The construction of experimental datasets is essential for expanding the scope of data-driven scientific discovery. Recent adva nces in natural language pro cessing (NLP) have facilitated automatic extraction of structured data from uns tructured scientific literature. While existing approaches--multi-step and direct methods--offer va luable capabilities, they also come with limitations when applied independently. He re, we propose a novel hybrid text-mining framework that integrates the advantages of both methods to convert unstructured scientific text into structured data. Our approach first tran sforms raw text into entity-recognized text, and subsequently into structured form. Furthermore, beyond the overall data structuring framework, we also enhance entity recogniti on performance by introducing an entity marker--a simple yet effective technique that uses sym bolic annotations to highlight target entities. Specifically, our entity marker-based hybrid approach not onl y consistently outperforms previous entity recognition approaches across three benchmark datasets (MatScholar, SOFC, and SOFC slot NER) but also improve the quality of final st ructured data--yielding up to a 58% improvement in entity-level F1 score and up to 83% improveme nt in relation-level F1 score compared to direct approach.

Large language models (LLMs) are rapidly pushing the limits of contemporary computing hardware. For example, training GPT-3 has been estimated to consume around 1300 MWh of electricity, and projections suggest future models may require city-scale (gigawatt) power budgets. These demands motivate exploration of computing paradigms beyond conventional von Neumann architectures. This review surveys emerging photonic hardware optimized for next-generation generative AI computing. We discuss integrated photonic neural network architectures (e.g., Mach-Zehnder interferometer meshes, lasers, wavelength-multiplexed microring resonators) that perform ultrafast matrix operations. We also examine promising alternative neuromorphic devices, including spiking neural network circuits and hybrid spintronic-photonic synapses, which combine memory and processing. The integration of two-dimensional materials (graphene, TMDCs) into silicon photonic platforms is reviewed for tunable modulators and on-chip synaptic elements. Transformer-based LLM architectures (self-attention and feed-forward layers) are analyzed in this context, identifying strategies and challenges for mapping dynamic matrix multiplications onto these novel hardware substrates. We then dissect the mechanisms of mainstream LLMs, such as ChatGPT, DeepSeek, and LLaMA, highlighting their architectural similarities and differences. We synthesize state-of-the-art components, algorithms, and integration methods, highlighting key advances and open issues in scaling such systems to mega-sized LLM models. We find that photonic computing systems could potentially surpass electronic processors by orders of magnitude in throughput and energy efficiency, but require breakthroughs in memory, especially for long-context windows and long token sequences, and in storage of ultra-large datasets.

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are recognized to have significant effects on industry and business dynamics, not least because of their impact on the preconditions for entrepreneurship. There is still a lack of knowledge of GenAI as a theme in entrepreneurship research. This paper presents a systematic literature review aimed at identifying and analyzing the evolving landscape of research on the effects of GenAI on entrepreneurship. We analyze 83 peer-reviewed articles obtained from leading academic databases: Web of Science and Scopus. Using natural language processing and unsupervised machine learning techniques with TF-IDF vectorization, Principal Component Analysis (PCA), and hierarchical clustering, five major thematic clusters are identified: (1) Digital Transformation and Behavioral Models, (2) GenAI-Enhanced Education and Learning Systems, (3) Sustainable Innovation and Strategic AI Impact, (4) Business Models and Market Trends, and (5) Data-Driven Technological Trends in Entrepreneurship. Based on the review, we discuss future research directions, gaps in the current literature, as well as ethical concerns raised in the literature. We highlight the need for more macro-level research on GenAI and LLMs as external enablers for entrepreneurship and for research on effective regulatory frameworks that facilitate business experimentation, innovation, and further technology development.

We envision the "virtual eye" as a next-generation, AI-powered platform that uses interconnected foundation models to simulate the eye's intricate structure and biological function across all scales. Advances in AI, imaging, and multiomics provide a fertile ground for constructing a universal, high-fidelity digital replica of the human eye. This perspective traces the evolution from early mechanistic and rule-based models to contemporary AI-driven approaches, integrating in a unified model with multimodal, multiscale, dynamic predictive capabilities and embedded feedback mechanisms. We propose a development roadmap emphasizing the roles of large-scale multimodal datasets, generative AI, foundation models, agent-based architectures, and interactive interfaces. Despite challenges in interpretability, ethics, data processing and evaluation, the virtual eye holds the potential to revolutionize personalized ophthalmic care and accelerate research into ocular health and disease.

Recently, the visual generation ability by GPT-4o(mni) has been unlocked by OpenAI. It demonstrates a very remarkable generation capability with excellent multimodal condition understanding and varied task instructions. In this paper, we aim to explore the capabilities of GPT-4o across various tasks. Inspired by previous study, we constructed a task taxonomy along with a carefully curated set of test samples to conduct a comprehensive qualitative test. Benefiting from GPT-4o's powerful multimodal comprehension, its image-generation process demonstrates abilities surpassing those of traditional image-generation tasks. Thus, regarding the dimensions of model capabilities, we evaluate its performance across six task categories: traditional image generation tasks, discriminative tasks, knowledge-based generation, commonsense-based generation, spatially-aware image generation, and temporally-aware image generation. These tasks not only assess the quality and conditional alignment of the model's outputs but also probe deeper into GPT-4o's understanding of real-world concepts. Our results reveal that GPT-4o performs impressively well in general-purpose synthesis tasks, showing strong capabilities in text-to-image generation, visual stylization, and low-level image processing. However, significant limitations remain in its ability to perform precise spatial reasoning, instruction-grounded generation, and consistent temporal prediction. Furthermore, when faced with knowledge-intensive or domain-specific scenarios, such as scientific illustrations or mathematical plots, the model often exhibits hallucinations, factual errors, or structural inconsistencies. These findings suggest that while GPT-4o marks a substantial advancement in unified multimodal generation, there is still a long way to go before it can be reliably applied to professional or safety-critical domains.

