Writing comprehensive and accurate descriptions of technical drawings in patent documents is crucial to effective knowledge sharing and enabling the replication and protection of intellectual property. However, automation of this task has been largely overlooked by the research community. To this end, we introduce PatentDesc-355K, a novel large-scale dataset containing ~355K patent figures along with their brief and detailed textual descriptions extracted from more than 60K US patent documents. In addition, we propose PatentLMM - a novel multimodal large language model specifically tailored to generate high-quality descriptions of patent figures. Our proposed PatentLMM comprises two key components: (i) PatentMME, a specialized multimodal vision encoder that captures the unique structural elements of patent figures, and (ii) PatentLLaMA, a domain-adapted version of LLaMA fine-tuned on a large collection of patents. Extensive experiments demonstrate that training a vision encoder specifically designed for patent figures significantly boosts the performance, generating coherent descriptions compared to fine-tuning similar-sized off-the-shelf multimodal models. PatentDesc-355K and PatentLMM pave the way for automating the understanding of patent figures, enabling efficient knowledge sharing and faster drafting of patent documents. We make the code and data publicly available.

ocial relationships in the digital sphere are becoming more usual and frequent, and they constitute a very important aspect for all of us. Violent interactions in this sphere are very frequent, and have serious effects on the victims. Within this global scenario, there is one kind of digital violence that is becoming really worrying: sexism against women. Sexist comments that are publicly posted in social media (newspaper comments, social networks, etc.), usually obtain a lot of attention and become viral, with consequent damage to the persons involved. In this paper, we introduce an anti-sexism alert system, based on natural language processing (NLP) and artificial intelligence (AI), that analyzes any public post, and decides if it could be considered a sexist comment or not. Additionally, this system also works on analyzing all the public comments linked to any multimedia content (piece of news, video, tweet, etc.) and decides, using a color-based system similar to traffic lights, if there is sexism in the global set of posts. We have created a labeled data set in Spanish, since the majority of studies focus on English, to train our system, which offers a very good performance after the validation experiments.ocial Violent interactions in this sphere are very frequent, and have serious effects on the victims. Within this global scenario, there is one kind of digital violence that is becoming really worrying: sexism against women.

With the advent of big data, it has become more difficult for a human records manager to ensure they classify all information for retention. There are finally ways to deal with the massive scale of unstructured data through the application of modern machine learning, and artificial intelligence (AI) approaches. In this article, we will cover the types of machine learning that are useful for records management. The conventional approach is to have users categorize records as they create them. But, this approach does not scale with the volume of data now under management. The extra workload is disruptive to users who are trying to do their real jobs.

A few weeks ago, Jochen Leidner, Director of Research at Thomson Reuters, discussed and dissected artificial intelligence (AI). Leidner offered some case studies, conducted by his team, to demonstrate successful ways of building AI products. However, before getting into specifics, Leidner gave a very brief explanation of what AI is and what it does. "It automates things that we deemed, maybe, not automatable a couple of years ago – if it works, or to an extent." Dr. Leidner, who is also the Royal Academy of Engineering Visiting Professor of Data Analytics at the University of Sheffield, made the point very early on that AI is a tool to be utilized with other forms of intelligence.

UK ISPs will begin sending out emails to subscribers later this month, warning them of movie, TV and music piracy identified on their connection and pointing them to legal content sources. Providers confirmed to ISPreview the voluntary programme will kick off later this month, with Virgin Media offering more details on how it'll work. If the ISP can link the IP address with a subscriber account, it'll send an email expected to contain an itemisation of the media in question, as well as links to bona fide content sources and other educational, anti-piracy resources. Torrents are the main target here, since it's easy to identify IP addresses due to the peer-to-peer nature of the network (as long as there's no VPN at play). Tracking people that turn to other sources of illegal media, such as direct download sites, is more complex.