Gladstone A.I. co-founders and CEOs Edouard Harris and Jeremie Harris explain the major role that A.I will play in national security and warfare on'The Will Cain Show.' Elon Musk met with members of the Senate DOGE caucus at the White House. MUSK'S MOVE BLOCKED: A California judge denied Elon Musk's move to halt OpenAI's efforts to convert it into a for-profit entity, saying in a ruling that the SpaceX and Tesla CEO hadn't met "the high burden required for a preliminary injunction." 'DOWNFALLS' OF AI: A federal judge has declined to impose sanctions on an attorney who submitted a brief that contained incorrect case citations and quotes generated by artificial intelligence. DEFEND YOUR DATA: Windows has always been a favorite target for hackers, but it seems they have now figured out how to actively target Macs as well. We've seen an alarming rise in malware affecting Mac computers, stealing personal data and cryptocurrency.

The UK's competition watchdog will not hold a formal investigation into Microsoft's partnership with the startup behind the artificial intelligence chatbot ChatGPT, stating that while the 2.9tn ( 2.3tn) tech company has "material influence" over OpenAI it does not control it. The Competition and Markets Authority (CMA) said Microsoft, OpenAI's biggest financial backer with a 13bn investment, acquired material influence over the San Francisco-based business in 2019 but did not exercise de facto control over it – and therefore did not meet the threshold for an official inquiry. The decision follows expressions of disquiet over the appointment of the former boss of Amazon UK, Doug Gurr, as the CMA's interim chair. The organisation's chief executive, Sarah Cardell, has also said the CMA does not want to create a "chilling effect" on business confidence, amid pressure from the UK government on regulators to produce pro-growth proposals. The CMA's executive director for mergers, Joel Bamford, said: "We have found that there has not been a change of control by Microsoft from material influence to de facto control over OpenAI. Because this change of control has not happened, the partnership in its current form does not qualify for review under the UK's merger control regime."

A US judge on Tuesday denied Elon Musk's request for a preliminary injunction to pause OpenAI's transition to a for-profit model but agreed to hear a trial in the fall of this year, the latest turn in the high-stakes legal fight. The tech billionaire does not have "the high burden required for a preliminary injunction" to block the conversion of OpenAI, said Yvonne Gonzalez Rogers, a US district judge in Oakland, California. But Rogers wrote in the order that she wanted to resolve the lawsuit quickly given "the public interest at stake and potential for harm if a conversion contrary to law occurred". Musk and OpenAI, which he co-founded as a non-profit in 2015 but left before it took off, have been embroiled in a yearlong legal battle. The CEO of Tesla and X, formerly Twitter, accuses OpenAI of straying from its founding mission to develop artificial intelligence for the good of humanity, not corporate profit.

US federal judge Yvonne Gonzalez Rogers has denied Elon Musk's request for an injunction that would have immediately stopped OpenAI's conversion into a for-profit entity. Musk filed for an injunction late last year after suing OpenAI and Microsoft and accusing them of telling investors not to fund rival AI companies, such as his own xAI. According to the Financial Times, the judge dismissed his request based on that claim of anticompetitive behavior. Gonzalez Rogers cited a previous statement by OpenAI CEO Sam Altman, saying that the company only warned certain investors who were granted access to sensitive information that their rights would be terminated if they made a non-passive investment in rival companies. The judge also reportedly rejected the request based on Musk's claim that OpenAI and Altman broke their contract with him and violated the company's founding mission of building AI "for the benefit of humanity."

The convergence of generative artificial intelligence and advanced computer vision technologies introduces a groundbreaking approach to transforming textual descriptions into three-dimensional representations. This research proposes a fully automated pipeline that seamlessly integrates text-to-image generation, various image processing techniques, and deep learning methods for reflection removal and 3D reconstruction. By leveraging state-of-the-art generative models like Stable Diffusion, the methodology translates natural language inputs into detailed 3D models through a multi-stage workflow. The reconstruction process begins with the generation of high-quality images from textual prompts, followed by enhancement by a reinforcement learning agent and reflection removal using the Stable Delight model. Advanced image upscaling and background removal techniques are then applied to further enhance visual fidelity. These refined two-dimensional representations are subsequently transformed into volumetric 3D models using sophisticated machine learning algorithms, capturing intricate spatial relationships and geometric characteristics. This process achieves a highly structured and detailed output, ensuring that the final 3D models reflect both semantic accuracy and geometric precision. This approach addresses key challenges in generative reconstruction, such as maintaining semantic coherence, managing geometric complexity, and preserving detailed visual information. Comprehensive experimental evaluations will assess reconstruction quality, semantic accuracy, and geometric fidelity across diverse domains and varying levels of complexity. By demonstrating the potential of AI-driven 3D reconstruction techniques, this research offers significant implications for fields such as augmented reality (AR), virtual reality (VR), and digital content creation.

Generative AI (GenAI) represents a transformative leap in artificial intelligence, using advanced models such as Transformers, GPT-4, and Gemini to generate human-like content in multiple modalities [1],[2]. Unlike traditional AI models that focus on classification or predictive tasks using predefined patterns, GenAI utilizes deep learning architectures like Transformer-based Large Language Models (LLMs)[2] to create text, images, code, and audio. The most prominent GenAI models include GPT-4 for advanced text generation[1] and Google Gemini for multimodal AI applications[2]. These models leverage massive data sets and training methodologies such as Reinforcement Learning with Human Feedback (RLHF)[3] and retrieval-augmented generation (RAG)[4] to improve their contextual understanding and adaptability. These AI models, trained on large-scale data, can understand context, generate creative outputs, automate workflows, and drive innovation across industries. GenAI is transforming fields such as healthcare (AI-assisted diagnosis and drug discovery[5]), finance (automated risk analysis and fraud detection[6]), customer support (intelligent virtual assistants[7]), and software development (AI-driven code generation[8]). The emergence of multimodal AI which enables models to process and generate text, images, and audio simultaneously is further unlocking new possibilities in automation, personalization, and decision-making.

