This paper presents a new AI challenge, the Tales of Tribute AI Competition (TOTAIC), based on a two-player deck-building card game released with the High Isle chapter of The Elder Scrolls Online. Currently, there is no other AI competition covering Collectible Card Games (CCG) genre, and there has never been one that targets a deck-building game. Thus, apart from usual CCG-related obstacles to overcome, like randomness, hidden information, and large branching factor, the successful approach additionally requires long-term planning and versatility. The game can be tackled with multiple approaches, including classic adversarial search, single-player planning, and Neural Networks-based algorithms. This paper introduces the competition framework, describes the rules of the game, and presents the results of a tournament between sample AI agents. The first edition of TOTAIC is hosted at the IEEE Conference on Games 2023.

Identifying and understanding implicit attitudes toward food can help efforts to mitigate social prejudice due to food's pervasive role as a marker of cultural and ethnic identity. Stereotypes about food are a form of microaggression that contribute to harmful public discourse that may in turn perpetuate prejudice toward ethnic groups and negatively impact economic outcomes for restaurants. Through careful linguistic analyses, we evaluate social theories about attitudes toward immigrant cuisine in a large-scale study of framing differences in 2.1M English language Yelp reviews of restaurants in 14 US states. Controlling for factors such as restaurant price and neighborhood racial diversity, we find that immigrant cuisines are more likely to be framed in objectifying and othering terms of authenticity (e.g., authentic, traditional), exoticism (e.g., exotic, different), and prototypicality (e.g., typical, usual), but that non-Western immigrant cuisines (e.g., Indian, Mexican) receive more othering than European cuisines (e.g., French, Italian). We further find that non-Western immigrant cuisines are framed less positively and as lower status, being evaluated in terms of affordability and hygiene. Finally, we show that reviews generated by large language models (LLMs) reproduce many of the same framing tendencies. Our results empirically corroborate social theories of taste and gastronomic stereotyping, and reveal linguistic processes by which such attitudes are reified.

The composition of the academic workforce thus shapes what advances are made and who benefits from them,20,21 in part because demographic diversity in science is known to accelerate innovation and improve problem solving.17,31 Despite a continued emphasis on broadening participation, women faculty in the U.S. remain underrepresented relative to women's share of the U.S. population by more than a factor of two, and Black, Hispanic, and Native faculty by more than a factor of five.37,40 Women's underrepresentation among computing researchers also persists internationally. For example, women are estimated to comprise less than 10% of contributors to international computer science journals.25 On one hand, there are generational problems, in which faculty diversity changes slowly because it takes many years for diversity increases at the earliest stages of training to propagate up to more senior levels.16 On the other hand, there are structural and social climate problems in the U.S.,1 in which members of underrepresented groups who aspire to or have a faculty career are pushed or pulled out of the community, which may counteract efforts to address generational problems. In concert, these two effects may lead to a persistent overrepresentation of majority groups5 despite efforts to the contrary. We consider a third class of problem, which exists because most faculty are hired via searches that focus on a particular subfield of computing--for example, artificial intelligence (AI). As a result, field-level demographic dynamics such as gender, racial, and socioeconomic representation are in fact driven by diversity differences across computing's subfields and the representation of those subfields among the suppliers of future faculty.8 For example, faculty searches in subfields with fewer women than other subfields are less likely to increase a department's gender diversity.

