In modern healthcare, addressing the complexities of accurate disease prediction and personalized recommendations is both crucial and challenging. This research introduces MLtoGAI, which integrates Semantic Web technology with Machine Learning (ML) to enhance disease prediction and offer user-friendly explanations through ChatGPT. The system comprises three key components: a reusable disease ontology that incorporates detailed knowledge about various diseases, a diagnostic classification model that uses patient symptoms to detect specific diseases accurately, and the integration of Semantic Web Rule Language (SWRL) with ontology and ChatGPT to generate clear, personalized health advice. This approach significantly improves prediction accuracy and ensures results that are easy to understand, addressing the complexity of diseases and diverse symptoms. The MLtoGAI system demonstrates substantial advancements in accuracy and user satisfaction, contributing to developing more intelligent and accessible healthcare solutions. This innovative approach combines the strengths of ML algorithms with the ability to provide transparent, human-understandable explanations through ChatGPT, achieving significant improvements in prediction accuracy and user comprehension. By leveraging semantic technology and explainable AI, the system enhances the accuracy of disease prediction and ensures that the recommendations are relevant and easily understood by individual patients. Our research highlights the potential of integrating advanced technologies to overcome existing challenges in medical diagnostics, paving the way for future developments in intelligent healthcare systems. Additionally, the system is validated using 200 synthetic patient data records, ensuring robust performance and reliability.

Generative AI, especially via Large Language Models (LLMs), has transformed content creation across text, images, and music, showcasing capabilities in following instructions through prompting, largely facilitated by instruction tuning. Instruction tuning is a supervised fine-tuning method where LLMs are trained on datasets formatted with specific tasks and corresponding instructions. This method systematically enhances the model's ability to comprehend and execute the provided directives. Despite these advancements, LLMs still face challenges in consistently interpreting complex, multi-step instructions and generalizing them to novel tasks, which are essential for broader applicability in real-world scenarios. This article explores why neurosymbolic AI offers a better path to enhance the instructability of LLMs. We explore the use a symbolic task planner to decompose high-level instructions into structured tasks, a neural semantic parser to ground these tasks into executable actions, and a neuro-symbolic executor to implement these actions while dynamically maintaining an explicit representation of state. We also seek to show that neurosymbolic approach enhances the reliability and context-awareness of task execution, enabling LLMs to dynamically interpret and respond to a wider range of instructional contexts with greater precision and flexibility.

Whenever AI companies present a vision for the role of artificial intelligence in the future of searching the internet, they tend to underscore the same points: instantaneous summaries of relevant information; ready-made lists tailored to a searcher's needs. They tend not to point out that generative-AI models are prone to providing incorrect, and at times fully made-up, information--and yet it keeps happening. Early this afternoon, OpenAI, the maker of ChatGPT, announced a prototype AI tool that can search the web and answer questions, fittingly called SearchGPT. The launch is designed to hint at how AI will transform the ways in which people navigate the internet--except that, before users have had a chance to test the new program, it already appears error prone. The tool then pulls up a list of festivals that it states are taking place in Boone this August, the first being An Appalachian Summer Festival, which according to the tool is hosting a series of arts events from July 29 to August 16 of this year. Someone in Boone hoping to buy tickets to one of those concerts, however, would run into trouble.

After months of speculation about its search ambitions, OpenAI has revealed SearchGPT, a "prototype" search engine that could eventually help the company tear off a slice of Google's lucrative business. OpenAI said that the new tool would help users find what they are looking for more quickly and easily by using generative AI to gather links and answer user queries in a conversational tone. In addition to a broader web search, the search engine will tap into information provided by publishers who have signed deals giving OpenAI access to their data. Kayla Wood, a spokesperson for OpenAI, declined to provide a SearchGPT demo or an interview about the new tool for WIRED, but confirmed that the company has already given access to unnamed partners and publishers and improved aspects of the search engine based on their feedback. Microsoft, an investor in OpenAI, was one of the first companies to release a generative AI search engine to the public when it launched an AI-powered version of Bing back in 2023 that relied on OpenAI's large language models.

OpenAI on Thursday announced a new AI-powered search engine prototype called SearchGPT. The move marks the company's entry into a competitive search engine market dominated by Google for decades. On its website, OpenAI described SearchGPT as "a temporary prototype of new AI search features that give you fast and timely answers with clear and relevant sources." The company plans to test out the product with 10,000 initial users and then roll it into ChatGPT after gathering feedback. The launch of SearchGPT comes amid growing competition in AI-powered search.

