When a company triples in value in just a few months, as computer chip company Nvidia has, investors take notice. But regulators do too, because they know from experience how monopolies engage in illegal anti-competitive behavior that squashes competitors and manipulates the market to expand their dominance. The US Department of Justice (as well as other competition authorities and tech observers) suspects Nvidia has used such tactics to entrench its chips monopoly, and last month it was reported that the Department of Justice was opening an antitrust investigation. Before the pandemic, few beyond video game enthusiasts – whose top-of-the-line gaming computers and consoles are built on high-capacity Nvidia chips – had ever heard of the company. But thanks to the generative AI boom, Nvidia has become one of the fastest-growing companies ever, and its chips have powered every important AI milestone – including OpenAI's development of ChatGPT, which holds two-thirds of the AI business tools market.

We've collected some of the articles, opinion pieces, videos and resources relating to generative AI models. We periodically update this list to add further resources of interest. This article represents the fifth in the series.

Generative Artificial Intelligence (AI) models may carry societal transformation to an extent demanding a delicate balance between opportunity and risk. This manuscript is the first of a series of White Papers informing the development of IEEE-SA's p3995: `Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence (AI) Models', Chair: Marina Cort\^{e}s (https://standards.ieee.org/ieee/3395/11378/). In this first horizon-scanning we identify key attention areas for standards activities in AI. We examine different principles for regulatory efforts, and review notions of accountability, privacy, data rights and mis-use. As a safeguards standard we devote significant attention to the stability of global infrastructures and consider a possible overdependence on cloud computing that may result from densely coupled AI components. We review the recent cascade-failure-like Crowdstrike event in July 2024, as an illustration of potential impacts on critical infrastructures from AI-induced incidents in the (near) future. It is the first of a set of articles intended as White Papers informing the audience on the standard development. Upcoming articles will focus on regulatory initiatives, technology evolution and the role of AI in specific domains.

This study aims to improve knowledge-based question-answering (QA) systems by overcoming the limitations of existing Retrieval-Augmented Generation (RAG) models and implementing an advanced RAG system based on Graph technology to develop high-quality generative AI services. While existing RAG models demonstrate high accuracy and fluency by utilizing retrieved information, they may suffer from accuracy degradation as they generate responses using pre-loaded knowledge without reprocessing. Additionally, they cannot incorporate real-time data after the RAG configuration stage, leading to issues with contextual understanding and biased information. To address these limitations, this study implemented an enhanced RAG system utilizing Graph technology. This system is designed to efficiently search and utilize information. Specifically, it employs LangGraph to evaluate the reliability of retrieved information and synthesizes diverse data to generate more accurate and enhanced responses. Furthermore, the study provides a detailed explanation of the system's operation, key implementation steps, and examples through implementation code and validation results, thereby enhancing the understanding of advanced RAG technology. This approach offers practical guidelines for implementing advanced RAG systems in corporate services, making it a valuable resource for practical application.

Small and medium-sized agricultural holders face challenges like limited access to localized, timely information, impacting productivity and sustainability. Traditional extension services, which rely on in-person agents, struggle with scalability and timely delivery, especially in remote areas. We introduce Farmer.Chat, a generative AI-powered chatbot designed to address these issues. Leveraging Generative AI, Farmer.Chat offers personalized, reliable, and contextually relevant advice, overcoming limitations of previous chatbots in deterministic dialogue flows, language support, and unstructured data processing. Deployed in four countries, Farmer.Chat has engaged over 15,000 farmers and answered over 300,000 queries. This paper highlights how Farmer.Chat's innovative use of GenAI enhances agricultural service scalability and effectiveness. Our evaluation, combining quantitative analysis and qualitative insights, highlights Farmer.Chat's effectiveness in improving farming practices, enhancing trust, response quality, and user engagement.

As part of a broader look at the impact of generative AI, this study investigated the emotional responses of journalists to the release of ChatGPT at the time of its launch. By analyzing nearly 1 million Tweets from journalists at major U.S. news outlets, we tracked changes in emotional tone and sentiment before and after the introduction of ChatGPT in November 2022. Using various computational and natural language processing techniques to measure emotional shifts in response to ChatGPT's release, we found an increase in positive emotion and a more favorable tone post-launch, suggesting initial optimism toward AI's potential. This research underscores the pivotal role of journalists as interpreters of technological innovation and disruption, highlighting how their emotional reactions may shape public narratives around emerging technologies. The study contributes to understanding the intersection of journalism, emotion, and AI, offering insights into the broader societal impact of generative AI tools.

After weeks of speculation about a new and more powerful AI product in the works, OpenAI today announced its first "reasoning model." The program, known as o1, may in many respects be OpenAI's most powerful AI offering yet, with problem-solving capacities that resemble those of a human mind more than any software before. Or, at least, that's how the company is selling it. As with most OpenAI research and product announcements, o1 is, for now, somewhat of a tease. The start-up claims that the model is far better at complex tasks but released very few details about the model's training.

OpenAI said on Thursday it was launching its "Strawberry" series of AI models designed to spend more time processing answers to queries in order to solve hard problems. The models are capable of reasoning through complex tasks and can solve more challenging problems than previous models in science, coding and math, the AI firm said in a blog post. OpenAI used the code name Strawberry to refer to the project internally, while it dubbed the models announced on Thursday o1 and o1-mini. The o1 will be available in ChatGPT and its API starting Thursday, the company said. ChatGPT has struggled to recognize that the word "strawberry" contains three instances of the letter R. Noam Brown, a researcher at OpenAI focused on improving reasoning in the company's models, confirmed in a post on X that the models were the same as the Strawberry project.

OpenAI has unveiled its latest artificial intelligence model called o1, which, the company claims, can perform complex reasoning tasks more effectively than its predecessors. The release comes as OpenAI faces increasing competition in the race to develop more sophisticated AI systems. O1 was trained to "spend more time thinking through problems before they respond, much like a person would," OpenAI said on its website. "Through training, [the models] learn to refine their thinking process, try different strategies, and recognize their mistakes." OpenAI envisions the new model being used by healthcare researchers to annotate cell sequencing data, by physicists to generate mathematical formulas and software developers.

OpenAI made the last big breakthrough in artificial intelligence by increasing the size of its models to dizzying proportions, when it introduced GPT-4 last year. The company today announced a new advance that signals a shift in approach--a model that can "reason" logically through many difficult problems and is significantly smarter than existing AI without a major scale-up. The new model, dubbed OpenAI-o1, can solve problems that stump existing AI models, including OpenAI's most powerful existing model, GPT-4o. Rather than summon up an answer in one step, as a large language model normally does, it reasons through the problem, effectively thinking out loud as a person might, before arriving at the right result. "This is what we consider the new paradigm in these models," Mira Murati, OpenAI's chief technology officer, tells WIRED.