AI ARMS RACE: OpenAI co-founder Sam Altman joined three other artificial intelligence (AI) and technology executives for a Senate Commerce Committee hearing on winning the global AI race and strengthening domestic capabilities in computing and innovation. Sam Altman, chief executive officer of OpenAI, during a fireside chat at University College London (UCL) in London, UK, on Wednesday, May 24, 2023. Altman said part of the reason for his current tour of European cities is to discover a suitable location for a new office. EMBRACING AI: Some companies have been adjusting their workforce as they simultaneously embrace artificial intelligence and automation more, according to Forbes. NEW INVESTORS: OpenAI is shaking up its corporate structure to bring in new investors and accelerate the development of artificial general intelligence (AGI).

AI chatbots from tech companies such as OpenAI and Google have been getting so-called reasoning upgrades over the past months – ideally to make them better at giving us answers we can trust, but recent testing suggests they are sometimes doing worse than previous models. The errors made by chatbots, known as "hallucinations", have been a problem from the start, and it is becoming clear we may never get rid of them. Hallucination is a blanket term for certain kinds of mistakes made by the large language models (LLMs) that power systems like OpenAI's ChatGPT or Google's Gemini. It is best known as a description of the way they sometimes present false information as true. But it can also refer to an AI-generated answer that is factually accurate, but not actually relevant to the question it was asked, or fails to follow instructions in some other way.

Sen. John Fetterman, D-Pa., receives praise for his less-than-formal attire from Sam Altman during a Commerce Committee hearing. Sen. John Fetterman, D-Pa., was one of the final senators to question OpenAI chief Sam Altman during Thursday's Senate Commerce Committee hearing, and the subject of both Three Mile Island and the Democrat's penchant for Carhartt outerwear came up. Fetterman said that as a senator he has been able to meet people with "much more impressive jobs and careers" and that due to Altman's technology, "humans will have a wonderful ability to adapt." He told Altman that some Americans are worried about AI on various levels, and he asked the executive to address it. In response, Altman said he appreciated Fetterman's praise.

Recently, after an update that was supposed to make ChatGPT "better at guiding conversations toward productive outcomes," according to release notes from OpenAI, the bot couldn't stop telling users how brilliant their bad ideas were. ChatGPT reportedly told one person that their plan to sell literal "shit on a stick" was "not just smart--it's genius." Many more examples cropped up, and OpenAI rolled back the product in response, explaining in a blog post that "the update we removed was overly flattering or agreeable--often described as sycophantic." The company added that the chatbot's system would be refined and new guardrails would be put into place to avoid "uncomfortable, unsettling" interactions. But this was not just a ChatGPT problem. Sycophancy is a common feature of chatbots: A 2023 paper by researchers from Anthropic found that it was a "general behavior of state-of-the-art AI assistants," and that large language models sometimes sacrifice "truthfulness" to align with a user's views.

--Remarkable advancements in generative AI technology have given rise to a spectrum of novel deepfake categories with unprecedented leaps in their realism, and deepfakes are increasingly becoming a nuisance to law enforcement authorities and the general public. In particular, we observe alarming levels of confusion, deception, and loss of faith regarding multimedia content within society caused by face deepfakes, and existing deepfake detectors are struggling to keep up with the pace of improvements in deepfake generation. This is primarily due to their reliance on specific forgery artifacts, which limits their ability to generalise and detect novel deepfake types. T o combat the spread of malicious face deepfakes, this paper proposes a new strategy that leverages coarse-to-fine spatial information, semantic information, and their interactions while ensuring feature distinctiveness and reducing the redundancy of the modelled features. A novel feature orthogonality-based disentanglement strategy is introduced to ensure branch-level and cross-branch feature disentanglement, which allows us to integrate multiple feature vectors without adding complexity to the feature space or compromising generalisation. Comprehensive experiments on three public benchmarks: FaceForensics++, Celeb-DF, and the Deepfake Detection Challenge (DFDC) show that these design choices enable the proposed approach to outperform current state-of-the-art methods by 5% on the Celeb-DF dataset and 7% on the DFDC dataset in a cross-dataset evaluation setting. I NTRODUCTION The fake video published by BuzzFeed showing an apparent speech by former US President Barack Obama that was in fact performed by Jordan Peele [1] shows how easy it is to create convincing audio and video fakes. In recent years, we have seen an explosion of deep fakes, especially multimodal (video and audio) deep fakes. The extent and severe impact of fake multimedia content were clearly evident during the recent COVID-19 global pandemic [2] and the lead-up to the US federal 2020 election. Thus, the early detection of deep fakes is vital for stopping the spread of misinformation, which has influenced elections and led to serious consequences, including blackmail and fraud. To combat the surge of misleading deepfakes, a multitude of detection methods have emerged. However, there are significant concerns about whether these techniques can keep pace with the rapid advancements in deepfake generation [3], [4].