In a blog post spotted by The Verge, the company this week published its response to President Trump's AI Action Plan. Announced at the end of February, the initiative saw the White House seek input from private industry, with the goal of eventually enacting policy that will work to "enhance America's position as an AI powerhouse" and enable innovation in the sector. "America's robust, balanced intellectual property system has long been key to our global leadership on innovation. In the same document, the company recommends the US maintain tight export controls on AI chips to China. It also says the US government should broadly adopt AI tools.

The automated future just lurched a few steps closer. Over the past few weeks, nearly all of the major AI firms--OpenAI, Anthropic, Google, xAI, Amazon, Microsoft, and Perplexity, among others--have announced new products that are focused not on answering questions or making their human users somewhat more efficient, but on completing tasks themselves. They are being pitched for their ability to "reason" as people do and serve as "agents" that will eventually carry out complex work from start to finish. Humans will still nudge these models along, of course, but they are engineered to help fewer people do the work of many. Last month, Anthropic launched Claude Code, a coding program that can do much of a human software developer's job but far faster, "reducing development time and overhead."

An original photograph taken by Felice Frankel (left) and an AI-generated image of the same content. For over 30 years, science photographer Felice Frankel has helped MIT professors, researchers, and students communicate their work visually. Throughout that time, she has seen the development of various tools to support the creation of compelling images: some helpful, and some antithetical to the effort of producing a trustworthy and complete representation of the research. In a recent opinion piece published in Nature magazine, Frankel discusses the burgeoning use of generative artificial intelligence (GenAI) in images and the challenges and implications it has for communicating research. On a more personal note, she questions whether there will still be a place for a science photographer in the research community.

Tech companies have long claimed that simply expanding their current AI models will lead to artificial general intelligence (AGI), which can match or surpass human capabilities. But as the performance of the most recent models has plateaued, AI researchers doubt that today's technology will lead to superintelligent systems. In a survey of 475 AI researchers, about 76 per cent of respondents said it was "unlikely" or "very unlikely" that scaling up current approaches will succeed in achieving AGI. The findings are part of a report by the Association for the Advancement of Artificial Intelligence, an international scientific society based in Washington DC. This is a notable change in attitude from the "scaling is all you need" optimism that has spurred tech companies since the start of the generative AI boom in 2022.

AI expert Marva Bailer explains how, even though there are currently laws in place, the average person has more access than ever to create deepfakes of celebrities. Scarlett Johansson has taken a vocal stand on artificial intelligence, after having her likeness and voice used without permission. Last year, Johansson said she had been asked to voice OpenAI's Chatbot by CEO Sam Altman, but turned down the job, only for people to notice that the feature, named "Sky," sounded almost exactly like the actress. It was like: If that can happen to me, how are we going to protect ourselves from this? There's no boundary here; we're setting ourselves up to be taken advantage of," the 40-year-old told InStyle Magazine earlier this month. In a statement to NPR following the release of "Sky," Johansson said, "When I heard the released demo, I was shocked, angered and in disbelief that Mr. Altman would pursue a voice that sounded so eerily similar to mine that my closest friends and news outlets could not tell the difference.

The rise of Generative AI (GenAI) has sparked significant debate over balancing the interests of creative rightsholders and AI developers. As GenAI models are trained on vast datasets that often include copyrighted material, questions around fair compensation and proper attribution have become increasingly urgent. To address these challenges, this paper proposes a framework called \emph{Content ARCs} (Authenticity, Rights, Compensation). By combining open standards for provenance and dynamic licensing with data attribution, and decentralized technologies, Content ARCs create a mechanism for managing rights and compensating creators for using their work in AI training. We characterize several nascent works in the AI data licensing space within Content ARCs and identify where challenges remain to fully implement the end-to-end framework.

