Sub-visible particle analysis using flow imaging microscopy combined with deep learning has proven effective in identifying particle types, enabling the distinction of harmless components such as silicone oil from protein particles. However, the scarcity of available data and severe imbalance between particle types within datasets remain substantial hurdles when applying multi-class classifiers to such problems, often forcing researchers to rely on less effective methods. The aforementioned issue is particularly challenging for particle types that appear unintentionally and in lower numbers, such as silicone oil and air bubbles, as opposed to protein particles, where obtaining large numbers of images through controlled settings is comparatively straightforward. In this work, we develop a state-of-the-art diffusion model to address data imbalance by generating high-fidelity images that can augment training datasets, enabling the effective training of multi-class deep neural networks. We validate this approach by demonstrating that the generated samples closely resemble real particle images in terms of visual quality and structure. To assess the effectiveness of using diffusion-generated images in training datasets, we conduct large-scale experiments on a validation dataset comprising 500,000 protein particle images and demonstrate that this approach improves classification performance with no negligible downside. Finally, to promote open research and reproducibility, we publicly release both our diffusion models and the trained multi-class deep neural network classifiers, along with a straightforward interface for easy integration into future studies, at https://github.com/utkuozbulak/svp-generative-ai.

Selecting third-party software packages in open-source ecosystems like Python is challenging due to the large number of alternatives and limited transparent evidence for comparison. Generative AI tools are increasingly used in development workflows, but their suggestions often overlook dependency evaluation, emphasize popularity over suitability, and lack reproducibility. This creates risks for projects that require transparency, long-term reliability, maintainability, and informed architectural decisions. This study formulates software package selection as a Multi-Criteria Decision-Making (MCDM) problem and proposes a data-driven framework for technology evaluation. Automated data pipelines continuously collect and integrate software metadata, usage trends, vulnerability information, and developer sentiment from GitHub, PyPI, and Stack Overflow. These data are structured into a decision model representing relationships among packages, domain features, and quality attributes. The framework is implemented in PySelect, a decision support system that uses large language models to interpret user intent and query the model to identify contextually appropriate packages. The approach is evaluated using 798,669 Python scripts from 16,887 GitHub repositories and a user study based on the Technology Acceptance Model. Results show high data extraction precision, improved recommendation quality over generative AI baselines, and positive user evaluations of usefulness and ease of use. This work introduces a scalable, interpretable, and reproducible framework that supports evidence-based software selection using MCDM principles, empirical data, and AI-assisted intent modeling.

This paper introduces and formalizes Noosemìa, a novel cognitive-phenomenological pattern emerging from human interaction with generative AI systems, particularly those enabling dialogic or multimodal exchanges. We propose a multidisciplinary framework to explain how, under certain conditions, users attribute intentionality, agency, and even interiority to these systems - a process grounded not in physical resemblance, but in linguistic performance, epistemic opacity, and emergent technological complexity. By linking an LLM declination of meaning holism to our technical notion of the LLM Contextual Cognitive Field, we clarify how LLMs construct meaning relationally and how coherence and a simulacrum of agency arise at the human-AI interface. The analysis situates noosemia alongside pareidolia, animism, the intentional stance and the uncanny valley, distinguishing its unique characteristics. We also introduce a-noosemia to describe the phenomenological withdrawal of such projections. The paper concludes with reflections on the broader philosophical, epistemological and social implications of noosemic dynamics and directions for future research.

That was how Sam Altman, chief executive of OpenAI, described the latest upgrade to ChatGPT this week. The race Altman was referring to was artificial general intelligence (AGI), a theoretical state of AI where, by OpenAI's definition, a highly autonomous system is able to do a human's job. Describing the new GPT-5 model, which will power ChatGPT, as a "significant step on the path to AGI", he nonetheless added a hefty caveat. "[It is] missing something quite important, many things quite important," said Altman, such as the model's inability to "continuously learn" even after its launch. In other words, these systems are impressive but they have yet to crack the autonomy that would allow them to do a full-time job.

In mid-2023, if a user asked OpenAI's ChatGPT for a recipe for artichoke pasta or instructions on how to make a ritual offering to the ancient Canaanite deity Moloch, its response might have taken – very roughly – 2 watt-hours, or about as much electricity as an incandescent bulb consumes in 2 minutes. OpenAI released a model on Thursday that will underpin the popular chatbot – GPT-5. Ask that version of the AI for an artichoke recipe, and the same amount of pasta-related text could take several times – even 20 times – that amount of energy, experts say. As it rolled out GPT-5, the company highlighted the model's breakthrough capabilities: its ability to create websites, answer PhD-level science questions, and reason through difficult problems. But experts who have spent the past years working to benchmark the energy and resource usage of AI models say those new powers come at a cost: a response from GPT-5 may take a significantly larger amount of energy than a response from previous versions of ChatGPT.

