Sorcha O'Carroll, senior director for mergers at the CMA, said: "The invitation to comment is the first part of the CMA's information gathering process and comes in advance of launching any phase 1 investigation, which would only happen once the CMA has received the information it needs from the partnership parties."

The UK is considering an investigation into Microsoft's partnership with OpenAI to decide if it has resulted in an "acquisition of control" that's subject to antitrust law, the Competition and Markets Authority (CMA) wrote today. The regulator said it's considering "recent developments," no doubt referring to the Sam Altman CEO ouster drama in which Microsoft played a large role. "The CMA is now issuing an ITC to determine whether the Microsoft/OpenAI partnership, including recent developments, has resulted in a relevant merger situation and, if so, the potential impact on competition," it said in a news release. "The CMA will review whether the partnership has resulted in an acquisition of control -- that is, where it results in one party having material influence, de facto control or more than 50% of the voting rights over another entity." The regulator noted that the "close and multifaceted" partnership includes a multi-billion dollar investment by Microsoft, technology development cooperation and cloud services.

A little more than a year ago, the world seemed to wake up to the promise and dangers of artificial intelligence when OpenAI released ChatGPT, an application that enables users to converse with a computer in a singularly human way. Within five days, the chatbot had a million users. Within two months, it was logging a hundred million monthly users--a number that has now nearly doubled. Call this the year many of us learned to communicate, create, cheat, and collaborate with robots. Shortly after ChatGPT came out, Google released its own chatbot, Bard; Microsoft incorporated OpenAI's model into its Bing search engine; Meta débuted LLaMA; and Anthropic came out with Claude, a "next generation AI assistant for your tasks, no matter the scale."

The release of ChatGPT, Bard, and other large language model (LLM)-based chatbots has drawn huge attention on foundations models (FMs) worldwide. FMs are massive artificial intelligence (AI) models that are pre-trained on vast amounts of broad data and can be adapted to perform a wide variety of tasks [1]. With numerous projects already underway to explore their potential, it is widely predicted that FMs will serve as the fundamental building blocks for most future AI and artificial generative intelligence (AGI) systems. Many reusable solutions have been proposed to tackle various challenges in designing FM-based systems. However, there is a lack of systematic guidance on the architecture design of FM-based systems. The impact of integrating FMs into software architecture are not fully studied yet. Additionally, the FM's growing capabilities can eventually absorb the other components of AI systems, introducing the moving boundary and interface evolution challenges in architecture design. On the other hand, there are unique challenges on building responsible AI into the architecture of FM-based systems. First, accountability becomes more complex due to the involvement of multiple stakeholders.

The advancements in Artificial Intelligence (AI) technologies such as ChatGPT have gained popularity in recent days. The integration of ChatGPT in educational contexts has already created attractions due to a wide range of applications. However, the automatic generation of human-like texts also poses potential risks to academic integrity, especially when faced with writing-intensive language courses. Considering the ongoing debates, this study aims to investigate the academic policies and guidelines established by US universities regarding the use of ChatGPT in teaching and learning. The data sources include academic policies, statements, guidelines as well as relevant resources that were provided by the top 50 universities in the United States, according to U.S. News. Thematic analysis and qualitative analysis were employed in the analysis and showed that most top 50 universities were open but cautious towards the integration of generative AI in teaching and learning and also expressed their concerns on ethical usage, accuracy, and data privacy. Most universities also provided a variety of resources and guidelines, including syllabus templates/samples, workshops and discussions, shared articles, and one-on-one consultations, with focuses on general technical introduction, ethical concerns, pedagogical applications, preventive strategies, data privacy, limitations, and detective tools. The findings will inform future policy-making regarding the integration of ChatGPT in college-level education and influence the provision of supportive resources by universities for the appropriate application of ChatGPT in education.

Recent progress in generative AI, including large language models (LLMs) like ChatGPT, has opened up significant opportunities in fields ranging from natural language processing to knowledge discovery and data mining. However, there is also a growing awareness that the models can be prone to problems such as making information up or `hallucinations', and faulty reasoning on seemingly simple problems. Because of the popularity of models like ChatGPT, both academic scholars and citizen scientists have documented hallucinations of several different types and severity. Despite this body of work, a formal model for describing and representing these hallucinations (with relevant meta-data) at a fine-grained level, is still lacking. In this paper, we address this gap by presenting the Hallucination Ontology or HALO, a formal, extensible ontology written in OWL that currently offers support for six different types of hallucinations known to arise in LLMs, along with support for provenance and experimental metadata. We also collect and publish a dataset containing hallucinations that we inductively gathered across multiple independent Web sources, and show that HALO can be successfully used to model this dataset and answer competency questions.

Large Language Models have difficulty communicating uncertainty, which is a significant obstacle to applying LLMs to complex medical tasks. This study evaluates methods to measure LLM confidence when suggesting a diagnosis for challenging clinical vignettes. GPT4 was asked a series of challenging case questions using Chain of Thought and Self Consistency prompting. Multiple methods were investigated to assess model confidence and evaluated on their ability to predict the models observed accuracy. The methods evaluated were Intrinsic Confidence, SC Agreement Frequency and CoT Response Length. SC Agreement Frequency correlated with observed accuracy, yielding a higher Area under the Receiver Operating Characteristic Curve compared to Intrinsic Confidence and CoT Length analysis. SC agreement is the most useful proxy for model confidence, especially for medical diagnosis. Model Intrinsic Confidence and CoT Response Length exhibit a weaker ability to differentiate between correct and incorrect answers, preventing them from being reliable and interpretable markers for model confidence. We conclude GPT4 has a limited ability to assess its own diagnostic accuracy. SC Agreement Frequency is the most useful method to measure GPT4 confidence.

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The history of artificial intelligence has been punctuated by periods of so-called "AI winter," when the technology seemed to meet a dead end and funding dried up. Each one has been accompanied by proclamations that making machines truly intelligent is just too darned hard for humans to figure out. Google's release of Gemini, claimed to be a fundamentally new kind of AI model and the company's most powerful to date, suggests that a new AI winter isn't coming anytime soon. In fact, although the 12 months since ChatGPT launched have been a banner year for AI, there is good reason to think that the current AI boom is only getting started. OpenAI didn't have high expectations when it launched the "low key research preview" called ChatGPT in November 2022.

Hype about Gemini, Google DeepMind's long-rumored response to OpenAI's GPT-4, has been building for months. Now, the company has finally revealed what it has been working on in secret all this time. Gemini is Google's biggest AI launch yet--its push to take on competitors OpenAI and Microsoft in the race for AI supremacy. There is no doubt that the model is pitched as best-in-class across a wide range of capabilities--an "everything machine." Judging from its demos, it does many things very well--but few things that we haven't seen before.