The White House reaffirmed its mission to increase AI collaboration between the U.S. and Singapore last week. But during those talks, another country was on everyone's minds: China. When President Joe Biden hosted Singapore Prime Minister Lee Hsien Loong on March 29, he acknowledged the bilateral strategic partnership between the two nations -- and the 5,400 U.S. companies with locations in Singapore. On the sidelines, U.S. Commerce Department representatives met with Singapore officials to expand the countries' economic efforts related to trustworthy AI, data privacy, digital trade standards and advanced manufacturing. Those efforts build on a previous Memorandum of Understanding, called the U.S.-Singapore Partnership for Growth and Innovation, the two countries signed in October.

Necessity is the mother of invention, and Rwanda's government seems to understand this more than most with the launch of the Centre of the Fourth Industrial Revolution (C4IR). "With the advent of the Fourth Industrial Revolution and the rapid innovations witnessed during the Covid-19 pandemic, there is an increased urgency to develop digital and technological capacities to build more resilient systems for a healthier society and more sustainable economy," said Rwandan Minister of Information Communication Technology and Innovation Paula Ingabire. Ingabire made the comment in a media statement posted on the World Economic Forum's (WEF) website. Rwanda has launched its C4IR, saying it will "work with stakeholders around the world to design and pilot new approaches to technology governance that foster innovation in an inclusive and responsible manner". Some of the projects that the C4IR is already working on are the country's artificial intelligence (AI) policy and laws on the protection of personal data and privacy.

For the past 15 years, police forces searching for criminals in Europe have been able to share fingerprints, DNA data, and details of vehicle owners with each other. If officials in France suspect someone they are looking for is in Spain, they can ask Spanish authorities to check fingerprints against their database. Now European lawmakers are set to include millions of photos of people's faces in this system--and allow facial recognition to be used on an unprecedented scale. The expansion of facial recognition across Europe is included in wider plans to "modernize" policing across the continent, and it comes under the Prüm II data-sharing proposals. The details were first announced in December, but criticism from European data regulators has gotten louder in recent weeks, as the full impact of the plans have been understood.

A research study being led by Royal Papworth Hospital and the University of Cambridge is hoping to use artificial intelligence to help diagnose heart valve diseases earlier. Valvular heart disease (VHD) affects nearly two million people in the UK with this number expected to double by 2040. About half of those affected by VHD are unaware of their condition, because symptoms often do not develop until the disease has become severe. Cardiovascular Acoustics and an Intelligent Stethoscope (CAIS) is a clinical study aimed at creating a first-of-its-kind screening tool which could be used to diagnose valve disease before symptoms emerge. Almost 1,200 patients with suspected heart valve disease or congenital heart disease have so far signed up to the study across five NHS hospital sites.

The April 4, 2022 report from the U.N. Intergovernmental Panel on Climate Change makes it clear that it is "now or never" for the planet. We are "firmly on track toward an unlivable world," U.N. Secretary-General Antonio Guterres said in releasing the report. There's every chance that global temperatures will soar by 3 degrees Celsius, twice as much as the agreed-upon 1.5 C limit. Unless we take drastic steps and cut down emissions by 43 percent within this decade, the full force of this existential threat will be upon us. In this context, it is interesting that some researchers have taken artificial intelligence--a technology often considered an existential threat in its own right– and tried to turn it into a vehicle for climate action.

An artificial intelligence tool that reads chest X-rays without oversight from a radiologist got regulatory clearance in the European Union last week -- a first for a fully autonomous medical imaging AI, the company, called Oxipit, said in a statement. It's a big milestone for AI and likely to be contentious, as radiologists have spent the last few years pushing back on efforts to fully automate parts of their job. The tool, called ChestLink, scans chest X-rays and automatically sends patient reports on those that it sees as totally healthy, with no abnormalities. Any images that the tool flags as having a potential problem are sent to a radiologist for review. Most X-rays in primary care don't have any problems, so automating the process for those scans could cut down on radiologists' workloads, the Oxipit said in informational materials.

SafeGuard, a cloud platform designed to protect assets from cybersecurity threats and risk factors, today announced it has raised $45 million in a mix of equity and debt. SafeGuard, which was founded in 2014, develops products that identify risks in communication channels such as social media, chat apps, and collaboration platforms -- like Slack, LinkedIn, and WhatsApp. SafeGuard also helps companies take action and claims it can shield high-profile or targeted individuals from account takeovers, spearphishing, malicious content, threats of violence, and misinformation, as well as bad actor connections. To this end, SafeGuard leverages an AI-powered engine called Threat Cortex that detects and spotlights risks across different attack surfaces. On the compliance side of the equation, SafeGuard offers a tool that taps AI to alert employees, customers, and partners if their digital communications are at risk of violating regulations like the Financial Industry Regulatory Authority and Financial Conduct Authority.

Amid rising geopolitical tensions and intensifying polarisation, building a global consensus around the use of artificial intelligence (AI) is likely to be tough. Yet experts at a recent Science Business Data Rules workshop were cautiously optimistic that the necessary political will exists. If they fail to achieve some form of coordination, all of the world's major powers will suffer, according to MEP Brando Benifei, one of the European Parliament's rapporteurs for the EU's AI Act, which could arrive on the statute books next year. "I think it would be a problem, not just for Europe, but for all the players involved because artificial intelligence will be a very pervasive technology," he said. "Having two different contexts of application, standards and regulation will make it complicated to deal with all the activities that now interconnect the world. So I think we need really to put an effort into avoiding this situation."

Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment. In this paper, we ask the question: Can we improve QA systems further \emph{post-}deployment based on user interactions? We focus on two kinds of improvements: 1) improving the QA system's performance itself, and 2) providing the model with the ability to explain the correctness or incorrectness of an answer. We collect a retrieval-based QA dataset, FeedbackQA, which contains interactive feedback from users. We collect this dataset by deploying a base QA system to crowdworkers who then engage with the system and provide feedback on the quality of its answers. The feedback contains both structured ratings and unstructured natural language explanations. We train a neural model with this feedback data that can generate explanations and re-score answer candidates. We show that feedback data not only improves the accuracy of the deployed QA system but also other stronger non-deployed systems. The generated explanations also help users make informed decisions about the correctness of answers. Project page: https://mcgill-nlp.github.io/feedbackqa/

In this work, a Gaussian process regression(GPR) model incorporated with given physical information in partial differential equations(PDEs) is developed: physics-assisted Gaussian processes(PAGP). The targets of this model can be divided into two types of problem: finding solutions or discovering unknown coefficients of given PDEs with initial and boundary conditions. We introduce three different models: continuous time, discrete time and hybrid models. The given physical information is integrated into Gaussian process model through our designed GP loss functions. Three types of loss function are provided in this paper based on two different approaches to train the standard GP model. The first part of the paper introduces the continuous time model which treats temporal domain the same as spatial domain. The unknown coefficients in given PDEs can be jointly learned with GP hyper-parameters by minimizing the designed loss function. In the discrete time models, we first choose a time discretization scheme to discretize the temporal domain. Then the PAGP model is applied at each time step together with the scheme to approximate PDE solutions at given test points of final time. To discover unknown coefficients in this setting, observations at two specific time are needed and a mixed mean square error function is constructed to obtain the optimal coefficients. In the last part, a novel hybrid model combining the continuous and discrete time models is presented. It merges the flexibility of continuous time model and the accuracy of the discrete time model. The performance of choosing different models with different GP loss functions is also discussed. The effectiveness of the proposed PAGP methods is illustrated in our numerical section.