As artificial intelligence (AI) continues to advance--particularly in generative models--an open question is whether these systems can replicate foundational models of human social perception. A well-established framework in social cognition suggests that social judgments are organized along two primary dimensions: valence (e.g., trustworthiness, warmth) and dominance (e.g., power, assertiveness). This study examines whether multimodal generative AI systems can reproduce this valence-dominance structure when evaluating facial images and how their representations align with those observed across world regions. Through principal component analysis (PCA), we found that the extracted dimensions closely mirrored the theoretical structure of valence and dominance, with trait loadings aligning with established definitions. However, many world regions and generative AI models also exhibited a third component, the nature and significance of which warrant further investigation. These findings demonstrate that multimodal generative AI systems can replicate key aspects of human social perception, raising important questions about their implications for AI-driven decision-making and human-AI interactions.

The dynamic nature of proteins, influenced by ligand interactions, is essential for comprehending protein function and progressing drug discovery. Traditional structure-based drug design (SBDD) approaches typically target binding sites with rigid structures, limiting their practical application in drug development. While molecular dynamics simulation can theoretically capture all the biologically relevant conformations, the transition rate is dictated by the intrinsic energy barrier between them, making the sampling process computationally expensive. To overcome the aforementioned challenges, we propose to use generative modeling for SBDD considering conformational changes of protein pockets. We curate a dataset of apo and multiple holo states of protein-ligand complexes, simulated by molecular dynamics, and propose a full-atom flow model (and a stochastic version), named DynamicFlow, that learns to transform apo pockets and noisy ligands into holo pockets and corresponding 3D ligand molecules. Additionally, the resultant holo-like states provide superior inputs for traditional SBDD approaches, playing a significant role in practical drug discovery. Modern deep learning is advancing several areas within drug discovery. Notably, among these, structure-based drug design (SBDD) (Anderson, 2003) emerges as a particularly significant and challenging domain. SBDD aims to discover drug-like ligand molecules specifically tailored to target binding sites. However, the complexity of chemical space and the dynamic nature of molecule conformations make traditional methods such as high throughput and virtual screenings inefficient. Additionally, relying on compound databases limits the diversity of identified molecules. Thus, deep generative models, such as autoregressive models (Luo et al., 2021; Peng et al., 2022) and diffusion models (Guan et al., 2023; Schneuing et al., 2022), have been introduced as a tool for de novo 3D ligand molecule design based on binding pockets, significantly transforming research paradigms. However, most SBDD methods based on deep generative models assume that proteins are rigid (Peng et al., 2022; Guan et al., 2024). However, the dynamic behavior of proteins is crucial for practical drug discovery (Karelina et al., 2023; Boehr et al., 2009). Thermodynamic fluctuations result in proteins existing as an ensemble of various conformational states, and such states may interact with different drug molecules. During binding, the protein's structure may undergo fine-tuning, adopting different conformations to optimize its interaction with the drug, a phenomenon referred to as induced fit (Sherman et al., 2006).

Normally, these models work by inverting the process of natural diffusion, where they start with a distribution of random noise and progressively transform it into a structured data distribution resembling the training data. This transformation occurs in multiple steps, which incrementally denoise the noisy sample until it reaches the desired complexity and detail. In contrast to the normal diffusion models mentioned above (Song et al. 2022, 2024), which generate synthetic images by denoising random noise distributions without incorporating any specific guidance, our GenMDI model generates a synthetic image considering the previous image and the next image surrounding the generated image. This image generation process with guidance or condition is known as the conditional diffusion process, which is often used in the generation of video frames (Voleti et al. 2022). By conditioning the reverse diffusion process on the previous and subsequent images, GenMDI ensures that the generated image maintains continuity and reflects the dynamics of the surrounding images. This approach not only preserves the natural flow and consistency of MDI time-series magnetograms but also enhances our model's ability to accurately generate synthetic images. To our knowledge, this is the first time a conditional diffusion model has been used to improve the temporal resolution of MDI magnetograms. The remainder of this paper is organized as follows. Section 2 describes the data used in this study.

The rapid adoption of generative AI (GenAI) in design has sparked discussions about its benefits and unintended consequences. While AI is often framed as a tool for enhancing productivity by automating routine tasks, historical research on automation warns of paradoxical effects, such as de-skilling and misplaced responsibilities. To assess UX practitioners' perceptions of AI, we analyzed over 120 articles and discussions from UX-focused subreddits. Our findings indicate that while practitioners express optimism about AI reducing repetitive work and augmenting creativity, they also highlight concerns about over-reliance, cognitive offloading, and the erosion of critical design skills. Drawing from human-automation interaction literature, we discuss how these perspectives align with well-documented automation ironies and function allocation challenges. We argue that UX professionals should critically evaluate AI's role beyond immediate productivity gains and consider its long-term implications for creative autonomy and expertise. This study contributes empirical insights into practitioners' perspectives and links them to broader debates on automation in design.