'I think this image should be just as important as the image of the gate in Auschwitz,' says the US-based German historian Jürgen Matthäus. 'I think this image should be just as important as the image of the gate in Auschwitz,' says the US-based German historian Jürgen Matthäus. Thu 2 Oct 2025 03.23 EDTLast modified on Thu 2 Oct 2025 08.22 EDT It is one of the most chilling images of the Holocaust: a bespectacled Nazi soldier trains a pistol at the head of a resigned man kneeling in a suit before a pit full of corpses. The picture taken in today's Ukraine was long known, mistakenly, as The Last Jew in Vinnitsa, and was for decades shrouded in mystery. The US-based German historian Jürgen Matthäus has for years painstakingly assembled the puzzle pieces and, with the help of artificial intelligence, is confident he has identified the killer.

Spectral inference provides fast algorithms and provable optimality for latent topic analysis. But for real data these algorithms require additional ad-hoc heuristics, and even then often produce unusable results. We explain this poor performance by casting the problem of topic inference in the framework of Joint Stochastic Matrix Factorization (JSMF) and showing that previous methods violate the theoretical conditions necessary for a good solution to exist. We then propose a novel rectification method that learns high quality topics and their interactions even on small, noisy data. This method achieves results comparable to probabilistic techniques in several domains while maintaining scalability and provable optimality.

Japan's Digital Agency to cooperate with OpenAI on administrative tools The Digital Agency will enable its employees to use OpenAI's cutting-edge large language model-based AI tools for their work. The Digital Agency said Thursday that it will cooperate with OpenAI to fully use artificial intelligence technology in administrative work and service. As part of the initiative, the agency will enable its employees to use OpenAI's cutting-edge large language model-based AI tools for their work. It is also considering joint development with the U.S. company of a generative AI app for administrative use. The agency plans to provide its employees with access to generative AI tools and encourage other government agencies to adopt these services starting as early as fiscal year 2026.

Specifically, we train GPT -3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.

According to Meta's rules, advertisers who seek to run political ads in the United States have to undergo a special authorization process. Washington - Scammers are among the top political ad spenders on Meta's platforms, using deepfake videos of American politicians -- including President Donald Trump -- to promote fake government benefits, a watchdog group said Wednesday. The nonprofit Tech Transparency Project said it identified 63 scam advertisers that collectively spent $49 million on Facebook and Instagram, often targeting seniors with ads promoting fake stimulus checks, government spending cards and healthcare payments. The ads have reached tens of thousands of the platforms' users. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Neural Information Processing Systems http://nips.cc/

In 2025, Large Language Model (LLM) services have launched a new feature -- AI video chat -- allowing users to interact with AI agents via real-time video communication (RTC), just like chatting with real people. Despite its significance, no systematic study has characterized the performance of existing AI video chat systems. To address this gap, this paper proposes a comprehensive benchmark with carefully designed metrics across four dimensions: quality, latency, internal mechanisms, and system overhead. Using custom testbeds, we further evaluate five mainstream AI video chatbots with this benchmark. This work provides the research community a baseline of real-world performance and identifies unique system bottlenecks. In the meantime, our benchmarking results also open up several research questions for future optimizations of AI video chatbots.

Artificial intelligence is set to revolutionize social and political life in unpredictable ways, raising questions about the principles that ought to guide its development and regulation. By examining digital advertising and social media algorithms, this article highlights how artificial intelligence already poses a significant threat to the republican conception of liberty -- or freedom from unaccountable power -- and thereby highlights the necessity of protecting republican liberty when integrating artificial intelligence into society. At an individual level, these algorithms can subconsciously influence behavior and thought, and those subject to this influence have limited power over the algorithms they engage. At the political level, these algorithms give technology company executives and other foreign parties the power to influence domestic political processes, such as elections; the multinational nature of algorithm-based platforms and the speed with which technology companies innovate make incumbent state institutions ineffective at holding these actors accountable. At both levels, artificial intelligence has thus created a new form of unfreedom: digital domination. By drawing on the works of Quentin Skinner, Philip Pettit, and other republican theorists, this article asserts that individuals must have mechanisms to hold algorithms (and those who develop them) accountable in order to be truly free.

The proliferation of transformer-based language models has revolutionized NLP domain while simultaneously introduced significant challenges regarding model transparency and trustworthiness. The complexity of achieving explainable systems in this domain is evidenced by the extensive array of explanation methods and evaluation metrics developed by researchers. To address the challenge of selecting optimal explainability approaches, we present \textbf{\texttt{o-mega}}, a hyperparameter optimization tool designed to automatically identify the most effective explainable AI methods and their configurations within the semantic matching domain. We evaluate o-mega on a post-claim matching pipeline using a curated dataset of social media posts paired with refuting claims. Our tool systematically explores different explainable methods and their hyperparameters, demonstrating improved transparency in automated fact-checking systems. As a result, such automated optimization of explanation methods can significantly enhance the interpretability of claim-matching models in critical applications such as misinformation detection, contributing to more trustworthy and transparent AI systems.