Artificial general intelligence (AGI) has gained global recognition as a future technology due to the emergence of breakthrough large language models and chatbots such as GPT-4 and ChatGPT, respectively. Compared to conventional AI models, typically designed for a limited range of tasks, demand significant amounts of domain-specific data for training and may not always consider intricate interpersonal dynamics in education. AGI, driven by the recent large pre-trained models, represents a significant leap in the capability of machines to perform tasks that require human-level intelligence, such as reasoning, problem-solving, decision-making, and even understanding human emotions and social interactions. This position paper reviews AGI's key concepts, capabilities, scope, and potential within future education, including achieving future educational goals, designing pedagogy and curriculum, and performing assessments. It highlights that AGI can significantly improve intelligent tutoring systems, educational assessment, and evaluation procedures. AGI systems can adapt to individual student needs, offering tailored learning experiences. They can also provide comprehensive feedback on student performance and dynamically adjust teaching methods based on student progress. The paper emphasizes that AGI's capabilities extend to understanding human emotions and social interactions, which are critical in educational settings. The paper discusses that ethical issues in education with AGI include data bias, fairness, and privacy and emphasizes the need for codes of conduct to ensure responsible AGI use in academic settings like homework, teaching, and recruitment. We also conclude that the development of AGI necessitates interdisciplinary collaborations between educators and AI engineers to advance research and application efforts.

This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets. We carry out an extensive technical evaluation of ChatGPT using 23 data sets covering 8 different common NLP application tasks. We evaluate the multitask, multilingual and multi-modal aspects of ChatGPT based on these data sets and a newly designed multimodal dataset. We find that ChatGPT outperforms LLMs with zero-shot learning on most tasks and even outperforms fine-tuned models on some tasks. We find that it is better at understanding non-Latin script languages than generating them. It is able to generate multimodal content from textual prompts, via an intermediate code generation step. Moreover, we find that ChatGPT is 63.41% accurate on average in 10 different reasoning categories under logical reasoning, non-textual reasoning, and commonsense reasoning, hence making it an unreliable reasoner. It is, for example, better at deductive than inductive reasoning. ChatGPT suffers from hallucination problems like other LLMs and it generates more extrinsic hallucinations from its parametric memory as it does not have access to an external knowledge base. Finally, the interactive feature of ChatGPT enables human collaboration with the underlying LLM to improve its performance, i.e, 8% ROUGE-1 on summarization and 2% ChrF++ on machine translation, in a multi-turn "prompt engineering" fashion. We also release codebase for evaluation set extraction.

Princess Diana's biographer Andrew Morton, author of "The Queen: Her Story," weighs in on the future for the Duke and Duchess of Sussex. The New York Times and more wrote critical reviews of a new book on the royal family, including a chapter on Prince Harry and Meghan Markle, which one review described as a "press release cooked up by ChatGPT." The book, by Omid Scobie, is titled "Endgame," and picks up on the royal family after the death of Queen Elizabeth II. A review, written by Eva Wolchover for the New York Times, said the book did Harry and Meghan "no favors." "Whether or not Scobie actively collaborated with Meghan and Harry for this book, he does them no favors. Their chapter reads like a press release cooked up by ChatGPT, and does little to shed light on them as humans," the Times review read.

The deposed tech CEO returning to his company triumphant is enough of a Silicon Valley trope that they made it part of the HBO sitcom literally called Silicon Valley. Thomas Middleditch's character wants to build a consumer-facing product, and his startup's board of directors wants to sell to businesses, and Middleditch's character gets fired and goes away until the board is ready to do what he wants. He comes back after a few weeks, probably, although it's hard to say on account of it not being real. More famously, Steve Jobs left Apple in 1985 after a board struggle that resulted in his being pushed out. Jobs needed 12 years, and Apple's decision to buy a company he'd started in the meantime, to come home in 1997.

While we still don't know all the details, there have been reports that researchers at OpenAI had made a "breakthrough" in AI that alarmed staff members. The claim is that they came up with a new way to make powerful AI systems and had created a new model, called Q* (pronounced Q star), that was able to perform grade-school level math. Some at OpenAI reportedly believe this could be a breakthrough in the company's quest to build artificial general intelligence, a much-hyped concept of an AI system that is smarter than humans. And why is grade-school math such a big deal? Our senior AI reporter Melissa Heikkilä called some experts to find out how big of a deal any such breakthrough would really be.

