Drones could soon be forced to have electronic number plates so they can be tracked by police and security teams as they fly through the skies. The plans are part of new regulations being drawn up by the Government that would allow a drone's speed, location, height, take-off point to be tracked - as well as the operator's location. To collect the information, remote ID technology will be installed in the drones, working in a similar way to the automatic number plate recognition (ANPR) system used on cars, vans and lorries. The move comes amid growing concern that the UK's drone registration scheme is not being enforced properly, as well as fears that drones could be used by terrorists to cause serious harm or economic damage. According to the Civil Aviation Authority, anyone with a drone weighing more than 250g needs to pass a test and get a flyer ID from the authority.

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

An artificial intelligence (AI) driven assessment tool for diagnosing mental health disorders has become the first mental health chatbot to secure a Class IIa UKCA (UK Conformity Assessed) medical device certification. Using machine learning, Limbic Access is designed to support patient self-referral through digital conversations that are incorporated into the psychological therapy pathway. The chatbot can classify common mental health disorders treated by NHS Talking Therapies (IAPTs) with an accuracy of 93%. The certification comes as NHS Improving Access to Psychological Therapies (IAPT) services are experiencing significant capacity challenges in the face of record demand. In 2021-22, 1.24 million referrals accessed IAPT services, compared to 1.02 million in 2020-21, an increase of 21.5%.

Japan's internet watchdog can request instructional posts related to murder, guns and explosives be removed from March, police said Thursday, as authorities respond to former Prime Minister Shinzo Abe's suspected killer using information found online to build weapons. The National Police Agency said by adding to the types of content that can be requested for removal by internet service providers, it aims to prevent crimes before they occur. To strengthen its online surveillance, the agency said it will also consider using artificial intelligence to analyze social media posts. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

The TriFORCE project, funded by the German Federal Ministry of Education and Research (BMBF), led by Prof. Dr. Carlo D'Eramo, at the University of Würzburg (JMU), is seeking 1 Ph.D. student with a strong interest in Reinforcement Learning and its application to robotics problems. Every student with a master degree and a strong passion for Reinforcement Learning, Robotics, and AI, is strongly encouraged to apply!

Late last year, White House Office of Science and Technology Policy released the Blueprint for an AI Bill of Rights, instantly elevating the topic of responsible AI to the top of leadership agendas across executive branch agencies. While the themes of the blueprint are not entirely new--building on prior work including the AI in Government Act of 2020, a December 2020 executive order on trustworthy AI, and the Federal Privacy Council's Fair Information Practice Principles--the report brings new urgency to ongoing agency efforts to leverage data in ways consistent with our democratic ideals. With a stated goal of supporting "the development of policies and practices that protect civil rights and promote democratic values in the building, deployment and governance of automated systems," the blueprint is rooted in five principles: safe and effective systems; algorithmic discrimination protections; data privacy; notice and explanation; and human alternatives, consideration and fallback. The Blueprint also includes notes on applying the principles and a technical companion to support operationalization. Some agencies that are less mature in their data capabilities might consider the blueprint to be of limited relevance.

Cenk Sidar is the cofounder and CEO of Enquire AI, combining AI, data science, and human intelligence to deliver real-time insights. In recent years, tech-celeration has changed the way humans interact in and beyond the workplace. While rapid tech adoption is considered good, it also fuels the emergence of new risks and "unknown unknowns" in an ever-changing macro landscape. As we enter 2023 on the brink of economic strife, something must balance the scales and help business leaders tackle their biggest problems. One answer lies in another tech breakthrough: Artificial intelligence is ready to perform at scale. Its full implementation cannot be predicted at this point, but it promises real-time actionable insights and offers newfound agility in an uncertain world.

The potential is huge, which is why Microsoft is extending its partnership with OpenAI through a "multiyear, multibillion dollar investment"--reportedly a $10 billion outlay that would value the company at near $30 billion. One can imagine a future where everyone has a "generative A.I." assistant that offers quick solutions to business challenges, writes instant computer code based on verbal commands, and conjures up art and video on demand. But the downsides are clear as well. ChatGPT is often wrong, and provides no attribution or sourcing for its information. It has made it instantly easier to saturate the Internet with invasive ads and dubious information, and has opened up a whole new superhighway for cheating in schools.

Signature-based techniques give mathematical insight into the interactions between complex streams of evolving data. These insights can be quite naturally translated into numerical approaches to understanding streamed data, and perhaps because of their mathematical precision, have proved useful in analysing streamed data in situations where the data is irregular, and not stationary, and the dimension of the data and the sample sizes are both moderate. Understanding streamed multi-modal data is exponential: a word in $n$ letters from an alphabet of size $d$ can be any one of $d^n$ messages. Signatures remove the exponential amount of noise that arises from sampling irregularity, but an exponential amount of information still remain. This survey aims to stay in the domain where that exponential scaling can be managed directly. Scalability issues are an important challenge in many problems but would require another survey article and further ideas. This survey describes a range of contexts where the data sets are small enough to remove the possibility of massive machine learning, and the existence of small sets of context free and principled features can be used effectively. The mathematical nature of the tools can make their use intimidating to non-mathematicians. The examples presented in this article are intended to bridge this communication gap and provide tractable working examples drawn from the machine learning context. Notebooks are available online for several of these examples. This survey builds on the earlier paper of Ilya Chevryev and Andrey Kormilitzin which had broadly similar aims at an earlier point in the development of this machinery. This article illustrates how the theoretical insights offered by signatures are simply realised in the analysis of application data in a way that is largely agnostic to the data type.

We present a model inversion algorithm, CKLEMAP, for data assimilation and parameter estimation in partial differential equation models of physical systems with spatially heterogeneous parameter fields. These fields are approximated using low-dimensional conditional Karhunen-Lo\'{e}ve expansions, which are constructed using Gaussian process regression models of these fields trained on the parameters' measurements. We then assimilate measurements of the state of the system and compute the maximum a posteriori estimate of the CKLE coefficients by solving a nonlinear least-squares problem. When solving this optimization problem, we efficiently compute the Jacobian of the vector objective by exploiting the sparsity structure of the linear system of equations associated with the forward solution of the physics problem. The CKLEMAP method provides better scalability compared to the standard MAP method. In the MAP method, the number of unknowns to be estimated is equal to the number of elements in the numerical forward model. On the other hand, in CKLEMAP, the number of unknowns (CKLE coefficients) is controlled by the smoothness of the parameter field and the number of measurements, and is in general much smaller than the number of discretization nodes, which leads to a significant reduction of computational cost with respect to the standard MAP method. To show its advantage in scalability, we apply CKLEMAP to estimate the transmissivity field in a two-dimensional steady-state subsurface flow model of the Hanford Site by assimilating synthetic measurements of transmissivity and hydraulic head. We find that the execution time of CKLEMAP scales nearly linearly as $N^{1.33}$, where $N$ is the number of discretization nodes, while the execution time of standard MAP scales as $N^{2.91}$. The CKLEMAP method improved execution time without sacrificing accuracy when compared to the standard MAP.