TikTok Is Now Collecting Even More Data About Its Users. According to its new privacy policy, TikTok now collects more data on its users, including their precise location, after majority ownership officially switched to a group based in the US. When TikTok users in the US opened the app today, they were greeted with a pop-up asking them to agree to the social media platform's new terms of service and privacy policy before they could resume scrolling. These changes are part of TikTok's transition to new ownership. In order to continue operating in the US, TikTok was compelled by the US government to transition from Chinese control to a new, American-majority corporate entity.

Current Multimodal Large Language Models exhibit very strong performance for several demanding tasks. While commercial MLLMs deliver acceptable performance in low-resource languages, comparable results remain unattained within the open science community. In this paper, we aim to develop a strong MLLM for a low-resource language, namely Basque. For that purpose, we develop our own training and evaluation image-text datasets. Using two different Large Language Models as backbones, the Llama-3.1-Instruct model and a Basque-adapted variant called Latxa, we explore several data mixtures for training. We show that: i) low ratios of Basque multimodal data (around 20%) are already enough to obtain solid results on Basque benchmarks, and ii) contrary to expected, a Basque instructed backbone LLM is not required to obtain a strong MLLM in Basque. Our results pave the way to develop MLLMs for other low-resource languages by openly releasing our resources.

The popular filesharing service WeTransfer has said user content will not be used to train artificial intelligence after a change in its service terms had triggered a public backlash. The company, which is regularly used by creative professionals to transfer their work online, had suggested in new terms that uploaded files could be used to "improve machine learning models". The clause had previously said the service had a right to "reproduce, modify, distribute and publicly display" content, and the updated version caused confusion among users. A WeTransfer spokesperson said user content had never been used, even internally, to test or develop AI models and that "no specific kind of AI" was being considered for use by the Dutch company. The firm said: "There's no change in how WeTransfer handles your content in practice."

Large language models (LLMs) are highly capable but face latency challenges in real-time applications, such as conducting online hallucination detection. To overcome this issue, we propose a novel framework that leverages a small language model (SLM) classifier for initial detection, followed by a LLM as constrained reasoner to generate detailed explanations for detected hallucinated content. This study optimizes the real-time interpretable hallucination detection by introducing effective prompting techniques that align LLM-generated explanations with SLM decisions. Empirical experiment results demonstrate its effectiveness, thereby enhancing the overall user experience.

When users first found out about Adobe's new terms of service (which were quietly updated in February), there was an uproar. Adobe told users it could access their content "through both automated and manual methods" and use "techniques such as machine learning in order to improve [Adobe's] Services and Software." Many understood the update as the company forcing users to grant unlimited access to their work, for purposes of training Adobe's generative AI: Firefly. Late on Tuesday, Adobe issued a clarification: In an updated version of its terms of service agreement, it pledged not to train AI on its user content stored locally or in the cloud and gave users the option to opt-out of content analytics. Caught in the crossfire of intellectual property lawsuits, the ambiguous language used to previously update the terms shed light on a climate of acute skepticism among artists, many of whom over rely on Adobe for their work.

By uploading any User Content you hereby grant and will grant OpenAI and its affiliated companies a nonexclusive, worldwide, royalty free, fully paid up, transferable, sublicensable, perpetual, irrevocable license to copy, display, upload, perform, distribute, store, modify and otherwise use your User Content for any OpenAI-related purpose in any form, medium or technology now known or later developed.

Phil Libin, chief executive officer of Evernote Corp. Customers are furious about an update to the company's privacy policy. In a startling update to its privacy policy, note-taking app Evernote is allowing a select group of employees to read user content for the sake of improving its machine learning technology. What's perhaps more startling is that it appears Evernote employees have always been able to access user content, but somehow no one noticed. There's no clear way to opt out other than to quit the service entirely. On Twitter, Evernote users have been suitably outraged, calling the new privacy policy "disgraceful" and "hard to believe."

Facebook on Wednesday introduced DeepText, an artificial intelligence-fueled text analytics engine. "Text is a prevalent form of communication on Facebook," wrote Facebook software engineers Ahmad Abdulkader, Aparna Lakshmiratan and Joy Zhang in a post describing its capabilities. "Understanding the various ways text is used on Facebook can help us improve people's experiences with our products," they continued, "whether we're surfacing more of the content that people want to see or filtering out undesirable content like spam." DeepText can understand with near-human accuracy the textual content of several thousands posts per second, spanning more than 20 languages. The engine leverages several deep neural network architectures and can perform word-level and character-level based learning, noted Abdulkader, Lakshmiratan and Zhang.