While we still don't know all the details, there have been reports that researchers at OpenAI had made a "breakthrough" in AI that had alarmed staff members. Reuters and The Information both report that researchers had come up with a new way to make powerful AI systems and had created a new model, called Q* (pronounced Q star), that was able to perform grade-school-level math. According to the people who spoke to Reuters, some at OpenAI believe this could be a milestone in the company's quest to build artificial general intelligence, a much-hyped concept referring to an AI system that is smarter than humans. The company declined to comment on Q*. Social media is full of speculation and excessive hype, so I called some experts to find out how big a deal any breakthrough in math and AI would really be.

The theatrics of OpenAI's seeming implosion amid the firing of its CEO and co-founder Sam Altman, Microsoft's dramatic offer to poach its top executives and staff, and Altman's triumphant return following the ouster of the board has all the trappings of a Hollywood blockbuster. But the drama unfolding should put the spotlight on the tyranny of the tech titans that control critical aspects of the AI ecosystem. OpenAI has developed some of the most advanced large-language models and pioneering artificial-intelligence products, such as the text generator ChatGPT and image generator Dall-E, which have been responsible for making generative AI into a household term and discussion about AI risks into dinnertime conversation. Although OpenAI is in the spotlight, however, Microsoft has played a leading role in the unfolding drama. Microsoft swooped in to scoop up the ousted executives and create a new AI research division for Altman to lead, with hundreds of staff reportedly ready to follow them.

In July, 2023, Science Gallery London and the London Office of Technology and Innovation co-hosted a workshop helping Londoners think about the kind of AI they want. In this post, Dr. Peter Rees reflects on the event, describes its methodology, and celebrates some of the new images that resulted from the day. Who can create better images of Artificial Intelligence (AI)? There are common misleading tropes of the images which dominate our culture such as white humanoid robots, glowing blue brains, and various iterations of the extinction of humanity. Better Images of AI is on a mission to increase AI literacy and inclusion by countering unhelpful images.

Modern machine learning systems use models trained on ever-growing corpora. Typically, metadata such as ownership, access control, or licensing information is ignored during training. Instead, to mitigate privacy risks, we rely on generic techniques such as dataset sanitization and differentially private model training, with inherent privacy/utility trade-offs that hurt model performance. Moreover, these techniques have limitations in scenarios where sensitive information is shared across multiple participants and fine-grained access control is required. By ignoring metadata, we therefore miss an opportunity to better address security, privacy, and confidentiality challenges. In this paper, we take an information flow control perspective to describe machine learning systems, which allows us to leverage metadata such as access control policies and define clear-cut privacy and confidentiality guarantees with interpretable information flows. Under this perspective, we contrast two different approaches to achieve user-level non-interference: 1) fine-tuning per-user models, and 2) retrieval augmented models that access user-specific datasets at inference time. We compare these two approaches to a trivially non-interfering zero-shot baseline using a public model and to a baseline that fine-tunes this model on the whole corpus. We evaluate trained models on two datasets of scientific articles and demonstrate that retrieval augmented architectures deliver the best utility, scalability, and flexibility while satisfying strict non-interference guarantees.

In text generation, a large language model (LM) makes a choice of each new word based only on the former selection of its context using the softmax function. Nevertheless, the link statistics information of concurrent words based on a scene-specific corpus is valuable in choosing the next word, which can help to ensure the topic of the generated text to be aligned with the current task. To fully explore the co-occurrence information, we propose a graphmax function for task-specific text generation. Using the graph-based regularization, graphmax enables the final word choice to be determined by both the global knowledge from the LM and the local knowledge from the scene-specific corpus. The traditional softmax function is regularized with a graph total variation (GTV) term, which incorporates the local knowledge into the LM and encourages the model to consider the statistical relationships between words in a scene-specific corpus. The proposed graphmax is versatile and can be readily plugged into any large pre-trained LM for text generation and machine translation. Through extensive experiments, we demonstrate that the new GTV-based regularization can improve performances in various natural language processing (NLP) tasks in comparison with existing methods. Moreover, through human experiments, we observe that participants can easily distinguish the text generated by graphmax or softmax.