Jim Acosta and James Carville speculated whether President Trump will try to rig the 2026 midterms in his favor on "The Jim Acosta Show." Liberal journalist Jim Acosta "interviewed" the artificially animated avatar of deceased teenager Joaquin Oliver to promote a gun control message on Monday. Working with the gun control group Change the Ref, founded by Oliver's parents, Acosta had conversation on his Substack with an avatar created by the father of the son, who was killed in the Parkland high school shooting in 2018. He would have turned 25 on Monday. "I would like to know what your solution would be for gun violence," Acosta asked.

Breakthroughs, discoveries, and DIY tips sent every weekday. A Jetsons-style Rosey household assistant robot may finally be a reality--only it probably doesn't look quite like you would expect. In fact, the contraption in question more closely resembles a custodian's mop and bucket. Though it might not be much of a looker, researchers from the Suzhou Industrial Park Institute of Vocational Technology and Xi'an Jiaotong-Liverpool University in China say their robot design can autonomously steer clear of most large furniture and children. It can even pick up loose toys and sort through smelly socks--at least some of the time.

NVIDIA CEO and co-founder Jensen Huang commends President Donald Trump's A.I. agenda and outlines what the country's job future will look like on'Special Report.' Brilliant Labs has just raised the bar for wearable technology. Their new product, Halo, is the world's thinnest open-source AI glasses, yet it packs more intelligence and capability than any other smartglasses that have come before it. Designed to look and feel like a normal pair of glasses, Halo reimagines what's possible when cutting-edge AI meets sleek design. Unlike bulky smartglasses from other brands, Halo feels natural on your face, weighing just over 40 grams.

Petco Love Lost is a free platform that uses AI-powered photo matching to reunite lost pets with their families. What if your dog had a dating profile? Or your cat showed up to brunch with your friends? Thanks to a viral TikTok trend, thousands of pet lovers are asking AI to reimagine their pets as people, and the results are surprisingly romantic. A recent survey asked 1,000 Americans just how deeply they connect with their pets.

Existing AIG (AI-generated) text detectors struggle in real-world settings despite succeeding in internal testing, suggesting that they may not be robust enough. We rigorously examine the machine-learning procedure to build these detectors to address this. Most current AIG text detection datasets focus on zero-shot generations, but little work has been done on few-shot or one-shot generations, where LLMs are given human texts as an example. In response, we introduce the Diverse Adversarial Corpus of Texts Yielded from Language models (DACTYL), a challenging AIG text detection dataset focusing on one-shot/few-shot generations. We also include texts from domain-specific continued-pre-trained (CPT) language models, where we fully train all parameters using a memory-efficient optimization approach. Many existing AIG text detectors struggle significantly on our dataset, indicating a potential vulnerability to one-shot/few-shot and CPT-generated texts. We also train our own classifiers using two approaches: standard binary cross-entropy (BCE) optimization and a more recent approach, deep X-risk optimization (DXO). While BCE-trained classifiers marginally outperform DXO classifiers on the DACTYL test set, the latter excels on out-of-distribution (OOD) texts. In our mock deployment scenario in student essay detection with an OOD student essay dataset, the best DXO classifier outscored the best BCE-trained classifier by 50.56 macro-F1 score points at the lowest false positive rates for both. Our results indicate that DXO classifiers generalize better without overfitting to the test set. Our experiments highlight several areas of improvement for AIG text detectors.

Topic modeling is a Natural Language Processing (NLP) technique that is used to identify latent themes and extract topics from text corpora by grouping similar documents based on their most significant keywords. Although widely researched in English, topic modeling remains understudied in Bengali due to its morphological complexity, lack of adequate resources and initiatives. In this contribution, a novel Graph Convolutional Network (GCN) based model called GHTM (Graph-Based Hybrid Topic Model) is proposed. This model represents input vectors of documents as nodes in the graph, which GCN uses to produce semantically rich embeddings. The embeddings are then decomposed using Non-negative Matrix Factorization (NMF) to get the topical representations of the underlying themes of the text corpus. This study compares the proposed model against a wide range of Bengali topic modeling techniques, from traditional methods such as LDA, LSA, and NMF to contemporary frameworks such as BERTopic and Top2Vec on three Bengali datasets. The experimental results demonstrate the effectiveness of the proposed model by outperforming other models in topic coherence and diversity. In addition, we introduce a novel Bengali dataset called "NCTBText" sourced from Bengali textbook materials to enrich and diversify the predominantly newspaper-centric Bengali corpora.

