The training of topic models for a multilingual environment is a challenging task, requiring the use of sophisticated algorithms, topic-aligned corpora, and manual evaluation. These difficulties are further exacerbated when the developer lacks knowledge of the target language or is working in an environment with limited data, where only small or unusable multilingual corpora are available. Considering these challenges, we introduce Topic Model Translation (TMT), a novel, robust and transparent technique designed to transfer topic models (e.g., Latent Dirichlet Allocation (LDA) based topic models) from one language to another, without the need for metadata, embeddings, or aligned corpora. TMT enables the reuse of topic models across languages, making it especially suitable for scenarios where large corpora in the target language are unavailable or manual translation is infeasible. Furthermore, we evaluate TMT extensively using both quantitative and qualitative methods, demonstrating that it produces semantically coherent and consistent topic translations.

The capability to jointly process multi-modal information is becoming an essential task. However, the limited number of paired multi-modal data and the large computational requirements in multi-modal learning hinder the development. We propose a novel Tri-Modal Translation (TMT) model that translates between arbitrary modalities spanning speech, image, and text. We introduce a novel viewpoint, where we interpret different modalities as different languages, and treat multi-modal translation as a well-established machine translation problem. To this end, we tokenize speech and image data into discrete tokens, which provide a unified interface across modalities and significantly decrease the computational cost. In the proposed TMT, a multi-modal encoder-decoder conducts the core translation, whereas modality-specific processing is conducted only within the tokenization and detokenization stages. We evaluate the proposed TMT on all six modality translation tasks. TMT outperforms single model counterparts consistently, demonstrating that unifying tasks is beneficial not only for practicality but also for performance.

SINGAPORE-HEADQUARTERED UNO Digital Bank and Trusting Social, an artificial intelligence (AI) fintech company, recently announced a strategic partnership to unlock credit access to Filipino consumers. By offering a frictionless and friendly lending experience, the partnership aims to empower traditionally underserved citizens and bring them into the formal financial ecosystem. The strategic partnership would leverage Trusting Social's expertise in AI-led customer onboarding solutions, including credit scoring. UNO's new digital banking platform could help build a more financially inclusive banking landscape in the Philippines. Manish Bhai, chief executive, UNO Digital Bank discussed with The Manila Times how the partnership with Trusting Social would allow unbanked Filipinos access to formal lending services. The Manila Times (TMT): Please describe the business of UNO Digital Bank and its presence in the Philippines (if any).

Technology, media and telecoms (TMT) regulators will ponder rather than act on AI. Increased use of AI to generate deepfakes in the US presidential campaign may be the catalyst for substantive regulation. Debate regarding access to, and ownership of, data will continue with little regulatory change. For many industries, the focus will remain on operational efficiency. AI-based virtual assistants will gain significant traction.

The event's single objective was to accelerate healthcare's pragmatic adoption of new technologies by informing, inspiring, and debating, the role and value of technology innovation to health systems, healthcare workforce, and patients around the world. Click here to find unedited audio, presentations where available, sponsor page, and photos, with links to speaker photos, and the 2nd Annual Innovation Ignition Competition. Partners in Digital Health, publisher of Telehealth and Medicine Today (TMT) has announced a partnership with Orvium, the first decentralized social platform for scientific collaboration, funding, and publications management based on blockchain and artificial Intelligence. TMT will be the first journal to run on the new Orvium decentralized platform. Manuel Martin, CEO and Co-founder of Orvium, said, "This represents another significant milestone for our platform as we move into this next exciting phase of development. Having a major journal like'Telehealth and Medicine Today' as the first to go through our decentralized platform is a strong vote of confidence from a major publisher, and underlines the credentials of our platform."

The prosaically named Thirty Meter Telescope (TMT) project, a planned observatory to be built on Mauna Kea, the Big Island, in Hawaii, is huge in every way: a reported US 1.4 billion dollar budget, a giant mirror composed of 492 smaller mirror segments, and a goal of investigating not just the stars in our Milky Way but galaxies forming at the very edge of the observable universe. Though this project is backed by the governments of China, Japan, Canada, and India, as well as the United States, it may never be built. For its location is considered sacred by some Hawaiians, whose protests have been heard all the way to the State Supreme Court of Hawaii, which in December 2015 invalidated TMT's previously granted building permit. With the project suspended for over a year, involved scientist and construction companies can only keep their fingers crossed that the contested case will go their way. In the meantime, Mitsubishi Electric, which has developed the main structure of TMT, announced this week the completion of a prototype robot for a segmented-handling system (SHS) to install and replace the mirror segments.