We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.

There is little doubt that generative AI will affect the economy--but how, exactly, remains an open question. Despite fears that these AI tools will upend jobs and exacerbate wealth inequality, early evidence suggests the technology could help level the playing field--but only if we deploy it in the right ways. Likewise, the Inflation Reduction Act and the Chips Act both have huge implications for the economy, and for efforts to revive America's high-tech manufacturing base. Rotman and Honan will look at who stands to benefit from these transformative economic events, and what the risks are. Then, on September 12, our next edition of Roundtables will tackle another important question: How should we regulate AI? Charlotte Jee, news editor, and Melissa Heikkilä, senior reporter for AI, will discuss the state of AI regulation today and what to watch for in the months ahead.

And with it, so are the digital labor platforms used by many AI companies to employ human gig workers. Those people perform the vital but often unseen labor of generating or labeling the masses of data that AI systems heavily rely on--often as part of efforts to make AIs more reliable and less biased. Even as these workers take on the vital task of making modern AI safer, the companies that employ them are uniformly failing to meet even a basic threshold of labor rights standards, according to a new report from the University of Oxford's Internet Institute, shared exclusively with TIME. Researchers assessed 15 digital work platforms--among them Amazon Mechanical Turk, Scale AI and Appen--and found that all of them were "still far from safeguarding basic standards of fair work," according to the report. "While the run for AI deployments gets public hype and momentum, workers behind the design, building and testing of these technological solutions, unfortunately, still face enormous challenges and experience unfair working conditions," the report says.

NEW ERA BEGINS - Fox News Channel kicks off its first week of the new primetime lineup. Laura Ingraham's "The Ingraham Angle" begins at 7 p.m. ET, followed by "Jesse Watters Primetime" at 8 p.m. ET, "Hannity" will remain at 9 p.m. ET and "Gutfeld!" BIDEN BUCKS - Whistleblower confirms attorney who donated to Biden's 2020 campaign'refused to bring charges' against Hunter. HAPPENING TODAY - RFK Jr has'no business' testifying in Congress on government censorship, Democrats say. 'TURNED UPSIDE-DOWN' - Lawyer reveals how wife found out husband was Gilgo Beach suspect as she files for divorce.

Compared to other forms of data collection, biometrics such as facial, iris or thumbprint scans have greater potential for invasive surveillance or discrimination, privacy advocates say. Facial recognition, for instance, makes it difficult to protect your privacy in public spaces -- law enforcement has already used it to identify protesters such as one man accused of assaulting a police officer during a racial justice demonstration in 2020. At least three Black men in the U.S. have filed lawsuits alleging wrongful arrests due to faulty facial recognition.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' Artificial intelligence is opening the door to a disturbing trend of people creating realistic images of children in sexual settings, which could increase the number of cases of sex crimes against kids in real life, experts warn. AI platforms that can mimic human conversation or create realistic images exploded in popularity late last year into 2023 following the release of chatbot ChatGPT, which served as a watershed moment for the use of artificial intelligence. As the curiosity of people across the world was piqued by the technology for work or school tasks, others have embraced the platforms for more nefarious purposes. The National Crime Agency, which is the UK's lead agency combating organized crime, warned this week that the proliferation of machine-generated explicit images of children is having a "radicalizing" effect "normalizing" pedophilia and disturbing behavior against kids.

Author Note We have no known conflict of interest to disclose. Correspondence concerning this article should be addressed to Hyemin Han, University of Alabama, Box 872031, Tuscaloosa, AL 35487, United States. Email: hyemin.han@ua.edu 2 Potential Benefits of Employing Large Language Models in Research in Moral Education and Development Abstract Recently, computer scientists have developed large language models (LLMs) by training prediction models with large-scale language corpora and human reinforcements. The LLMs have become one promising way to implement artificial intelligence with accuracy in various fields. Interestingly, recent LLMs possess emergent functional features that emulate sophisticated human cognition, especially in-context learning and the chain of thought, which were unavailable in previous prediction models. In this paper, I will examine how LLMs might contribute to moral education and development research. To achieve this goal, I will review the most recently published conference papers and ArXiv preprints to overview the novel functional features implemented in LLMs. I also intend to conduct brief experiments with ChatGPT to investigate how LLMs behave while addressing ethical dilemmas and external feedback. The results suggest that LLMs might be capable of solving dilemmas based on reasoning and revising their reasoning process with external input. Furthermore, a preliminary experimental result from the moral exemplar test may demonstrate that exemplary stories can elicit moral elevation in LLMs as do they among human participants. I will discuss the potential implications of LLMs on research on moral education and development with the results. Keywords: Large language models, Artificial intelligence, Moral reasoning, Moral exemplar, Simulation 3 Introduction One of the most impactful recent developments in computer science is large language models (LLMs) (Grossmann et al., 2023), which implement advanced artificial intelligence.

