Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust. With the increasing accessibility of generative AI, tools for voice cloning, face-swapping, and synthetic media creation have advanced significantly, lowering both financial and technical barriers for their use. While these technologies present innovative opportunities, their rapid growth raises concerns about trust, privacy, and security. This white paper explores the implications of deepfake technology, analyzing its role in enabling fraud, misinformation, and the erosion of authenticity in multimedia. Using cost-effective, easy to use tools such as Runway, Rope, and ElevenLabs, we explore how realistic deepfakes can be created with limited resources, demonstrating the risks posed to individuals and organizations alike. By analyzing the technical and ethical challenges of deepfake mitigation and detection, we emphasize the urgent need for regulatory frameworks, public awareness, and collaborative efforts to maintain trust in digital media.

Individuals are increasingly relying on large language model (LLM)-enabled conversational agents for emotional support. While prior research has examined privacy and security issues in chatbots specifically designed for mental health purposes, these chatbots are overwhelmingly "rule-based" offerings that do not leverage generative AI. Little empirical research currently measures users' privacy and security concerns, attitudes, and expectations when using general-purpose LLM-enabled chatbots to manage and improve mental health. Through 21 semi-structured interviews with U.S. participants, we identified critical misconceptions and a general lack of risk awareness. Participants conflated the human-like empathy exhibited by LLMs with human-like accountability and mistakenly believed that their interactions with these chatbots were safeguarded by the same regulations (e.g., HIPAA) as disclosures with a licensed therapist. We introduce the concept of "intangible vulnerability," where emotional or psychological disclosures are undervalued compared to more tangible forms of information (e.g., financial or location-based data). To address this, we propose recommendations to safeguard user mental health disclosures with general-purpose LLM-enabled chatbots more effectively.

Thinking Machines Lab, an artificial intelligence company founded by top researchers who fled OpenAI, has raised a record 2 billion seed round that values the fledgling firm at 12 billion. The funding round was led by Andreessen Horowitz and included Nvidia, Accel, Cisco, and AMD--among others. The mammoth investment reflects the ultracompetitive race to build advanced AI systems, as well as the premium placed on top AI talent. It is the largest seed funding round in history. Thinking Machines is led by CEO Mira Murati, who stepped down as OpenAI's chief technology officer last September.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. As a classically trained singer preparing for a professional career, Erin Perry can see quite clearly how artificial intelligence is upending her field--all the way down to the classroom. Perry just completed her first year as a graduate student in voice performance at the Peabody Institute, the prestigious music conservatory run by Johns Hopkins University. It's been rewarding so far: She's been learning how to navigate the modern classical music sector and confronting the relevant impacts of generative A.I., having taken on a project to study the major record labels' lawsuit against the Amazon-backed A.I. startup Anthropic, which trained its models on songwriters' lyrics sans permission or compensation. Understandably, Perry's rather skeptical of A.I.'s artistic applications, and fearful of the sweeping effects it could have on her chosen field, especially as generative-music startups like Suno and Udio are programmed to replicate specific artists and musical styles.

In 2021, OpenAI shocked the world by improving the zero-shot classification accuracy on ImageNet from 11.5% to 76.2% via the CLIP series of models (Radford et al., 2021). This event redefined the goal of zero-shot prediction from producing models that generalized to unseen classes to those that generalized to unseen tasks entirely. Two fundamental drivers of CLIP's success were 1) the use of natural language as a medium for representing arbitrary classes (as in the previous state-of-the-art Visual N-grams (Li et al., 2017)), and 2) a massive, yet carefully designed pre-training set which significantly impacted downstream performance Radford et al. (2021); Fang et al. (2023); Xu et al. (2024). Despite the remarkable success of these foundation model-based pipelines Bommasani et al. (2022), there are unique components of zero-shot prediction that warrant investigation from a theoretical point of view. To clarify these gaps, we contrast zero-shot prediction (ZSP) with the related setting of few-shot learning (FSL). Let x X denote an input (often an image) that accompanies a discrete value y Y (often a class label).

