Time has joined a growing number of publications to sign a licensing deal with OpenAI. The ChatGPT creator will legally be able to train its large language models on 101 years worth of the storied publication's journalism, as Axios first reported. OpenAI will also have access to real-time content from Time, with the apparent aim of answering user queries about breaking news. In return, OpenAI will cite Time and link back to source material on the publication's website. Perhaps Time will get a monetary kickback too, like other publishers that have shuffled over to OpenAI with a ragged cap in hand and an eye on one a new revenue source for struggling media companies.

The integration of Generative Artificial Intelligence (GAI) and Large Language Models (LLMs) in academia has spurred a global discourse on their potential pedagogical benefits and ethical considerations. Positive reactions highlight some potential, such as collaborative creativity, increased access to education, and empowerment of trainers and trainees. However, negative reactions raise concerns about ethical complexities, balancing innovation and academic integrity, unequal access, and misinformation risks. Through a systematic survey and text-mining-based analysis of global and national directives, insights from independent research, and eighty university-level guidelines, this study provides a nuanced understanding of the opportunities and challenges posed by GAI and LLMs in education. It emphasizes the importance of balanced approaches that harness the benefits of these technologies while addressing ethical considerations and ensuring equitable access and educational outcomes. The paper concludes with recommendations for fostering responsible innovation and ethical practices to guide the integration of GAI and LLMs in academia.

The importance of addressing fairness and bias in artificial intelligence (AI) systems cannot be over-emphasized. Mainstream media has been awashed with news of incidents around stereotypes and bias in many of these systems in recent years. In this survey, we fill a gap with regards to the minimal study of fairness and bias in Large Multimodal Models (LMMs) compared to Large Language Models (LLMs), providing 50 examples of datasets and models along with the challenges affecting them; we identify a new category of quantifying bias (preuse), in addition to the two well-known ones in the literature: intrinsic and extrinsic; we critically discuss the various ways researchers are addressing these challenges. Our method involved two slightly different search queries on Google Scholar, which revealed that 33,400 and 538,000 links are the results for the terms "Fairness and bias in Large Multimodal Models" and "Fairness and bias in Large Language Models", respectively. We believe this work contributes to filling this gap and providing insight to researchers and other stakeholders on ways to address the challenge of fairness and bias in multimodal A!.

Modern generative models demonstrate impressive capabilities, likely stemming from an ability to identify and manipulate abstract concepts underlying their training data. However, fundamental questions remain: what determines the concepts a model learns, the order in which it learns them, and its ability to manipulate those concepts? To address these questions, we propose analyzing a model's learning dynamics via a framework we call the concept space, where each axis represents an independent concept underlying the data generating process. By characterizing learning dynamics in this space, we identify how the speed at which a concept is learned, and hence the order of concept learning, is controlled by properties of the data we term concept signal. Further, we observe moments of sudden turns in the direction of a model's learning dynamics in concept space. Surprisingly, these points precisely correspond to the emergence of hidden capabilities, i.e., where latent interventions show the model possesses the capability to manipulate a concept, but these capabilities cannot yet be elicited via naive input prompting. While our results focus on synthetically defined toy datasets, we hypothesize a general claim on emergence of hidden capabilities may hold: generative models possess latent capabilities that emerge suddenly and consistently during training, though a model might not exhibit these capabilities under naive input prompting.

Exact unlearning was first introduced as a privacy mechanism that allowed a user to retract their data from machine learning models on request. Shortly after, inexact schemes were proposed to mitigate the impractical costs associated with exact unlearning. More recently unlearning is often discussed as an approach for removal of impermissible knowledge i.e. knowledge that the model should not possess such as unlicensed copyrighted, inaccurate, or malicious information. The promise is that if the model does not have a certain malicious capability, then it cannot be used for the associated malicious purpose. In this paper we revisit the paradigm in which unlearning is used for in Large Language Models (LLMs) and highlight an underlying inconsistency arising from in-context learning. Unlearning can be an effective control mechanism for the training phase, yet it does not prevent the model from performing an impermissible act during inference. We introduce a concept of ununlearning, where unlearned knowledge gets reintroduced in-context, effectively rendering the model capable of behaving as if it knows the forgotten knowledge. As a result, we argue that content filtering for impermissible knowledge will be required and even exact unlearning schemes are not enough for effective content regulation. We discuss feasibility of ununlearning for modern LLMs and examine broader implications.

