Instant runoff voting (IRV) has recently gained popularity as an alternative to plurality voting for political elections, with advocates claiming a range of advantages, including that it produces more moderate winners than plurality and could thus help address polarization. However, there is little theoretical backing for this claim, with existing evidence focused on case studies and simulations. In this work, we prove that IRV has a moderating effect relative to plurality voting in a precise sense, developed in a 1-dimensional Euclidean model of voter preferences. We develop a theory of exclusion zones, derived from properties of the voter distribution, which serve to show how moderate and extreme candidates interact during IRV vote tabulation. The theory allows us to prove that if voters are symmetrically distributed and not too concentrated at the extremes, IRV cannot elect an extreme candidate over a moderate. In contrast, we show plurality can and validate our results computationally. Our methods provide new frameworks for the analysis of voting systems, deriving exact winner distributions geometrically and establishing a connection between plurality voting and stick-breaking processes.

Social media companies relying on artificial intelligence software to moderate their platforms are generating unviable reports on cases of child sexual abuse, preventing US police from seeing potential leads and delaying investigations of alleged predators, the Guardian can reveal. By law, US-based social media companies are required to report any child sexual abuse material detected on their platforms to the National Center for Missing & Exploited Children (NCMEC). NCMEC acts as a nationwide clearinghouse for leads about child abuse, which it forwards to the relevant law enforcement departments in the US and around the world. The organization said in its annual report that it received more than 32m reports of suspected child sexual exploitation from companies and the public in 2022, roughly 88m images, videos and other files. Meta is the largest reporter of these tips, with more than 27m, or 84%, generated by its Facebook, Instagram and WhatsApp platforms in 2022.

AI tools like ChatGPT can draft letters, tell jokes and even give legal advice – but only in the form of computerized text. Now, scientists have created an AI that can imitate human handwriting, which could herald fresh issues regarding fraud and fake documents. Amazingly, the results are almost indistinguishable from the real thing drafted by human hands. Below is one column of writing by the team's AI model and another by humans, but can you tell which is which? Scroll down to reveal the answer!

For the better part of 17 years, the roiling, rolling, fractious, sometimes funny, sometimes horrifying, never-ever-ending global conversation had a central home: Twitter. If you wanted to know what was happening and what people were talking about right now, it was the only game in town. But then Elon Musk purchased Twitter, renamed it X, fired most of its employees, and more or less eliminated its moderation and verification systems. Many people have begun casting about for a replacement service--ideally one that is beyond any individual's control. The dream of a decentralized Twitter-like service has been around for years.

Some of those people are policymakers, who have been trying hard to respond to the problems AI products pose without reducing our ability to harness their power. So at the beginning of this year, my colleagues and I looked around the world for signs of how AI regulations are likely to change this year. We summarized what we found here. In China, one of the major moves to be on the lookout for in 2024 is whether the country will follow in the European Union's footsteps and announce its own comprehensive AI Act. In June of last year, China's top governing body released a list of legislation they were working on.

When users open an Incognito browser on Chrome, they'll see a notification warning them that other people using their device won't be able to see their activity but that their downloads, bookmarks and reading items will still be saved. Now, Google has updated that disclaimer in Chrome's experimental Canary channel, shortly after agreeing to settle a 5 billion lawsuit accusing it of tracking Incognito users. As first noticed by MSPowerUser, the company has tweaked the disclaimer in Canary to add language that says Incognito mode won't change how websites collect people's data. "Others who use this device won't see your activity, so you can browse more privately," the new disclaimer reads. "This won't change how data is collected by websites you visit and the services they use, including Google. Downloads, bookmarks and reading list items will be saved."

With growing concerns surrounding privacy and regulatory compliance, the concept of machine unlearning has gained prominence, aiming to selectively forget or erase specific learned information from a trained model. In response to this critical need, we introduce a novel approach called Attack-and-Reset for Unlearning (ARU). This algorithm leverages meticulously crafted adversarial noise to generate a parameter mask, effectively resetting certain parameters and rendering them unlearnable. ARU outperforms current state-of-the-art results on two facial machine-unlearning benchmark datasets, MUFAC and MUCAC. In particular, we present the steps involved in attacking and masking that strategically filter and re-initialize network parameters biased towards the forget set. Our work represents a significant advancement in rendering data unexploitable to deep learning models through parameter re-initialization, achieved by harnessing adversarial noise to craft a mask.

Neural style transfer (NST) is widely adopted in computer vision to generate new images with arbitrary styles. This process leverages neural networks to merge aesthetic elements of a style image with the structural aspects of a content image into a harmoniously integrated visual result. However, unauthorized NST can exploit artwork. Such misuse raises socio-technical concerns regarding artists' rights and motivates the development of technical approaches for the proactive protection of original creations. Adversarial attack is a concept primarily explored in machine learning security. Our work introduces this technique to protect artists' intellectual property. In this paper Locally Adaptive Adversarial Color Attack (LAACA), a method for altering images in a manner imperceptible to the human eyes but disruptive to NST. Specifically, we design perturbations targeting image areas rich in high-frequency content, generated by disrupting intermediate features. Our experiments and user study confirm that by attacking NST using the proposed method results in visually worse neural style transfer, thus making it an effective solution for visual artwork protection.

This paper introduces ClimateGPT, a model family of domain-specific large language models that synthesize interdisciplinary research on climate change. We trained two 7B models from scratch on a science-oriented dataset of 300B tokens. For the first model, the 4.2B domain-specific tokens were included during pre-training and the second was adapted to the climate domain after pre-training. Additionally, ClimateGPT-7B, 13B and 70B are continuously pre-trained from Llama~2 on a domain-specific dataset of 4.2B tokens. Each model is instruction fine-tuned on a high-quality and human-generated domain-specific dataset that has been created in close cooperation with climate scientists. To reduce the number of hallucinations, we optimize the model for retrieval augmentation and propose a hierarchical retrieval strategy. To increase the accessibility of our model to non-English speakers, we propose to make use of cascaded machine translation and show that this approach can perform comparably to natively multilingual models while being easier to scale to a large number of languages. Further, to address the intrinsic interdisciplinary aspect of climate change we consider different research perspectives. Therefore, the model can produce in-depth answers focusing on different perspectives in addition to an overall answer. We propose a suite of automatic climate-specific benchmarks to evaluate LLMs. On these benchmarks, ClimateGPT-7B performs on par with the ten times larger Llama-2-70B Chat model while not degrading results on general domain benchmarks. Our human evaluation confirms the trends we saw in our benchmarks. All models were trained and evaluated using renewable energy and are released publicly.

Land Use Land Cover (LULC) is a multidisciplinary field that categorizes and characterizes the earth's terrestrial surface. It encompasses various types of ground, from natural landscapes such as forests, wetlands, and deserts to human-altered environments such as agricultural fields, urban areas, and industrial sites. LULC studies provide a snapshot of the earth's surface at a given time, offering valuable insights into the spatial distribution and interaction of various land use types and land cover classes. The dynamic nature of LULC, driven by both natural processes and human activities, necessitates continuous monitoring and analysis to capture temporal changes. The importance of LULC studies extends to numerous fields. In environmental science, LULC data inform our understanding of biodiversity, ecosystem services, and the impacts of climate change. In urban planning and development, it helps to manage land resources, assess environmental impacts, and guide sustainable practices. LULC helps optimize land use for crop production in agriculture while minimizing environmental degradation. In addition, LULC data are integral to policy-making, supporting land conservation, urban growth, and climate change mitigation decisions.