We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology. In contrast to formal mathematics, where large databases of formal proofs are available (e.g., the Lean Mathematical Library), current datasets of natural-language mathematics, used to benchmark language models, either cover only elementary mathematics or are very small. We address this by publicly releasing two new datasets: GHOSTS and miniGHOSTS. These are the first natural-language datasets curated by working researchers in mathematics that (1) aim to cover graduate-level mathematics, (2) provide a holistic overview of the mathematical capabilities of language models, and (3) distinguish multiple dimensions of mathematical reasoning. These datasets also test whether ChatGPT and GPT-4 can be helpful assistants to professional mathematicians by emulating use cases that arise in the daily professional activities of mathematicians. We benchmark the models on a range of fine-grained performance metrics. For advanced mathematics, this is the most detailed evaluation effort to date. We find that ChatGPT can be used most successfully as a mathematical assistant for querying facts, acting as a mathematical search engine and knowledge base interface. GPT-4 can additionally be used for undergraduate-level mathematics but fails on graduate-level difficulty. Contrary to many positive reports in the media about GPT-4 and ChatGPT's exam-solving abilities (a potential case of selection bias), their overall mathematical performance is well below the level of a graduate student. Hence, if your goal is to use ChatGPT to pass a graduate-level math exam, you would be better off copying from your average peer!

Recent work has shown that language models' (LMs) prompt-based learning capabilities make them well suited for automating data labeling in domains where manual annotation is expensive. The challenge is that while writing an initial prompt is cheap, improving a prompt is costly -- practitioners often require significant labeled data in order to evaluate the impact of prompt modifications. Our work asks whether it is possible to improve prompt-based learning without additional labeled data. We approach this problem by attempting to modify the predictions of a prompt, rather than the prompt itself. Our intuition is that accurate predictions should also be consistent: samples which are similar under some feature representation should receive the same prompt prediction. We propose Embroid, a method which computes multiple representations of a dataset under different embedding functions, and uses the consistency between the LM predictions for neighboring samples to identify mispredictions. Embroid then uses these neighborhoods to create additional predictions for each sample, and combines these predictions with a simple latent variable graphical model in order to generate a final corrected prediction. In addition to providing a theoretical analysis of Embroid, we conduct a rigorous empirical evaluation across six different LMs and up to 95 different tasks. We find that (1) Embroid substantially improves performance over original prompts (e.g., by an average of 7.3 points on GPT-JT), (2) also realizes improvements for more sophisticated prompting strategies (e.g., chain-of-thought), and (3) can be specialized to domains like law through the embedding functions.

Artificial Intelligence (AI), and in particular generative models, are transformative tools for knowledge work. They problematise notions of creativity, originality, plagiarism, the attribution of credit, and copyright ownership. Critics of generative models emphasise the reliance on large amounts of training data, and view the output of these models as no more than randomised plagiarism, remix, or collage of the source data. On these grounds, many have argued for stronger regulations on the deployment, use, and attribution of the output of these models. However, these issues are not new or unique to artificial intelligence. In this position paper, using examples from literary criticism, the history of art, and copyright law, I show how creativity and originality resist definition as a notatable or information-theoretic property of an object, and instead can be seen as the property of a process, an author, or a viewer. Further alternative views hold that all creative work is essentially reuse (mostly without attribution), or that randomness itself can be creative. I suggest that creativity is ultimately defined by communities of creators and receivers, and the deemed sources of creativity in a workflow often depend on which parts of the workflow can be automated. Using examples from recent studies of AI in creative knowledge work, I suggest that AI shifts knowledge work from material production to critical integration. This position paper aims to begin a conversation around a more nuanced approach to the problems of creativity and credit assignment for generative models, one which more fully recognises the importance of the creative and curatorial voice of the users of these models and moves away from simpler notational or information-theoretic views.