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.

Autonomous AI is no longer a hard-to-reach concept, it enables the agents to move beyond executing tasks to independently addressing complex problems, adapting to change while handling the uncertainty of the environment. However, what makes the agents truly autonomous? It is agentic reasoning, that is crucial for foundation models to develop symbolic logic, statistical correlations, or large-scale pattern recognition to process information, draw inferences, and make decisions. However, it remains unclear why and how existing agentic reasoning approaches work, in comparison to biological reasoning, which instead is deeply rooted in neural mechanisms involving hierarchical cognition, multimodal integration, and dynamic interactions. In this work, we propose a novel neuroscience-inspired framework for agentic reasoning. Grounded in three neuroscience-based definitions and supported by mathematical and biological foundations, we propose a unified framework modeling reasoning from perception to action, encompassing four core types, perceptual, dimensional, logical, and interactive, inspired by distinct functional roles observed in the human brain. We apply this framework to systematically classify and analyze existing AI reasoning methods, evaluating their theoretical foundations, computational designs, and practical limitations. We also explore its implications for building more generalizable, cognitively aligned agents in physical and virtual environments. Finally, building on our framework, we outline future directions and propose new neural-inspired reasoning methods, analogous to chain-of-thought prompting. By bridging cognitive neuroscience and AI, this work offers a theoretical foundation and practical roadmap for advancing agentic reasoning in intelligent systems. The associated project can be found at: https://github.com/BioRAILab/Awesome-Neuroscience-Agent-Reasoning .

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).

Artificial intelligence companies have been urged to replicate the safety calculations that underpinned Robert Oppenheimer's first nuclear test before they release all-powerful systems. Max Tegmark, a leading voice in AI safety, said he had carried out calculations akin to those of the US physicist Arthur Compton before the Trinity test and had found a 90% probability that a highly advanced AI would pose an existential threat. The US government went ahead with Trinity in 1945, after being reassured there was a vanishingly small chance of an atomic bomb igniting the atmosphere and endangering humanity. In a paper published by Tegmark and three of his students at the Massachusetts Institute of Technology (MIT), they recommend calculating the "Compton constant" – defined in the paper as the probability that an all-powerful AI escapes human control. In a 1959 interview with the US writer Pearl Buck, Compton said he had approved the test after calculating the odds of a runaway fusion reaction to be "slightly less" than one in three million.

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.

In recent years, as the sophistication and capabilities of large language models (LLMs) have grown, so have the tasks for which they're applicable, going beyond information extraction and synthesis15 to include analysis, content creation, and reasoning.8 Unsurprisingly, many researchers find them useful for research tasks, such as identifying relevant papers,19 synthesizing literature reviews,3 writing proposals,11 and analyzing data.31 They have also been found effective for investigative tasks, such as drug discovery.35 There is growing concern, however, that a large portion of this success hinges on prompt engineering, which is often an ad-hoc method to revise prompts being fed into an LLM to achieve a desired response or analysis.24 LLMs are increasingly being used in scientific research, but their application often involves ad-hoc decisions that can impact research quality.

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.

The whole point of Microsoft Copilot Vision for Windows is that it's like an AI assistant, looking over your shoulder as you struggle through a task and making suggestions. So, I was pretty convinced that if Microsoft were to release Copilot Vision for testing, it would be able to do something simple like help me play Windows Solitaire. Sometimes, Microsoft's new Copilot Vision for Windows feels like a real step forward for useful AI: this emerging Windows technology sees what you see on your screen, allowing you to talk to your PC and ask it for help. Unfortunately, that step ahead is often followed by that cliché: two steps back. Copilot Vision for Windows is, at times, genuinely helpful. Outside of some nostalgic tears by former Microsoft CEO Steve Ballmer, the announcement of Copilot Vision for Windows was the highlight of Microsoft's 50th anniversary celebration at the company's Redmond, Washington campus.

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.