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].

The emergence of Generative AI (Gen AI) and Large Language Models (LLMs) has enabled more advanced chatbots capable of human-like interactions. However, these conversational agents introduce a broader set of operational risks that extend beyond traditional cybersecurity considerations. In this work, we propose a novel, instrumented risk-assessment metric that simultaneously evaluates potential threats to three key stakeholders: the service-providing organization, end users, and third parties. Our approach incorporates the technical complexity required to induce erroneous behaviors in the chatbot--ranging from non-induced failures to advanced prompt-injection attacks--as well as contextual factors such as the target industry, user age range, and vulnerability severity. To validate our metric, we leverage Garak, an open-source framework for LLM vulnerability testing. We further enhance Garak to capture a variety of threat vectors (e.g., misinformation, code hallucinations, social engineering, and malicious code generation). Our methodology is demonstrated in a scenario involving chatbots that employ retrieval-augmented generation (RAG), showing how the aggregated risk scores guide both short-term mitigation and longer-term improvements in model design and deployment. The results underscore the importance of multi-dimensional risk assessments in operationalizing secure, reliable AI-driven conversational systems.

While Artificial intelligence (AI), including Generative AI, are effective at generating high-quality traffic data and optimization solutions in intelligent transportation systems (ITSs), these techniques often demand significant training time and computational resources, especially in large-scale and complex scenarios. To address this, we introduce a novel and efficient algorithm for solving the maximum weighted independent set (MWIS) problem, which can be used to model many ITSs applications, such as traffic signal control and vehicle routing. Given the NP-hard nature of the MWIS problem, our proposed algorithm, DynLS, incorporates three key innovations to solve it effectively. First, it uses a scores-based adaptive vertex perturbation (SAVP) technique to accelerate convergence, particularly in sparse graphs. Second, it includes a region location mechanism (RLM) to help escape local optima by dynamically adjusting the search space. Finally, it employs a novel variable neighborhood descent strategy, ComLS, which combines vertex exchange strategies with a reward mechanism to guide the search toward high-quality solutions. Our experimental results demonstrate DynLS's superior performance, consistently delivering high-quality solutions within 1000 seconds. DynLS outperformed five leading algorithms across 360 test instances, achieving the best solution for 350 instances and surpassing the second-best algorithm, Cyclic-Fast, by 177 instances. Moreover, DynLS matched Cyclic-Fast's convergence speed, highlighting its efficiency and practicality. This research represents a significant advancement in heuristic algorithms for the MWIS problem, offering a promising approach to aid AI techniques in optimizing intelligent transportation systems.