Individual human decision-makers may benefit from different forms of support to improve decision outcomes. However, a key question is which form of support will lead to accurate decisions at a low cost. In this work, we propose learning a decision support policy that, for a given input, chooses which form of support, if any, to provide. We consider decision-makers for whom we have no prior information and formalize learning their respective policies as a multi-objective optimization problem that trades off accuracy and cost. Using techniques from stochastic contextual bandits, we propose $\texttt{THREAD}$, an online algorithm to personalize a decision support policy for each decision-maker, and devise a hyper-parameter tuning strategy to identify a cost-performance trade-off using simulated human behavior. We provide computational experiments to demonstrate the benefits of $\texttt{THREAD}$ compared to offline baselines. We then introduce $\texttt{Modiste}$, an interactive tool that provides $\texttt{THREAD}$ with an interface. We conduct human subject experiments to show how $\texttt{Modiste}$ learns policies personalized to each decision-maker and discuss the nuances of learning decision support policies online for real users.

This paper assesses the accuracy, reliability and bias of the Large Language Model (LLM) ChatGPT-4 on the text analysis task of classifying the political affiliation of a Twitter poster based on the content of a tweet. The LLM is compared to manual annotation by both expert classifiers and crowd workers, generally considered the gold standard for such tasks. We use Twitter messages from United States politicians during the 2020 election, providing a ground truth against which to measure accuracy. The paper finds that ChatGPT-4 has achieves higher accuracy, higher reliability, and equal or lower bias than the human classifiers. The LLM is able to correctly annotate messages that require reasoning on the basis of contextual knowledge, and inferences around the author's intentions - traditionally seen as uniquely human abilities. These findings suggest that LLM will have substantial impact on the use of textual data in the social sciences, by enabling interpretive research at a scale.

Large language models (LLMs) have taken the scientific world by storm, changing the landscape of natural language processing and human-computer interaction. These powerful tools can answer complex questions and, surprisingly, perform challenging creative tasks (e.g., generate code and applications to solve problems, write stories, pieces of music, etc.). In this paper, we present a collaborative game design framework that combines interactive evolution and large language models to simulate the typical human design process. We use the former to exploit users' feedback for selecting the most promising ideas and large language models for a very complex creative task - the recombination and variation of ideas. In our framework, the process starts with a brief and a set of candidate designs, either generated using a language model or proposed by the users. Next, users collaborate on the design process by providing feedback to an interactive genetic algorithm that selects, recombines, and mutates the most promising designs. We evaluated our framework on three game design tasks with human designers who collaborated remotely.

Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet seen an attempt to develop foundation models for geospatial artificial intelligence (GeoAI). In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI. We first investigate the potential of many existing FMs by testing their performances on seven tasks across multiple geospatial subdomains including Geospatial Semantics, Health Geography, Urban Geography, and Remote Sensing. Our results indicate that on several geospatial tasks that only involve text modality such as toponym recognition, location description recognition, and US state-level/county-level dementia time series forecasting, these task-agnostic LLMs can outperform task-specific fully-supervised models in a zero-shot or few-shot learning setting. However, on other geospatial tasks, especially tasks that involve multiple data modalities (e.g., POI-based urban function classification, street view image-based urban noise intensity classification, and remote sensing image scene classification), existing foundation models still underperform task-specific models. Based on these observations, we propose that one of the major challenges of developing a FM for GeoAI is to address the multimodality nature of geospatial tasks. After discussing the distinct challenges of each geospatial data modality, we suggest the possibility of a multimodal foundation model which can reason over various types of geospatial data through geospatial alignments. We conclude this paper by discussing the unique risks and challenges to develop such a model for GeoAI.

ChatGPT (GPT) has become one of the most talked-about innovations in recent years, with over 100 million users worldwide. However, there is still limited knowledge about the sources of information GPT utilizes. As a result, we carried out a study focusing on the sources of information within the field of environmental science. In our study, we asked GPT to identify the ten most significant subdisciplines within the field of environmental science. We then asked it to compose a scientific review article on each subdiscipline, including 25 references. We proceeded to analyze these references, focusing on factors such as the number of citations, publication date, and the journal in which the work was published. Our findings indicate that GPT tends to cite highly-cited publications in environmental science, with a median citation count of 1184.5. It also exhibits a preference for older publications, with a median publication year of 2010, and predominantly refers to well-respected journals in the field, with Nature being the most cited journal by GPT. Interestingly, our findings suggest that GPT seems to exclusively rely on citation count data from Google Scholar for the works it cites, rather than utilizing citation information from other scientific databases such as Web of Science or Scopus. In conclusion, our study suggests that Google Scholar citations play a significant role as a predictor for mentioning a study in GPT-generated content. This finding reinforces the dominance of Google Scholar among scientific databases and perpetuates the Matthew Effect in science, where the rich get richer in terms of citations. With many scholars already utilizing GPT for literature review purposes, we can anticipate further disparities and an expanding gap between lesser-cited and highly-cited publications.