Science is a catalyst for human progress. But a publishing monopoly and funding monopsony have inhibited research. We intend to improve incentives in science by developing smart research contracts. These will collectively reward scientific activities, including proposals, papers, replications, datasets, analyses, annotations, editorials, and more. Peer-to-peer review networks will be designed to help evaluate proposals and publications. Long term, these smart contracts help accelerate research by minimizing science friction, ensuring science quality, and maximizing science variance. Email bits@atoms.org or follow @atoms_org to help us build a flourishing research economy. Papers are the fundamental asset of the research economy: they serve as proof of work that valuable research has been completed. Funding agencies and research institutions evaluate scientists based on their publications. Principal investigators (PIs) attract prospective students and collaborators via papers. Investors and companies use scientific literature to conduct due diligence on research ranging from basic discoveries to clinical studies. Thus, the evaluation and dissemination of papers are vital to this research economy. Publishers are the sole arbiters of papers today. They assign a value -- denominated in "prestige" -- by accepting a paper into the appropriate journal based on selectivity and domain. To evaluate papers, journals typically outsource it to two or three PIs, who often outsource it further to their students. Reviewers are unpaid for this peer review work, as it is an expected part of their scientific duties. Peer review is believed to be necessary because of the industrialization of science. Research papers and proposals have become too specialized and too numerous, making it difficult to assess merit prima facie. As a result, scientific incentives have become distorted in two major ways: prestige capture and reviewer misalignment. Over half of all research papers in 2013 were published by five companies, who have used their centuries of brand equity to build an economic moat. This results in prestige capture, which akin to regulatory capture, causes public and scientific interest to be directed towards the regulators of prestige.

The future of museums is virtual. Moving collections online and creating virtual environments for patrons to experience a museum's offerings exponentially expands the levels of impact and reach a museum can have. Providing digital experiences of museum collections is a necessity for museums that want to build impact, prestige, and revenue. In fact, 98% of museums agree that their highest investment priorities include online platforms and digitalizing their collections [1]. It is clear that museums see the value of digital transformation; however, 69% of museums claimed to have a digital strategy [2], but only 23% of museums have digitalized parts of their collection [3].

While CES was a bit different this year, we still managed to check out a number of inspiring new devices, apps and services. While we acknowledged the most promising tech in Engadget's Best of CES awards, there remain a bunch of gadgets that didn't make the list that will be worth checking out when they actually hit shelves later this year. Things like HP's new Dragonfly laptops, TCL's 8K TVs and Cowin's two-piece soundbar are all things to look forward to as 2021 progresses (and some are even available already). Here are some of the CES 2021 gadgets you may have missed this week. Cherlynn Low found a lot to like about last year's HP Dragonfly laptop: it was lightweight at 2.2 pounds, had a great battery life and an attractive design.

Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard in causal inference is performing randomized controlled trials; unfortunately these are not always feasible due to ethical, legal, or cost constraints. As an alternative, methodologies for causal inference from observational data have been developed in statistical studies and social sciences. However, existing methods critically rely on restrictive assumptions such as the study population consisting of homogeneous elements that can be represented in a single flat table, where each row is referred to as a unit. In contrast, in many real-world settings, the study domain naturally consists of heterogeneous elements with complex relational structure, where the data is naturally represented in multiple related tables. In this paper, we present a formal framework for causal inference from such relational data. We propose a declarative language called CaRL for capturing causal background knowledge and assumptions and specifying causal queries using simple Datalog-like rules. CaRL provides a foundation for inferring causality and reasoning about the effect of complex interventions in relational domains. We present an extensive experimental evaluation on real relational data to illustrate the applicability of CaRL in social sciences and healthcare.

Note from the editor of this site: In the United States, there are over 700 colleges with less than 3K (3000) students. Many of these colleges are totally overlooked in the college search process. This website is devoted to these wonderful small colleges that totally transform the lives of their graduates. I recently retired from 42 years in the College Admission profession. All 42 years were at small, private, colleges.

Note from the editor of this site: In the United States, there are over 700 colleges with less than 3K (3000) students. Many of these colleges are totally overlooked in the college search process. This website is devoted to these wonderful small colleges that totally transform the lives of their graduates. I recently retired from 42 years in the College Admission profession. All 42 years were at small, private, colleges.

It was with a strangely deflated feeling in his gut that Harvard biologist Mohammed AlQuraishi made his way to Cancun for a scientific conference in December. Strange because a major advance had just been made in his field, something that might normally make him happy. Deflated because the advance hadn't been made by him or by any of his fellow academic researchers. It had been made by a machine. DeepMind, an AI company that Google bought in 2014, had outperformed all the researchers who'd submitted entries to the Critical Assessment of Structure Prediction (CASP) conference, which is basically a fancy science contest for grown-ups. Every two years, researchers working on one of the biggest puzzles in biochemistry, known as the protein folding problem, try to prove how good their predictive powers are by submitting a prediction about the 3D shapes that certain proteins will take.