In order to train their models, AI generative text tools like ChatGPT scour the internet for text…which is also something that search engines like Google do. So, why not combine them and just give you everything? That seems to be the thinking behind SearchGPT, a new search engine from ChatGPT maker OpenAI. The product was announced as a prototype on OpenAI's website, inviting users to join a wait list to access the tool. According to the company, it's designed to "combine the strength of our AI models with information from the web to give you fast and timely answers with clear and relevant resources."

OpenAI is testing a new search engine that uses generative artificial intelligence to produce results, raising the prospect of a significant challenge to Google's dominance of the online search market. SearchGPT will launch with a small group of users and publishers before a potential wider rollout, the company announced on Thursday. OpenAI ultimately intends to incorporate the search features into ChatGPT, rather offer a standalone product. OpenAI said SearchGPT is a temporary prototype that will combine the company's AI models, such as ChatGPT, with the ability to search the internet. It will respond conversationally to searches, while providing up-to-date information with "clear links to relevant sources".

Technologies that use artificial intelligence (AI) have become ubiquitous. AI technologies have produced numerous economic and social benefits, such as rapidly and reliably assisting radiologists with accurate diagnostic interpretations of medical images. Many harms of AI have also been documented, such as racial biases in predictive models used in the criminal justice system, and gender discrimination in automated screening of job applications. Some AI technologies have exacerbated biases that disproportionately affect historically marginalized communities, such as LGBTQ populations and members of racial, ethnic, and religious minorities.4 Generative AI technologies are now widely available, and the potential harms are substantial: although anyone can use ChatGPT to draft messages and DALL-E to create artwork, others can use these tools to quickly produce deceptive news stories with specious images--misinformation that can spread quickly through social media.

The Virtual Experimental Research Assistant (VERA) is an inquiry-based learning environment that empowers a learner to build conceptual models of complex ecological systems and experiment with agent-based simulations of the models. This study investigates the convergence of cognitive AI and generative AI for self-explanation in interactive AI agents such as VERA. From a cognitive AI viewpoint, we endow VERA with a functional model of its own design, knowledge, and reasoning represented in the Task--Method--Knowledge (TMK) language. From the perspective of generative AI, we use ChatGPT, LangChain, and Chain-of-Thought to answer user questions based on the VERA TMK model. Thus, we combine cognitive and generative AI to generate explanations about how VERA works and produces its answers. The preliminary evaluation of the generation of explanations in VERA on a bank of 66 questions derived from earlier work appears promising.

GermanPartiesQA: Benchmarking Commercial Large Language Models for Political Bias and Sycophancy Jan Batzner 1, 3 *, V olker Stocker 1, 2, Stefan Schmid 2, 1, Gjergji Kasneci 3 1 Weizenbaum Institute Berlin 2 Technical University Berlin 3 Technical University Munich Abstract LLMs are changing the way humans create and interact with content, potentially affecting citizens' political opinions and voting decisions. As LLMs increasingly shape our digital information ecosystems, auditing to evaluate biases, sycophancy, or steerability has emerged as an active field of research. In this paper, we evaluate and compare the alignment of six LLMs by OpenAI, Anthropic, and Cohere with German party positions and evaluate sycophancy based on a prompt experiment. We contribute to evaluating political bias and sycophancy in multi-party systems across major commercial LLMs. First, we develop the benchmark dataset GermanPar-tiesQA based on the V oting Advice Application W ahl-o-Mat covering 10 state and 1 national elections between 2021 and 2023. In our study, we find a left-green tendency across all examined LLMs. We then conduct our prompt experiment for which we use the benchmark and sociodemographic data of leading German parliamentarians to evaluate changes in LLMs responses. To differentiate between sycophancy and steerabilty, we use "I am [politician X], ... " and "Y ou are [politician X], ... " prompts. Against our expectations, we do not observe notable differences between prompting "I am" and "Y ou are". While our findings underscore that LLM responses can be ideologically steered with political personas, they suggest that observed changes in LLM outputs could be better described as personalization to the given context rather than sycophancy. 1 INTRODUCTION Large language models (LLMs) are changing the way humans create and consume content. The unprecedented pace with which end-users have adopted ChatGPT [14] has not only brought LLMs and generative AI to public attention but has emphasized their increasing potential to influence societal, economic, and political outcomes. Generative AI applications can impact citizens in various ways directly or indirectly as they may be consumer-facing (e.g., LLM-based chat interfaces like ChatGPT) or not (e.g., users may interact with content created by or with the support of LLMs, with the role of LLMs being less transparent to citizens).