As the United Nations General Assembly gathered this week in New York, the U.N. Secretary-General's envoy on technology, Amandeep Gill, hosted an event titled Governing AI for Humanity, where participants discussed the risks that AI might pose and the challenges of achieving international cooperation on artificial intelligence. Secretary-General António Guterres and Gill have said they believe that a new U.N. agency will be required to help the world cooperate in managing this powerful technology. But the issues that the new entity would seek to address and its structure are yet to be determined, and some observers say that ambitious plans for global cooperation like this rarely get the required support of powerful nations. Gill has led efforts to make advanced forms of technology safer before. He was chair of the Group of Governmental Experts of the Convention on Certain Conventional Weapons when the Campaign to Stop Killer Robots, which sought to compel governments to outlaw the development of lethal autonomous weapons systems, failed to gain traction with global superpowers including the U.S. and Russia.

Google's mission statement is to make the'world's information universally accessible' - but that hasn't stopped it from self-censoring to avoid offending Russia. A new study has shown the search giant's artificial intelligence chatbot, Bard, mostly refuses to answer critical questions about Russian President Vladimir Putin. In fact, it won't answer 90 percent of queries regardless of how offensive or inoffensive they are. One of the two researchers in Switzerland who did the test believe Google is being'pushed' by the Kremlin to censor anything critical about the Russian regime. Google's artificial intelligence chatbot, Bard, mostly refuses to answer critical questions about Russian President Vladimir Putin Mykola Makhortykh, a post-doctoral lecturer at the University of Bern and one of the researchers, told DailyMail.com: 'My personal opinion is that Google might have been pushed by the Russian government to censor some of the results which were critical to the Kremlin similar to how it was done by Yandex.'

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Pennsylvania state government will prepare to use artificial intelligence in its operations, Democratic Gov. Josh Shapiro said Wednesday, as states are increasingly trying to gauge the impact of AI and how to regulate it. Shapiro, speaking at a news conference at Carnegie Mellon University in Pittsburgh, said his administration is convening an AI governing board, publishing principles on the use of AI and developing training programs for state employees. Pennsylvanians will expect state government to understand AI, adapt to AI and ensure that it is being used safely in the private sector, Shapiro said.

A report published this week by the U.K.'s Competition & Markets Authority (CMA) has raised concerns about the potential ways the artificial intelligence industry could become monopolized or harm consumers in future, but stressed that it is too soon to tell whether these scenarios would materialize. The issues raised by the report highlight the difficulties policymakers face in governing AI, a source of both huge potential commercial value and many risks. Rishi Sunak, the British Prime Minister, is pushing for the U.K. to occupy a central role in international AI policy discussions, with a particular focus on risks from advanced AI systems. If the U.K. competition watchdog decides to start taking action against AI developers, tech companies around the world could be affected. The report, published on Monday, focuses on foundation models, which the CMA defines as "a type of AI technology that are trained on vast amounts of data that can be adapted to a wide range of tasks and operations." Examples include text-generating AI models, such as GPT-3.5, the model that powers OpenAI's ChatGPT, as well as image-generating AI models, such as Stable Diffusion.

Russian aerial defence systems destroyed a wave of 19 Ukrainian drones that were launched overnight in attacks against targets in the Russia-annexed Crimean peninsula, the surrounding Black Sea and other regions of Russia. The Russian defence ministry said early on Thursday that it had "thwarted" the attacks by Ukraine's aircraft-type unmanned aerial vehicles (UAVs). "In the night from 20th to 21st September, an attempt by the Kyiv regime to commit a terrorist attack with lethal drones on sites in the Russian Federation was intercepted," the defence ministry said on the Telegram messaging app. "Air defence systems destroyed 19 Ukrainian UAVs over the Black Sea and the territory of the Republic of Crimea, and one each over the territories of Kursk, Belgorod and Oryol regions," the ministry said. The Belgorod and Kursk regions of Russia border eastern Ukraine, while Oryol is closer to the capital, Moscow.

