Meta's drive to integrate artificial intelligence into its products yielded strong financial results for the second quarter in a row. The company plans to spend even more on AI in the coming months, though, and its share price slumped more than 12% as the company reported earnings Wednesday. A weak sales forecast and higher spending guidance rattled investors. Revenue at the world's largest social media business increased 27% to 36.46bn during the first quarter in contrast to analyst expectations of 36.16bn. Earnings per share more than doubled to 4.71, surpassing expectations on Wall Street of 4.32.

Two top artificial intelligence experts--one an optimist and the other more alarmist about the technology's future--engaged in a spirited debate at the TIME100 Summit on Wednesday. Both Yoshua Bengio, founder and scientific director of Mila Quebec AI Institute, a scientific hub, and Eric Schmidt, chairman of the Special Competitive Studies Project and former Google CEO, agree that A.I. is poised to transform modern society. But as moderator Stephanie Ruhle, an MSNBC host, put it: "Yoshua believes that the risk of AI potentially putting us into extinction should be considered a global risk, like we look at pandemics and nuclear war. Eric is super, super excited about AI." Bengio's main concern with AI is the difficulty in ensuring that AI systems are used for their intended purpose and not something harmful. "We absolutely need to clear the fog; right now, scientists really have no idea" how to get AI to behave according to the norms, law and values of society.

The study used AI to predict people's political orientation based on images of expressionless faces. BLANK SPACE: Researchers are warning that facial recognition technologies are "more threatening than previously thought" and pose "serious challenges to privacy" after a study found that artificial intelligence can be successful in predicting a person's political orientation based on images of expressionless faces. Former President Donald Trump and President Biden are seen in a split image. DISASTER RESPONSE: An artificial intelligence venture backed by Google is partnering with the military to use AI in responding to natural disasters. 'NATURAL PROGRESSION': A chemotherapy alternative called immunotherapy is showing promise in treating cancer -- and a new artificial intelligence tool could help ensure that patients have the best possible experience.

The Senate approved a measure that will require ByteDance to sell TikTok or face a ban, in a vote of 79 to 18. The Protecting Americans from Foreign Adversary Controlled Applications Act next goes to President Biden. The president has already said he'll sign the bill into law. TikTok has faced the ire of US politicians for a few years now, but this bill has picked up support across both political parties. It sailed through the House of Representatives before being approved (bundled with a package for foreign aid) by the Senate on Tuesday.

The world is facing the collapse of the 1948 international order established in the wake of World War II, amid the brutal wars in Gaza and Ukraine, while authoritarian policies continue to spread, Amnesty International has warned. The report accused the world's most powerful governments, including China, Russia and the United States, of leading the global disregard for international rules and values enshrined in the Universal Declaration of Human Rights of December 1948. The war in Gaza, which began on October 7, was a "descent into hell", Secretary-General Agnes Callamard wrote in her preface to the report, where "the'never again' moral and legal lessons [of 1948] were torn into a million pieces". Noting that Hamas had committed "horrific crimes" in its assault on communities in southern Israel on October 7, Callamard said Israel's "campaign of retaliation" had become a "campaign of collective punishment". Amnesty said while Israel continued to disregard international human rights law, the US, its foremost ally, and other countries including the United Kingdom and Germany were guilty of "grotesque double standards" given their willingness to back Israeli and US authorities over Gaza while condemning war crimes by Russia in Ukraine.

Oleksandr Pivnenko, the commander of Ukraine's National Guard, said Russia was preparing "unpleasant surprises" and could try to advance on the northeastern city of Kharkiv, the second-biggest in the country, in the coming months. Pivnenko said Kyiv's forces were prepared to thwart any assault. Russia's Defence Minister Sergei Shoigu said Moscow would "increase the intensity of attacks on logistics centres and storage bases for Western weapons" in Ukraine, as he claimed advances on the front line in Pervomaiske, Bohdanivka and Novomykhailivka this month. At least nine people were injured after a Russian drone attack on the Black Sea port of Odesa, which damaged more than a dozen residential apartments. Four children, including two babies, were among the injured and were taken to hospital.