Many existing AI music generation tools rely on text prompts, complex interfaces, or instrument-like controls, which may require musical or technical knowledge that non-musicians do not possess. This paper introduces DeformTune, a prototype system that combines a tactile deformable interface with the MeasureVAE model to explore more intuitive, embodied, and explainable AI interaction. We conducted a preliminary study with 11 adult participants without formal musical training to investigate their experience with AI-assisted music creation. Thematic analysis of their feedback revealed recurring challenge--including unclear control mappings, limited expressive range, and the need for guidance throughout use. We discuss several design opportunities for enhancing explainability of AI, including multimodal feedback and progressive interaction support. These findings contribute early insights toward making AI music systems more explainable and empowering for novice users.

--This paper proposes a data-driven motion-planning framework for nonlinear systems that constructs a sequence of overlapping invariant polytopes. Around each randomly sampled waypoint, the algorithm identifies a convex admissible region and solves data-driven linear-matrix-inequality problems to learn several ellipsoidal invariant sets together with their local state-feedback gains. The convex hull of these ellipsoids--still invariant under a piece-wise-affine controller obtained by interpolating the gains--is then approximated by a polytope. Safe transitions between nodes are ensured by verifying the intersection of consecutive convex-hull polytopes and introducing an intermediate node for a smooth transition. Control gains are interpolated in real time via simplex-based interpolation, keeping the state inside the invariant polytopes throughout the motion. Unlike traditional approaches that rely on system dynamics models, our method requires only data to compute safe regions and design state-feedback controllers. The approach is validated through simulations, demonstrating the effectiveness of the proposed method in achieving safe, dynamically feasible paths for complex nonlinear systems. Over the years, several motion-planning approaches have been proposed, including graph search-based methods [2], sampling-based methods like rapidly exploring random trees (RRT) [3], behavior-based approaches [4], machine learning-based approaches [5], potential fields [6], and optimization-based techniques such as differential dynamic programming [7]. Among them, RRT, as a sampling-based approach, has received a surge of interest due to its success in robotic applications. However, most of these successful strategies are under assumptions that cannot be certified in many applications [8], [9]. For instance, the planning is typically performed assuring that the waypoints are kinematically feasible.

Counterspeech is a key strategy against harmful online content, but scaling expert-driven efforts is challenging. Large Language Models (LLMs) present a potential solution, though their use in countering conspiracy theories is under-researched. Unlike for hate speech, no datasets exist that pair conspiracy theory comments with expert-crafted counterspeech. We address this gap by evaluating the ability of GPT-4o, Llama 3, and Mistral to effectively apply counterspeech strategies derived from psychological research provided through structured prompts. Our results show that the models often generate generic, repetitive, or superficial results. Additionally, they over-acknowledge fear and frequently hallucinate facts, sources, or figures, making their prompt-based use in practical applications problematic.

Generative models achieve remarkable results in multiple data domains, including images and texts, among other examples. Unfortunately, malicious users exploit synthetic media for spreading misinformation and disseminating deepfakes. Consequently, the need for robust and stable fake detectors is pressing, especially when new generative models appear everyday. While the majority of existing work train classifiers that discriminate between real and fake information, such tools typically generalize only within the same family of generators and data modalities, yielding poor results on other generative classes and data domains. Towards a universal classifier, we propose the use of large pre-trained multi-modal models for the detection of generative content. Effectively, we show that the latent code of these models naturally captures information discriminating real from fake. Building on this observation, we demonstrate that linear classifiers trained on these features can achieve state-of-the-art results across various modalities, while remaining computationally efficient, fast to train, and effective even in few-shot settings. Our work primarily focuses on fake detection in audio and images, achieving performance that surpasses or matches that of strong baseline methods.