This paper presents a generative artificial intelligence approach to probabilistic forecasting of electricity market signals, such as real-time locational marginal prices and area control error signals. Inspired by the Wiener-Kallianpur innovation representation of nonparametric time series, we propose a weak innovation autoencoder architecture and a novel deep learning algorithm that extracts the canonical independent and identically distributed innovation sequence of the time series, from which samples of future time series are generated. The validity of the proposed approach is established by proving that, under ideal training conditions, the generated samples have the same conditional probability distribution as that of the ground truth. Three applications involving highly dynamic and volatile time series in real-time market operations are considered: (i) locational marginal price forecasting for self-scheduled resources such as battery storage participants, (ii) interregional price spread forecasting for virtual bidders in interchange markets, and (iii) area control error forecasting for frequency regulations. Numerical studies based on market data from multiple independent system operators demonstrate the superior performance of the proposed generative forecaster over leading classical and modern machine learning techniques under both probabilistic and point forecasting metrics.

The recent proliferation of generative artificial intelligence (AI) technologies such as pre-trained large language models (LLMs) has opened up new frontiers in computational law. An exciting area of development is the use of AI to automate the deductive rule-based reasoning inherent in statutory and contract law. This paper argues that such automated deductive legal reasoning can now be viewed from the lens of software engineering, treating LLMs as interpreters of natural-language programs with natural-language inputs. We show how it is possible to apply principled software engineering techniques to enhance AI-driven legal reasoning of complex statutes and to unlock new applications in automated meta-reasoning such as mutation-guided example generation and metamorphic property-based testing.

Silicon Valley appears, once again, to be getting the better of America's newspapers and magazines. Tech companies are injecting every corner of the web with AI language models, which may pose an existential threat to journalism as we currently know it. After all, why go to a media outlet if ChatGPT can deliver the information you think you need? A growing number of media companies--the publishers of The Wall Street Journal, Business Insider, New York, Politico, The Atlantic, and many others--have signed licensing deals with OpenAI that will formally allow the start-up's AI models to incorporate recent partner articles into their responses. OpenAI is just the beginning, and such deals may soon be standard for major media companies: Perplexity, which runs a popular AI-powered search engine, has had conversations with various publishers (including The Atlantic's business division) about a potential ad-revenue-sharing arrangement, the start-up's chief business officer, Dmitry Shevelenko, told me yesterday.

OpenAI's abrupt move to ban access to its services in China is setting the scene for an industry shakeup, as local AI leaders from Baidu Inc. to Alibaba Group Holding Ltd. move to grab more of the field. The ChatGPT creator this week sent memos to Chinese users warning it will cut off access to its widely used AI development software and tools from July, triggering a scramble to fill the void. Since Tuesday, at least a half-dozen companies and startups including Tencent Holdings Ltd. and Zhipu AI began offering incentives to developers making the switch. OpenAI's shift will accentuate the divide between China and the U.S., which is trying to curb Beijing's AI and chip efforts. While the startup's exit offers an opportunity for sector leaders to grow their user base, it also deprives entrepreneurs and cash-strapped startups of some of the best tools available to fine-tune or get their AI applications off the ground.

OpenAI first added the ability for ChatGPT to speak in a one of several synthetic voices, or "personas," late last year. The demo in May used one of those voices to show off a newer, more capable AI system called GPT-4o that saw the chatbot speak in expressive tones, respond to a person's tone of voice and facial expressions, and have more complex conversations. One of the voices, which OpenAI called Sky, resembles the voice of an AI bot played by Johansson in the 2013 movie "Her," about a lonely man who falls in love with his AI assistant.