Open AI CEO Sam Altman, center, speaks with boxer Jake Paul and wrestler Logan Paul in Emancipation Hall at the 60th Presidential Inauguration, Monday, Jan. 20, 2025, at the U.S. Capitol in Washington. TECH TENSIONS: Elon Musk escalated tensions in the critical artificial intelligence race Thursday, asserting his most advanced AI model, Grok 4 Heavy, was already outperforming OpenAI's newly launched GPT-5 two weeks ago. BOT BOOM: Small business owners are rapidly adopting artificial intelligence to power their growth, with many saying it will lead to more job opportunities this year, according to a Goldman Sachs survey. POCKET GENIUS: OpenAI unveiled GPT-5 on Thursday, calling it a significant upgrade from its predecessors and a major step forward in building the capabilities of large language models. AI-DOCTORED PHOTOS: Airbnb has reportedly apologized to a woman after the host of a Manhattan apartment where she stayed used artificial intelligence to doctor images of the home, saying she caused thousands of dollars in damage.

Few recent software releases have been as hyped as OpenAI's launch of its GPT-5 model. "GPT-5 is the first time that it really feels like talking to an expert in any topic, like a PhD level expert," said CEO Sam Altman in a recent press briefing. Is this new release as big of an upgrade as OpenAI claims? What do these changes actually mean for ChatGPT users? WIRED reporters are currently testing this newest drop from OpenAI, and seeing how GPT-5's ability to write, code, and perform other tasks compares to past releases.

Although generative artificial intelligence (AI) is a global endeavor, it is still most often discussed in the singular rather than in the plural form, with little consideration of the diversity of the cultural, linguistic, and national environments in which it is embedded and framed. To better understand the deep implications of this emerging technology, generative AI needs to be situated more deliberately and rigorously in wider and more diverse cultural geographies. While communities of AI researchers and practitioners have taken important steps in this direction,2 we argue that a truly global approach to AI can only emerge if we emphasize the centrality of culture, understood not just as intellectual activities but as all the ideas, customs, and social behaviors that make up a particular way of life, whether of a people, a period, a group or humanity in general.9 In his classic discussion of the term, Raymond Williams points out that any definition of culture should be "in the plural: the specific and variable cultures of different nations and periods, but also the specific and variable cultures of social and economic groups within a nation."9 A focus on AI cultures, therefore, can help us identify and counteract the limits of universalism, for which generative AI is perceived and represented as having no cultural or geographical coordinates, as well as the limits of cultural essentialism, for which specific cultural approaches are framed within stereotypical representations.

A fragment of a bronze military diploma from Sardinia, issued by the emperor Trajan to a sailor on a warship, as restored by Aeneas. If you believe the hype, generative artificial intelligence (AI) is the future. However, new research suggests the technology may also improve our understanding of the past. A team of computer scientists from Google DeepMind, working with classicists and archaeologists from universities in the United Kingdom and Greece, described a new machine-learning system designed to help experts to understand ancient Latin inscriptions. Named Aeneas (after the mythical hero of Rome's foundation epic), the system is a generative neural network designed to provide context for Latin inscriptions written between the 7th century BCE and the 8th century CE.

There has been rapid development in generative AI tools across the education sector, which in turn is leading to increased adoption by teachers. However, this raises concerns regarding the safety and age-appropriateness of the AI-generated content that is being created for use in classrooms. This paper explores Oak National Academy's approach to addressing these concerns within the development of the UK Government's first publicly available generative AI tool - our AI-powered lesson planning assistant (Aila). Aila is intended to support teachers planning national curriculum-aligned lessons that are appropriate for pupils aged 5-16 years. To mitigate safety risks associated with AI-generated content we have implemented four key safety guardrails: (1) prompt engineering to ensure AI outputs are generated within pedagogically sound and curriculum-aligned parameters; (2) input threat detection to mitigate attacks; (3) an Independent Asynchronous Content Moderation Agent (IACMA) to assess outputs against predefined safety categories; and (4) taking a human-in-the-loop approach, to encourage teachers to review generated content before it is used in the classroom. Through our on-going evaluation of these safety guardrails we have identified several challenges and opportunities to take into account when implementing and testing safety guardrails. This paper highlights ways to build more effective safety guardrails in generative AI education tools including the on-going iteration and refinement of guardrails, as well as enabling cross-sector collaboration through sharing both open-source code/datasets and learnings.