We develop time-series machine learning (ML) methods for closure modeling of the Unsteady Reynolds Averaged Navier Stokes (URANS) equations applied to stably stratified turbulence (SST). SST is strongly affected by fine balances between forces and becomes more anisotropic in time for decaying cases. Moreover, there is a limited understanding of the physical phenomena described by some of the terms in the URANS equations. Rather than attempting to model each term separately, it is attractive to explore the capability of machine learning to model groups of terms, i.e., to directly model the force balances. We consider decaying SST which are homogeneous and stably stratified by a uniform density gradient, enabling dimensionality reduction. We consider two time-series ML models: Long Short-Term Memory (LSTM) and Neural Ordinary Differential Equation (NODE). Both models perform accurately and are numerically stable in a posteriori tests. Furthermore, we explore the data requirements of the ML models by extracting physically relevant timescales of the complex system. We find that the ratio of the timescales of the minimum information required by the ML models to accurately capture the dynamics of the SST corresponds to the Reynolds number of the flow. The current framework provides the backbone to explore the capability of such models to capture the dynamics of higher-dimensional complex SST flows.

While small businesses are increasingly turning to online crowdfunding platforms for essential funding, over 40% of these campaigns may fail to raise any money, especially those from low socio-economic areas. We utilize the latest advancements in AI technology to identify crucial factors that influence the success of crowdfunding campaigns and to improve their fundraising outcomes by strategically optimizing these factors. Our best-performing machine learning model accurately predicts the fundraising outcomes of 81.0% of campaigns, primarily based on their textual descriptions. Interpreting the machine learning model allows us to provide actionable suggestions on improving the textual description before launching a campaign. We demonstrate that by augmenting just three aspects of the narrative using a large language model, a campaign becomes more preferable to 83% human evaluators, and its likelihood of securing financial support increases by 11.9%. Our research uncovers the effective strategies for crafting descriptions for small business fundraising campaigns and opens up a new realm in integrating large language models into crowdfunding methodologies.

As climate change issues become more pressing, their impact in Africa calls for urgent, innovative solutions tailored to the continent's unique challenges. While Artificial Intelligence (AI) emerges as a critical and valuable tool for climate change adaptation and mitigation, its effectiveness and potential are contingent upon overcoming significant challenges such as data scarcity, infrastructure gaps, and limited local AI development. This position paper explores the role of AI in climate change adaptation and mitigation in Africa. It advocates for a collaborative approach to build capacity, develop open-source data repositories, and create context-aware, robust AI-driven climate solutions that are culturally and contextually relevant.

This paper addresses the challenges associated with hyperspectral image (HSI) reconstruction from miniaturized satellites, which often suffer from stripe effects and are computationally resource-limited. We propose a Real-Time Compressed Sensing (RTCS) network designed to be lightweight and require only relatively few training samples for efficient and robust HSI reconstruction in the presence of the stripe effect and under noisy transmission conditions. The RTCS network features a simplified architecture that reduces the required training samples and allows for easy implementation on integer-8-based encoders, facilitating rapid compressed sensing for stripe-like HSI, which exactly matches the moderate design of miniaturized satellites on push broom scanning mechanism. This contrasts optimization-based models that demand high-precision floating-point operations, making them difficult to deploy on edge devices. Our encoder employs an integer-8-compatible linear projection for stripe-like HSI data transmission, ensuring real-time compressed sensing. Furthermore, based on the novel two-streamed architecture, an efficient HSI restoration decoder is proposed for the receiver side, allowing for edge-device reconstruction without needing a sophisticated central server. This is particularly crucial as an increasing number of miniaturized satellites necessitates significant computing resources on the ground station. Extensive experiments validate the superior performance of our approach, offering new and vital capabilities for existing miniaturized satellite systems.