The rapid development of machine learning (ML) technologies, particularly large language models (LLMs), has led to major advancements in natural language processing (NLP, Abbasi et al. 2023). While much of this advancement happened under the umbrella of the common task framework which espouses transparency and openness (Abbasi et al. 2023), in recent years, closed LLMs such as GPT-3 and GPT-4 have set new performance standards in tasks ranging from text generation to question answering, demonstrating unprecedented capabilities in zero-shot and few-shot learning scenarios (Brown et al. 2020, OpenAI 2023). Given the strong performance of closed LLMs such as GPT-4, many studies within the LLM-as-a-judge paradigm rely on their scores as ground truth benchmarks for evaluating both open and closed LLMs (Chiang and Lee 2023), further entrenching the dominance of SOTA closed LLMs (Vergho et al. 2024). Along with closed LLMs, there are also LLMs where the pre-trained models (i.e., training weights) and inference code are publicly available ("open LLMs") such as Llama (Touvron et al. 2023, Dubey et al. 2024) as well as LLMs where the full training data and training code are also available ("open-source LLMs") such as OLMo (Groeneveld et al. 2024). Open and open-source LLMs provide varying levels of transparency for developers and researchers (Liu et al. 2023). Access to model weights, training data, and inference code enables several benefits for the user-developer-researcher community, including lower costs per input/output token through third-party API services, support for local/offline pre-training and fine-tuning, and deeper analysis of model biases and debiasing strategies. However, the dominance of closed LLMs raises a number of concerns, including accessibility and fairness (Strubell et al. 2020, Bender 2021, Irugalbandara et al. 2024).

Recent advances in generative AI have enabled increasingly sophisticated applications in various domains, from customer service chatbots to automated content generation. However, alongside these advancements, the vulnerability of large language models (LLMs) to adversarial inputs has emerged as a critical concern. Among these, prompt injection attacks pose a particularly insidious challenge, as they exploit the model's inherent instruction-following behavior to override intended constraints. While prompt injection is often discussed in theoretical contexts, its impact on deployed AI systems has been observed in practical settings. Research has demonstrated that even models with reinforced safety mechanisms--or with specific Knowledge based on RAG (Retrieval Augmented Generation)--can be manipulated into disclosing sensitive data, executing unauthorized instructions, or producing harmful content [4].

Generative Artificial Intelligence (genAI) represents a groundbreaking approach to creativity and automation, empowering machines to produce novel and highly realistic data, including images, text, and music. Among the diverse generative models, Diffusion Models have emerged as a powerful technique for high-quality image synthesis. Rooted in the principles of probabilistic modeling, Diffusion Models iteratively refine noise into detailed and coherent representations, achieving remarkable performance in domains like image generation, image inpainting and style transfer. Diffusion Models have gained traction due to their versatility and robustness, allowing them to excel in challenging tasks where conventional generative approaches, such as Generative Adversarial Networks (GANs), often struggle. These models leverage a forward-backward diffusion process, where images are progressively noised during the forward phase and restored to their original form during the reverse phase.

AI has the potential to empower everyone to become more independent and self-sufficient. The increasing use of artificial intelligence (AI)-based technologies in everyday settings creates new opportunities to understand how disabled people might use these technologies [Glazko, 2023]. It also enables the development of new types of assistive technologies as well as new ways for people with disabilities to interact with technology in ways that are both simpler (for those who need things simpler) and more efficient and effective for those who cannot use the traditional interfaces effectively. AI has been rapidly taken up in almost all accessibility communities [Adnin 2024, Alharbi 2024, Jiang 2024, Bennett 2024, Valencia 2023]. Since becoming widely available to the public, Generative Artificial Intelligence (GAI) has steadily gained recognition for its potential as a valuable tool in the private sector and by government, as well as a tool for accessibility. Studies of blind and visually impaired individuals have found that they use GAI to'offload' cognitively demanding tasks and obtain personal help such as fashion advice (e.g., [Xie 2024]), and to create content or retrieve information [Adnin 2024]. A study of GAI use by neurodiverse users found GAI can both support and complicate tasks like code-switching, emotional regulation, and accessing information [Glazko, 2025]. A study of people who use AAC found it helpful for text input [Valencia 2023]. However there are concerns with a technology that is often based on probability and thus tends toward the most common case rather than those at the margins.