During a five-day tour of India earlier this month, Nvidia CEO Jensen Huang visited four cities, dined with tech executives and researchers, took numerous selfies, and sat for a one-on-one conversation with Prime Minister Narendra Modi about the AI sector. Huang's India itinerary was so crammed that he confessed to surviving entire work days on spicy masala omelets and cold coffees. Huang may have been treated like a head of state, but the trip's purpose was all business. For Nvidia, whose graphics processors are vital to the development of artificial intelligence systems, the South Asian nation of 1.4 billion people presents a rare opportunity. As the U.S. increasingly clamps down on exports of high-end chips to China and the world seeks an alternative electronics manufacturing base, India could shape up to be a source of AI talent, a site for chip production and a market for Nvidia's products.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Northeastern University in Boston will be given $17.5 million by the Centers for Disease Control and Prevention (CDC) to lead an innovation center focused on infectious disease detection and preparation, the university announced. The Center for Advanced Epidemic Analytics and Predictive Modeling Technology, or EPISTORM, will "help detect and prepare the United States for the next outbreak of infectious disease, especially in rural areas," according to the university's Northeastern Global News (NGN). The funds will be used to coordinate the work of various consortium members across the U.S. to prepare local communities for outbreaks, including RSV and the seasonal flu.

The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use datasets that are temporally subsampled relative to the time steps required for the numerical integration of differential equations. Here, we investigate how this often overlooked processing step affects the quality of an emulator's predictions. We implement two ML architectures from a class of methods called reservoir computing: (1) a form of Nonlinear Vector Autoregression (NVAR), and (2) an Echo State Network (ESN). Despite their simplicity, it is well documented that these architectures excel at predicting low dimensional chaotic dynamics. We are therefore motivated to test these architectures in an idealized setting of predicting high dimensional geophysical turbulence as represented by Surface Quasi-Geostrophic dynamics. In all cases, subsampling the training data consistently leads to an increased bias at small spatial scales that resembles numerical diffusion. Interestingly, the NVAR architecture becomes unstable when the temporal resolution is increased, indicating that the polynomial based interactions are insufficient at capturing the detailed nonlinearities of the turbulent flow. The ESN architecture is found to be more robust, suggesting a benefit to the more expensive but more general structure. Spectral errors are reduced by including a penalty on the kinetic energy density spectrum during training, although the subsampling related errors persist. Future work is warranted to understand how the temporal resolution of training data affects other ML architectures.

Synthetic data is seen as the most promising solution to share individual-level data while preserving privacy. Shadow modeling-based Membership Inference Attacks (MIAs) have become the standard approach to evaluate the privacy risk of synthetic data. While very effective, they require a large number of datasets to be created and models trained to evaluate the risk posed by a single record. The privacy risk of a dataset is thus currently evaluated by running MIAs on a handful of records selected using ad-hoc methods. We here propose what is, to the best of our knowledge, the first principled vulnerable record identification technique for synthetic data publishing, leveraging the distance to a record's closest neighbors. We show our method to strongly outperform previous ad-hoc methods across datasets and generators. We also show evidence of our method to be robust to the choice of MIA and to specific choice of parameters. Finally, we show it to accurately identify vulnerable records when synthetic data generators are made differentially private. The choice of vulnerable records is as important as more accurate MIAs when evaluating the privacy of synthetic data releases, including from a legal perspective. We here propose a simple yet highly effective method to do so. We hope our method will enable practitioners to better estimate the risk posed by synthetic data publishing and researchers to fairly compare ever improving MIAs on synthetic data.

Synthetic data is emerging as one of the most promising solutions to share individual-level data while safeguarding privacy. While membership inference attacks (MIAs), based on shadow modeling, have become the standard to evaluate the privacy of synthetic data, they currently assume the attacker to have access to an auxiliary dataset sampled from a similar distribution as the training dataset. This is often seen as a very strong assumption in practice, especially as the proposed main use cases for synthetic tabular data (e.g. medical data, financial transactions) are very specific and don't have any reference datasets directly available. We here show how this assumption can be removed, allowing for MIAs to be performed using only the synthetic data. Specifically, we developed three different scenarios: (S1) Black-box access to the generator, (S2) only access to the released synthetic dataset and (S3) a theoretical setup as upper bound for the attack performance using only synthetic data. Our results show that MIAs are still successful, across two real-world datasets and two synthetic data generators. These results show how the strong hypothesis made when auditing synthetic data releases - access to an auxiliary dataset - can be relaxed, making the attacks more realistic in practice.