Advancements in artificial intelligence (AI) have significantly enhanced the realism and interactivity of non-player characters (NPCs) in virtual reality (VR), creating more engaging and believable user experiences. This paper evaluates AI-driven NPCs within a VR interrogation simulator, focusing on their perceived realism, usability, and system performance. The simulator features two AI-powered NPCs, a suspect, and a partner, using GPT-4 Turbo to engage participants in a scenario to determine the suspect's guilt or innocence. A user study with 18 participants assessed the system using the System Usability Scale (SUS), Game Experience Questionnaire (GEQ), and a Virtual Agent Believability Questionnaire, alongside latency measurements for speech-to-text (STT), text-to-speech (TTS), OpenAI GPT-4 Turbo, and overall (cycle) latency. Results showed an average cycle latency of 7 seconds, influenced by the increasing conversational context. Believability scored 6.67 out of 10, with high ratings in behavior, social relationships, and intelligence but moderate scores in emotion and personality. The system achieved a SUS score of 79.44, indicating good usability. These findings demonstrate the potential of large language models to improve NPC realism and interaction in VR while highlighting challenges in reducing system latency and enhancing emotional depth. This research contributes to the development of more sophisticated AI-driven NPCs, revealing the need for performance optimization to achieve increasingly immersive virtual experiences.

Visual coverage path planning with unmanned aerial vehicles (UAVs) requires agents to strategically coordinate UAV motion and camera control to maximize coverage, minimize redundancy, and maintain battery efficiency. Traditional reinforcement learning (RL) methods rely on environment-specific reward formulations that lack semantic adaptability. This study proposes Prompt-Informed Reinforcement Learning (PIRL), a novel approach that integrates the zero-shot reasoning ability and in-context learning capability of large language models with curiosity-driven RL. PIRL leverages semantic feedback from an LLM, GPT-3.5, to dynamically shape the reward function of the Proximal Policy Optimization (PPO) RL policy guiding the agent in position and camera adjustments for optimal visual coverage. The PIRL agent is trained using OpenAI Gym and evaluated in various environments. Furthermore, the sim-to-real-like ability and zero-shot generalization of the agent are tested by operating the agent in Webots simulator which introduces realistic physical dynamics. Results show that PIRL outperforms multiple learning-based baselines such as PPO with static rewards, PPO with exploratory weight initialization, imitation learning, and an LLM-only controller. Across different environments, PIRL outperforms the best-performing baseline by achieving up to 14% higher visual coverage in OpenAI Gym and 27% higher in Webots, up to 25% higher battery efficiency, and up to 18\% lower redundancy, depending on the environment. The results highlight the effectiveness of LLM-guided reward shaping in complex spatial exploration tasks and suggest a promising direction for integrating natural language priors into RL for robotics.

Diffusion and flow-based models have become the state of the art for generative AI across a wide range of data modalities, including images, videos, shapes, molecules, music, and more. This tutorial provides a self-contained introduction to diffusion and flow-based generative models from first principles. We systematically develop the necessary mathematical background in ordinary and stochastic differential equations and derive the core algorithms of flow matching and denoising diffusion models. We then provide a step-by-step guide to building image and video generators, including training methods, guidance, and architectural design. This tutorial is ideal for machine learning researchers who want to develop a principled understanding of the theory and practice of generative AI.

While autonomous driving technologies continue to advance, current Advanced Driver Assistance Systems (ADAS) remain limited in their ability to interpret scene context or engage with drivers through natural language. These systems typically rely on predefined logic and lack support for dialogue-based interaction, making them inflexible in dynamic environments or when adapting to driver intent. This paper presents Scene-Aware Conversational ADAS (SC-ADAS), a modular framework that integrates Generative AI components including large language models, vision-to-text interpretation, and structured function calling to enable real-time, interpretable, and adaptive driver assistance. SC-ADAS supports multi-turn dialogue grounded in visual and sensor context, allowing natural language recommendations and driver-confirmed ADAS control. Implemented in the CARLA simulator with cloud-based Generative AI, the system executes confirmed user intents as structured ADAS commands without requiring model fine-tuning. We evaluate SC-ADAS across scene-aware, conversational, and revisited multi-turn interactions, highlighting trade-offs such as increased latency from vision-based context retrieval and token growth from accumulated dialogue history. These results demonstrate the feasibility of combining conversational reasoning, scene perception, and modular ADAS control to support the next generation of intelligent driver assistance.

Deep generative models are increasingly used for molecular discovery, with most recent approaches relying on equivariant graph neural networks (GNNs) under the assumption that explicit equivariance is essential for generating high-quality 3D molecules. However, these models are complex, difficult to train, and scale poorly. We investigate whether non-equivariant convolutional neural networks (CNNs) trained with rotation augmentations can learn equivariance and match the performance of equivariant models. We derive a loss decomposition that separates prediction error from equivariance error, and evaluate how model size, dataset size, and training duration affect performance across denoising, molecule generation, and property prediction. To our knowledge, this is the first study to analyze learned equivariance in generative tasks.