It was an amazing year for games. While there were no new consoles,we did get new VR headsets and a wave of new handheld PCs offered even more options for playing games on the go (or at least on the couch). That's reflected in many of our picks for best games of the year, with several PC-only choices. The year kicked off with a fantastic remake of space horror Dead Space and the breakout success, Pizza Tower. But there were so many more.

For the first time in four centuries, it's good to be a beaver. Long persecuted for their pelts and reviled as pests, the dam-building rodents are today hailed by scientists as ecological saviors. Their ponds and wetlands store water in the face of drought, filter out pollutants, furnish habitat for endangered species, and fight wildfires. In California, Castor canadensis is so prized that the state recently committed millions to its restoration. While beavers' benefits are indisputable, however, our knowledge remains riddled with gaps.

Jodi Long was caught off guard by the cage filled with cameras meant to capture images of her face and body. "I was a little freaked out because, before I walked in there, I said I don't remember this being in my contract," the actor said. The filmmakers needed her digital scan, Long was told, because they wanted to make sure her arms were positioned correctly in a scene where she holds a computer-generated character. That moment in 2020 stuck with Long, president of SAG-AFTRA's Los Angeles local, while she was negotiating for protections around the use of artificial intelligence when actors went on strike. In November, the actors guild reached a deal with Hollywood studios that -- among other things -- required consent and compensation for the use of a worker's digital replica.

The New York Times has sued Microsoft and OpenAI for using its content to help develop artificial intelligence services, in a sign of the increasingly fraught relationship between the media and a technology that could upend the news industry. The Times didn't specify its monetary demands. OpenAI has faced criticism for scraping text widely from the web to train its popular chatbot since it debuted a year ago. While it has been sued by prominent authors, this is the first challenge to its practices by a major media organization. The startup has sought licensing deals with publishers, much like Alphabet's Google and Meta Platforms' Facebook have done in recent years.

Australia's privacy watchdog has launched an inquiry into how TikTok harvests personal data and whether it is being done with consent. The Office of the Australian Information Commissioner (OAIC) will examine whether the social media platform has breached the online privacy of Australians through the use of marketing pixels, which track people's online habits. This can include where they shop, how long they stay on websites and personal information, such as email addresses and mobile phone numbers. Liberal senator James Paterson, who has been campaigning against TikTok and its parent company, ByteDance, has alleged the social media platform is using pixels to collect information of non-TikTok users. "This conduct would be unacceptable from any company but is particularly alarming given TikTok is beholden to the Chinese Communist party and is required under China's intelligence laws to share information with Chinese government intelligence agencies," Paterson said.

The New York Times has sued OpenAI and Microsoft over the use of its content to train generative artificial intelligence and large-language model systems, a move that could see the company receive billions of dollars in damages. The lawsuit contains an appeal to the "vital" importance of the Times's independent journalism to democracy, arguing that it is "increasingly rare and valuable". The publisher's lawsuit is the latest in a string of similar cases, including one brought by more than a dozen authors in September targeting the company for its use of their writing. Language learning models have faced increasing scrutiny since they exploded in popularity in the past year, with news outlets in particular concerned that the tools will spread misinformation attributed to them and utilize their content with no incentive to click through to the original source. ChatGPT launched in November 2022 and amassed 100 million users in just two months.

$ $The usage of generative artificial intelligence (AI) tools based on large language models, including ChatGPT, Bard, and Claude, for text generation has many exciting applications with the potential for phenomenal productivity gains. One issue is authorship attribution when using AI tools. This is especially important in an academic setting where the inappropriate use of generative AI tools may hinder student learning or stifle research by creating a large amount of automatically generated derivative work. Existing plagiarism detection systems can trace the source of submitted text but are not yet equipped with methods to accurately detect AI-generated text. This paper introduces the idea of direct origin detection and evaluates whether generative AI systems can recognize their output and distinguish it from human-written texts. We argue why current transformer-based models may be able to self-detect their own generated text and perform a small empirical study using zero-shot learning to investigate if that is the case. Results reveal varying capabilities of AI systems to identify their generated text. Google's Bard model exhibits the largest capability of self-detection with an accuracy of 94\%, followed by OpenAI's ChatGPT with 83\%. On the other hand, Anthropic's Claude model seems to be not able to self-detect.

Nature is an amorphous concept. Yet, it is essential for the planet's well-being to understand how the economy interacts with it. To address the growing demand for information on corporate nature disclosure, we provide datasets and classifiers to detect nature communication by companies. We ground our approach in the guidelines of the Taskforce on Nature-related Financial Disclosures (TNFD). Particularly, we focus on the specific dimensions of water, forest, and biodiversity. For each dimension, we create an expert-annotated dataset with 2,200 text samples and train classifier models. Furthermore, we show that nature communication is more prevalent in hotspot areas and directly effected industries like agriculture and utilities. Our approach is the first to respond to calls to assess corporate nature communication on a large scale.

High-quality, large-scale corpora are the cornerstone of building foundation models. In this work, we introduce \textsc{MathPile}, a diverse and high-quality math-centric corpus comprising about 9.5 billion tokens. Throughout its creation, we adhered to the principle of ``\emph{less is more}'', firmly believing in the supremacy of data quality over quantity, even in the pre-training phase. Our meticulous data collection and processing efforts included a complex suite of preprocessing, prefiltering, language identification, cleaning, filtering, and deduplication, ensuring the high quality of our corpus. Furthermore, we performed data contamination detection on downstream benchmark test sets to eliminate duplicates. We hope our \textsc{MathPile} can help to enhance the mathematical reasoning abilities of language models. We plan to open-source different versions of \mathpile with the scripts used for processing, to facilitate future developments in this field.

Every major technical invention resurfaces the dual-use dilemma -- the new technology has the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, such as large language models (LLMs) and diffusion models, have shown remarkable capabilities (e.g., in-context learning, code-completion, and text-to-image generation and editing). However, GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks. This paper reports the findings of a workshop held at Google (co-organized by Stanford University and the University of Wisconsin-Madison) on the dual-use dilemma posed by GenAI. This paper is not meant to be comprehensive, but is rather an attempt to synthesize some of the interesting findings from the workshop. We discuss short-term and long-term goals for the community on this topic. We hope this paper provides both a launching point for a discussion on this important topic as well as interesting problems that the research community can work to address.