Language models (LMs) are increasingly being used in open-ended contexts, where the opinions reflected by LMs in response to subjective queries can have a profound impact, both on user satisfaction, as well as shaping the views of society at large. In this work, we put forth a quantitative framework to investigate the opinions reflected by LMs -- by leveraging high-quality public opinion polls and their associated human responses. Using this framework, we create OpinionsQA, a new dataset for evaluating the alignment of LM opinions with those of 60 US demographic groups over topics ranging from abortion to automation. Across topics, we find substantial misalignment between the views reflected by current LMs and those of US demographic groups: on par with the Democrat-Republican divide on climate change. Notably, this misalignment persists even after explicitly steering the LMs towards particular demographic groups. Our analysis not only confirms prior observations about the left-leaning tendencies of some human feedback-tuned LMs, but also surfaces groups whose opinions are poorly reflected by current LMs (e.g., 65+ and widowed individuals). Our code and data are available at https://github.com/tatsu-lab/opinions_qa.

Nowadays, the ever-increasing urbanization and industrialization has led to a growing of water demand and a decrease in water supply and resources, thus creating a huge divergence between demand and supply. Therefore, water resources can play an important role in regional socio-economic and environmental development [Setegn, 2015]. The effective distribution of water resources in both civil and industry life indicates the levels of urban sustainability and social inclusiveness. Proper water distribution and forecasting can act as a baseline for achieving optimal resource allocation and mitigating the gap between supply and demand, thus improving operations, planning and management. In Greece, the recent years, the need for accurate water demand forecasting has become particularly important [Bithas and Chrysostomos, 2006]. The systematically extraction of non-renewable ground water, the insertion of chemicals for water purification, the drought caused by climate changes in the region of the Mediterranean and the sudden rise of water demand due to the increase of refugees and migrants has created many environmental issues on the quantity and quality of the water resources as well as previously unseen socio-economic and political problems. Therefore, an accurate forecasting of water consumption can be a decisive factor for proper planning, management and optimization. Water consumption data are seen as time series, since a measurement of water consumption levels is taken periodically (weekly, monthly, quarterly).

Text-to-Image (T2I) generation is enabling new applications that support creators, designers, and general end users of productivity software by generating illustrative content with high photorealism starting from a given descriptive text as a prompt. Such models are however trained on massive amounts of web data, which surfaces the peril of potential harmful biases that may leak in the generation process itself. In this paper, we take a multi-dimensional approach to studying and quantifying common social biases as reflected in the generated images, by focusing on how occupations, personality traits, and everyday situations are depicted across representations of (perceived) gender, age, race, and geographical location. Through an extensive set of both automated and human evaluation experiments we present findings for two popular T2I models: DALLE-v2 and Stable Diffusion. Our results reveal that there exist severe occupational biases of neutral prompts majorly excluding groups of people from results for both models. Such biases can get mitigated by increasing the amount of specification in the prompt itself, although the prompting mitigation will not address discrepancies in image quality or other usages of the model or its representations in other scenarios. Further, we observe personality traits being associated with only a limited set of people at the intersection of race, gender, and age. Finally, an analysis of geographical location representations on everyday situations (e.g., park, food, weddings) shows that for most situations, images generated through default location-neutral prompts are closer and more similar to images generated for locations of United States and Germany.

The beekeeping sector has undergone considerable production variations over the past years due to adverse weather conditions, occurring more frequently as climate change progresses. These phenomena can be high-impact and cause the environment to be unfavorable to the bees' activity. We disentangle the honey production drivers with tree-based methods and predict honey production variations for hives in Italy, one of the largest honey producers in Europe. The database covers hundreds of beehive data from 2019-2022 gathered with advanced precision beekeeping techniques. We train and interpret the machine learning models making them prescriptive other than just predictive. Superior predictive performances of tree-based methods compared to standard linear techniques allow for better protection of bees' activity and assess potential losses for beekeepers for risk management.

It's important to think about what the dangers of A.I. are. I don't worry about evil robots taking over the world with their red laser eyes, that's not what keeps me up at night. But what does keep me up at night is the implementation of a surveillance state, like China is attempting to do, the creation of monopolistic like controls over A.I. by corporations that control large amounts of data, you know that I do worry about. I also think it's important to realize the limitations of our ability to regulate, because even if we clamp down on the development of new A.I., unscrupulous actors are still going to be developing it for economic gain. Foreign adversaries are still going to be developing it, too, for a competitive advantage over our country.

An open letter published today calls for "all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4." This 6-month moratorium would be better than no moratorium. I have respect for everyone who stepped up and signed it. I refrained from signing because I think the letter is understating the seriousness of the situation and asking for too little to solve it. The key issue is not "human-competitive" intelligence (as the open letter puts it); it's what happens after AI gets to smarter-than-human intelligence.

Probabilistic machine learning methods are becoming increasingly powerful tools in data analysis, informing a range of critical decisions across disciplines and applications, from forecasting election results to predicting the impact of microloans on addressing poverty. This class of methods uses sophisticated concepts from probability theory to handle uncertainty in decision-making. But the math is only one piece of the puzzle in determining their accuracy and effectiveness. In a typical data analysis, researchers make many subjective choices, or potentially introduce human error, that must also be assessed in order to cultivate users' trust in the quality of decisions based on these methods. To address this issue, MIT computer scientist Tamara Broderick, associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems (LIDS), and a team of researchers have developed a classification system--a "taxonomy of trust"--that defines where trust might break down in a data analysis and identifies strategies to strengthen trust at each step.

They include "grading" AI products according to how potentially harmful they might be and staggering regulation accordingly. So for example an email spam filter would be more lightly regulated than something designed to diagnose a medical conditions - and some AI uses, such as social grading by governments, would be prohibited altogether.

Fox News contributor Douglas Murray joined'Fox & Friends' to discuss why Musk and other experts are calling for a halt to artificial intelligence systems for six months. The top Republican on the Senate Artificial Intelligence Caucus warned Wednesday that pausing the development of AI technology could raise "national security" concerns on the same day that top tech industry giants called for a pause. In an open letter earlier in the day, tech industry giants like Tesla founder Elon Musk and Apple co-founder Steve Wozniak called on AI labs "to immediately pause for at least 6 months the training of AI systems" more advanced than the latest chatbot known as GPT-4. But Sen. Mike Rounds, R-S.D., who leads the Senate AI caucus, disagreed. "Unless China, the Communist Party in China, is prepared to show evidence that they're going to do the same thing, I'm afraid then that we would be restricting our ability to move forward with AI for a period of six months while China does not," Rounds told Fox News Digital.

'The Five' co-hosts weigh in on the creator of ChatGPT raising'major concerns' regarding the implications of how artificial intelligence could change society. Elon Musk's proposed temporary halt in AI development would give China the freedom to surpass the U.S. and develop "the most powerful tool" in the 21st century, according to an industry expert. "We need artificial intelligence to automate and manage so much of the existing technology, as well as open the ability for us to manage a significantly larger population," Sultan Meghji, a professor at Duke University's Pratt Engineering School, told Fox News Digital. "We will not be able to do that without artificial intelligence." "As we consider our global competition with the People's Republic of China and others," Meghji, who served as the first chief innovation officer for the FDIC, argued, "artificial intelligence is the most powerful tool in our toolbox, and I don't want to lose the 21st century to the Chinese."