In the rapidly evolving field of artificial intelligence, natural language processing has become a focal point for researchers and developers alike. As a testament to the remarkable progress in this area, several groundbreaking language models have emerged in recent years, pushing the boundaries of what machines can understand and generate. In this article, we will delve into the latest advancements in the world of large-scale language models, exploring enhancements introduced by each model, their capabilities, and potential applications. We'll start with a seminal BERT model from 2018 and finish with this year's latest breakthroughs like LLaMA by Meta AI and GPT-4 by OpenAI. If you'd like to skip around, here are the language models we featured: If this in-depth educational content is useful for you, you can subscribe to our AI research mailing list to be alerted when we release new material. In 2018, the Google AI team introduced a new cutting-edge model for Natural Language Processing (NLP) – BERT, or Bidirectional Encoder Representations from Transformers.

Bots like ChatGPT may not be able to pull off the next big Microsoft server worm or Colonial Pipeline ransomware super-infection but they may help criminal gangs and nation-state hackers develop some attacks against IT, according to Rob Joyce, director of the NSA's Cybersecurity Directorate. Joyce, speaking at CrowdStrike's Government Summit Tuesday, said he doesn't expect to see -- at least not "in the near term" -- AI used "for automated attacks that will rip through systems at speeds that are unfathomable today." Machine learning and its chatbot offspring are "the tools that are going to flow and increase the pace of the threat," Joyce claimed. "It's not going to generate the threat itself." Miscreants can use ML software to develop more authentic-seeming phishing lures and craft better ransom notes, while also scanning larger volumes of data for sensitive info they can monetize, he offered.

At a recent White House press conference, a Fox News correspondent asked the Biden administration's press secretary about AI safety researcher Eliezer Yudkowsky's highly publicized claim that if we don't pause or halt the development of artificial intelligence, then "literally everyone on earth will die." The question was met with some laughter from the White House press corps. But as someone with a technical background who covers AI and talks regularly to researchers, developers, and investors in the field, I saw nothing to chuckle at. Rather, I and other more optimistic AI watchers worry that overly dire warnings of imminent AI-driven destruction may cause us to pause or halt the development of a powerful technology with immense potential for improving our lives. Insiders hold a truly wide range of opinions on the best way to approach AI--from Yudkowsky's insistence that we immediately abandon all research in the area, to my own more moderate concern about large-scale industrial accidents arising from misuse of the technology, to an extreme optimism in some quarters about AI's potential to turn humanity into an immortal, star-spanning species.

OpenAI launched a Bug Bounty Program Tuesday that will pay you up to $20,000 if you uncover flaws in ChatGPT and its other artificial intelligence systems. The San Francisco-based company is inviting researchers, ethical hackers and tech enthusiasts to review certain functionality of ChatGPT and the framework of how systems communicate and share data with third-party applications. Rewards will be given to people based on the severity of the bugs they report, with compensation starting from $200 per vulnerability. The program follows news of Italy banning ChatGPT after a data breach at OpenAI allowed users to view people's conversations- an issue the bug bounty hunters could find before it strikes again. 'We are excited to build on our coordinated disclosure commitments by offering incentives for qualifying vulnerability information,' OpenAI shared in a statement.

At the end of March, the Italian Data Protection Authority (the "Garante"), announced that OpenAI's fancy new ChatGPT software would imminently be blocked from use within the European nation over concerns that ChatGPT's training and function violate the EU's General Data Protection Regulation (GDPR). On Wednesday, the Garante published a list of necessary steps OpenAI will have to take by the end of April if Italy is to lift its temporary limitation on the processing of its user data. "OpenAI will have to draft and make available, on its website, an information notice describing the arrangements and logic of the data processing required for the operation of ChatGPT along with the rights afforded to data subjects," the Garante announced. Additionally, Italian users must be shown said notice and will have to declare that they are over the age of 18 prior to the completion of their registrations. What's more, the company will be required to age gate the site to filter out users under the age of 18 by the end of September.