Many computational argumentation tasks, like stance classification, are topic-dependent: the effectiveness of approaches to these tasks significantly depends on whether the approaches were trained on arguments from the same topics as those they are tested on. So, which are these topics that researchers train approaches on? This paper contributes the first comprehensive survey of topic coverage, assessing 45 argument corpora. For the assessment, we take the first step towards building an argument topic ontology, consulting three diverse authoritative sources: the World Economic Forum, the Wikipedia list of controversial topics, and Debatepedia. Comparing the topic sets between the authoritative sources and corpora, our analysis shows that the corpora topics-which are mostly those frequently discussed in public online fora - are covered well by the sources. However, other topics from the sources are less extensively covered by the corpora of today, revealing interesting future directions for corpus construction.

Most Transformer language models are primarily pretrained on English text, limiting their use for other languages. As the model sizes grow, the performance gap between English and other languages with fewer compute and data resources increases even further. Consequently, more resource-efficient training methods are needed to bridge the gap for languages with fewer resources available. To address this problem, we introduce a cross-lingual and progressive transfer learning approach, called CLP-Transfer, that transfers models from a source language, for which pretrained models are publicly available, like English, to a new target language. As opposed to prior work, which focused on the cross-lingual transfer between two languages, we extend the transfer to the model size. Given a pretrained model in a source language, we aim for a same-sized model in a target language. Instead of training a model from scratch, we exploit a smaller model that is in the target language but requires much fewer resources. Both small and source models are then used to initialize the token embeddings of the larger model based on the overlapping vocabulary of the source and target language. All remaining weights are reused from the model in the source language. This approach outperforms the sole cross-lingual transfer and can save up to 80% of the training steps compared to the random initialization.

Getty is bringing its intellectual property rights infringement complaint to London's High Court of Justice. Stability AI already faces a separate major legal battle begun this week when a group of artists filed a class action lawsuit in California against it, along with Stable Diffusion platforms Midjourney and DeviantArt. Some of the billions of pictures in the LAION-5B dataset employed to train Stable Diffusion may have been scraped from the web, including Getty's servers, without their creators' awareness. Notably, Stability AI has suggested there will be an opt-out option for any artist whose work might be used to train new iterations of Stable Diffusion. Getty hasn't mentioned any financial compensation or desire to shut down Stable Diffusion in its case.

Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.

Depending on who you ask, generative AI is either massively overhyped or undervalued. Defined as algorithm-driven tech that creates text, art and other forms of media given a prompt, it's captured the attention of major VC backers who've piled hundreds of millions of dollars into firms like Jasper and Stability AI. But generative AI has yet to generate (no pun intended) correspondingly high returns, casting doubt on its near-term profit-making potential. Emmanuel de Maistre and Hervé Nivon think the problem is the application of the tech rather than the tech itself. While startups such as Stability AI aim to tackle a broad number of use cases with their generative AI, de Maistre and Nivon advocate for a narrower, slightly more focused approach.

Getty Images claims that Stability AI, the creator of Stable Diffusion, used images obtained from Getty Images to train their algorithms without obtaining proper licensing. This definition applies to photographs, art, and images that the artists allege have been infringed. First, let's discuss how the above-mentioned artificial intelligence models work. In general, deep neural networks and machine learning models are trained in a method that has its similarities to humans learning. Programmers do not instruct the algorithm to specifically do what it does, or in this case, do not get the algorithms to copy specific elements from original pictures when constructing a new image.

Businesses across industries are increasingly deploying artificial intelligence (AI) to analyze user preferences and personalize user experiences, boost productivity, and fight fraud. Financial services are using AI to hedge financial risks, while the healthcare industry is leveraging AI to improve diagnosis and treatments as well as to reduce the time for development of vaccines. Marketplaces, eCommerce, retail, travel, gaming, entertainment, social media and many other sectors are all using AI in some way or the other. This growing use of AI has already transformed the way the global economy works and how businesses interact with their consumers. However, in some cases it is also beginning to infringe on people's privacy. As a result, there is a growing debate about how much AI is too much.

Key trend #1 -- Migration to the cloud will accelerate, although the rising scale of cloud costs will become increasingly scrutinized by finance types as the economy tightens. Organizations will struggle with hybrid and multi-cloud architectures. Watch for new and innovative pricing schema to help combat the rising costs (e.g., fee caps and fixed fee). The flexibility and scalability the cloud offers to organizations. Cloud computing allows companies to easily add or remove resources as needed, without having to invest in expensive hardware and infrastructure.

OPPO is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. The US base salary range for this full-time position is $30-$60/hour. Our salary ranges are determined by role, level, and location.

How long will the naked eye be able to spot the difference between images made by generative artificial intelligence and art created by humans? Ari Melenciano, an artist who works at Google's Creative Lab, squints at her computer screen during our Zoom chat and scans artwork created with generative AI. "I mean, I can barely tell the difference now," she says. The public release of AI art tools, like Midjourney and DALL-E 2, has ignited contentious debates among artists, designers, and art fans alike. Many are critical of the fact that the technology's rapid progress was fueled by scraping the internet for publicly posted art and imagery, without credit or compensation to the artists who had their work stolen. "I think the current model of AI art generators is unethical, because of how they collected their data--against the knowledge of, basically, everybody involved," says Jared Krichevsky, a concept artist who designed the memeable AI-bot for the M3GAN movie.