AML Trends in 2022: What Y ou Need to Know .

The UN estimates 2-5% of global GDP or $0.8 - $2.0 trillion dollars are laundered

The achievement of the 2030 Sustainable Development Goals (SDGs) is dependent upon strategic resource distribution. We propose a causal discovery framework using Panel Vector Autoregression, along with both country-specific fixed effects and PCMCI+ conditional independence testing on 168 countries (2000-2025) to develop the first complete causal architecture of SDG dependencies. Utilizing 8 strategically chosen SDGs, we identify a distributed causal network (i.e., no single 'hub' SDG), with 10 statistically significant Granger-causal relationships identified as 11 unique direct effects. Education to Inequality is identified as the most statistically significant direct relationship (r = -0.599; p < 0.05), while effect magnitude significantly varies depending on income levels (e.g., high-income: r = -0.65; lower-middle-income: r = -0.06; non-significant). We also reject the idea that there exists a single 'keystone' SDG. Additionally, we offer a proposed tiered priority framework for the SDGs namely, identifying upstream drivers (Education, Growth), enabling goals (Institutions, Energy), and downstream outcomes (Poverty, Health). Therefore, we conclude that effective SDG acceleration can be accomplished through coordinated multi-dimensional intervention(s), and that single-goal sequential strategies are insufficient.

Elon Musk has been promoting Grok's popularity as if it were a piece of productivity software. Elon Musk has been promoting Grok's popularity as if it were a piece of productivity software. Today, we discuss Elon Musk's rosy depiction of Grok's image generation controversy; the seven-figure panic among Silicon Valley billionaires over a proposed wealth tax in California, though with one notable exception; and how AI and robotics have revitalized the Consumer Electronics Showcase. The firestorm over the Grok AI tool has been raging for more than a week now, and it shows no signs of dying down. Last week, I wrote about the rising backlash against Elon Musk's Grok AI tool, which in recent weeks has allowed users to generate thousands of sexualized images of women.

Tesla loses place as world's top electric vehicle seller to China's BYD Tesla has lost its place as the top global seller of electric vehicles to Chinese company BYD, capping a year defined by outrage over CEO Elon Musk's political manoeuvring and the end of United States tax breaks for customers. The company revealed on Friday that it had sold 1.64 million vehicles in 2025, compared with BYD's 2.26 million vehicles. The sales represented a 9 percent decline for Tesla from a year earlier. However, the market has become increasingly crowded with competitors, with China's electric vehicle market bounding ahead. Musk's embrace of US President Donald Trump in 2024 and subsequent spearheading of a controversial "government efficiency" panel (DOGE) behind widespread layoffs of federal workers has also proved polarising.

We derive the first closed-form condition under which artificial intelligence (AI) capital profits could sustainably finance a universal basic income (UBI) without relying on new taxation or the creation of new jobs. In a Solow-Zeira task-automation economy with a CES aggregator $σ< 1$, we introduce an AI capability parameter that scales the productivity of automatable tasks and obtain a tractable expression for the AI capability threshold -- the minimum productivity of AI relative to pre-AI automation required for a balanced transfer. Using current U.S. economic parameters, we find that even in the conservative scenario where no new tasks or jobs emerge, AI systems would only need to reach only 5-7 times today's automation productivity to fund an 11%-of-GDP UBI. Our analysis also reveals some specific policy levers: raising public revenue share (e.g. profit taxation) of AI capital from the current 15% to about 33% halves the required AI capability threshold to attain UBI to 3 times existing automation productivity, but gains diminish beyond 50% public revenue share, especially if regulatory costs increase. Market structure also strongly affects outcomes: monopolistic or concentrated oligopolistic markets reduce the threshold by increasing economic rents, whereas heightened competition significantly raises it. These results therefore offer a rigorous benchmark for assessing when advancing AI capabilities might sustainably finance social transfers in an increasingly automated economy.

Large language models demonstrate powerful capabilities across various natural language processing tasks, yet they also harbor safety vulnerabilities. To enhance LLM safety, various jailbreak defense methods have been proposed to guard against harmful outputs. However, improvements in model safety often come at the cost of severe over-refusal, failing to strike a good balance between safety and usability. In this paper, we first analyze the causes of over-refusal from a representation perspective, revealing that over-refusal samples reside at the boundary between benign and malicious samples. Based on this, we propose MOSR, designed to mitigate over-refusal by intervening the safety representation of LLMs. MOSR incorporates two novel components: (1) Overlap-Aware Loss Weighting, which determines the erasure weight for malicious samples by quantifying their similarity to pseudo-malicious samples in the representation space, and (2) Context-Aware Augmentation, which supplements the necessary context for rejection decisions by adding harmful prefixes before rejection responses. Experiments demonstrate that our method outperforms existing approaches in mitigating over-refusal while largely maintaining safety. Overall, we advocate that future defense methods should strike a better balance between safety and over-refusal.

Former U.S. Treasury Secretary Larry Summers says he will step back from all public commitments, adding the move is to allow him to rebuild trust and repair relationships with the people closest to me. Former U.S. Treasury Secretary Larry Summers is stepping down from a teaching post at Harvard University and as a director of one of its business and government schools, a spokesperson said on Wednesday, after Congress released documents showing Summers shared close ties with the late convicted sex offender Jeffrey Epstein. A spokesperson for Summers, Steven Goldberg, said Summers' co-teachers would complete the semester for three ongoing courses. Mr. Summers has decided it's in the best interest of the center for him to go on leave from his role as director as Harvard undertakes its review, he said. Summers, also a former president of Harvard University, is a director of the Mossavar-Rahmani Center for Business and Government at the Harvard Kennedy School. Summers has been under fire since the U.S. House Oversight Committee released documents detailing an ongoing personal correspondence between Summers and Epstein, who died by suicide in a Manhattan prison in 2019 as he faced sex-trafficking charges.