MAE) ranging from 0.1m to 0.5m for predicting bed level variations a week in advance, showing a reasonable performance. The Fully Convolutional Network (FCN) variant of CNN outperformed other CNN configurations, showing a comparable performance to LSTMs with significantly lower computational costs. We explored various innovative random-search heuristics for hyperparameter tuning and model optimisation which resulted in reduced computational cost compared to grid-search method. The impact of different combinations of sensor features on scour prediction showed the significance of the historical time series of scour for predicting upcoming events. Overall, this study provides a greater understanding of the potential of Deep Learning algorithms for real-time scour prediction and early warning for bridges with distinct geology, geomorphology and flow characteristics.

Artificial intelligence and automation are seeping into our daily working lives -- and female office workers are among those whose jobs are being taken over by machines. That's according to new research provided to ABC News by an Australian teaching organisation that's urging people to upskill so they don't find themselves out of work. Pearson's research looks at roles that are likely to be automated as technology advances. Historically, much of the conversation when it comes to automation has been about robots taking over factory jobs, or even replacing retail assistants in the form of self-service check-outs at supermarkets. Pearson's data also shows the less obvious pictures of automation encroaching into office environments, including for medical receptionists, accountants and personal assistants.

The maximum error in scour trough and filling peak forecasts are provided in Table 3 and graphically shown in Figure 22. The maximum error based on the mean of predictions varies between 0.5m to 0.7m for scour troughs and 0.4m to 1.7m for filling peaks. The lower bound (LB) and upper bound (UB) errors show a reasonable degree of variability in the LSTM predictions, varying between 0.2m to 0.9m for scour, and 0m to 1.4m for filling. Impact of Flow Velocity (Discharge) In order to explore whether velocity is a critical feature in presence of stage timeseries, we incorporated the discharge measurements (discharge), obtained from the USGS website, into the LSTM models for bridge 742 as an input feature and compared the performance among three different feature combinations: ssd:[sonar, stage, discharge], sd:[sonar, discharge], and ss:[sonar, stage]. Discharge is computed based on gage-height records (flow velocity) multiplied the river cross-section area. Gage-height records are obtained by systematic observation of a non-recording gage, or with automatic water level sensors relayed by remote gagging stations (Sauer and Turnipseed 2010). Figure 23 provides histograms of the discharge time-series for bridge 742 and its cross-correlation with sonar and stage. Stage and discharge show a large positive correlation as observed both in Figure 23 and Figure 24.

Artificial Intelligence offers a wide variety of services to streamline jobs such as mass production and data gathering. The main purpose of an AI in industry is to make certain tasks automatically so there’s no need to hire skilled personnel.

Alzheimer's disease is becoming increasingly prevalent as life expectancies lengthen. But the complexity of the condition makes it hard to find effective treatments. One way to expedite the search that's yielded promising results is using AI to find existing drugs that could be repurposed to combat the disorder. Harvard researchers recently used the approach to identify 80 candidate medications that merit further investigation. They discovered the contenders through a framework they call DRIAD (Drug Repurposing In Alzheimer's Disease).

A San Francisco-based technology company called Chooch AI is trying to narrow that gap with the help of artificial intelligence, reducing the time between a fire's eruption and the moment it's spotted by people. The company, which is working with state agencies, researchers and technologists, is working to develop an AI tool that would scour hyper-detailed imagery from satellites for evidence of wildfires largely invisible to the naked eye. If successfully refined, experts believe, the tool could lead to earlier wildfire detection that would almost certainly save more people and property from destruction.

NASA says it will put a robotic rover on the moon that can aid the agency in its search for lunar water. The four-wheeled vehicle, which NASA has dubbed the Volatiles Investigating Polar Exploration Rover, or VIPER, will be the size of a golf-cart and use various science instruments to probe the moon's surface for evidence of water and ice. VIPER is set to be delivered to the moon's surface by December 2022 and once there it will collect 100 days worth of data designed to map potential water sources. NASA's VIPER rover (rendering above) will explore the moon for water and help to guide an ongoing plan to return humans to the lunar surface A mobility'testbed' (pictured above) is was created to evaluate the rover's mobility system. It's toolkit for detecting water will include a drill able to bore beneath the surface and a spectrometer than can detect moisture.