Inception42, an Abu Dhabi-based technology entity, is deploying AI agents across various departments within the UAE government. These AI agents are being installed to enhance operational efficiency and automate administrative tasks within public sector operations. This initiative signifies a strategic move towards integrating advanced artificial intelligence solutions into governmental workflows throughout the Emirates. Why it matters: This large-scale deployment by a local entity underscores the UAE's commitment to digital transformation and its proactive adoption of AI to modernize public services and governance.
G42, an Abu Dhabi-based technology group, and Banco Santander, a global banking group, have signed a Memorandum of Understanding to establish a framework for strategic cooperation in artificial intelligence. The partnership aims to co-develop AI-enabled advisory and savings solutions for banking customers, as well as a banking intelligence layer spanning Santander’s global operations. G42 companies Inception (agentic AI) and Presight (applied AI) are expected to play central roles in this collaboration. Why it matters: This significant partnership between a leading UAE AI entity and a global bank highlights the growing integration of advanced AI, including sovereign AI principles, into core financial services and could set a precedent for large-scale AI adoption in the global banking sector.
Inception, a G42 company, has launched InceptionClaw, an enterprise-grade AI super assistant designed for enterprise leaders and government officials. Built on Inception's Catalyst platform and powered by Compass models, InceptionClaw actively manages workloads by monitoring calendars and emails, delivering structured briefs, alerts, and audio summaries. It operates with UAE-native sovereignty, ensuring all data remains within UAE jurisdiction under Greenshield sovereign controls, addressing critical data residency and trust requirements. The assistant also includes features like tamper-proof audit trails, code-reviewed skills, spending limits, and human approval queues for high-stakes actions.
The paper introduces MedNNS, a neural network search framework designed for medical imaging, addressing challenges in architecture selection and weight initialization. MedNNS constructs a meta-space encoding datasets and models based on their performance using a Supernetwork-based approach, expanding the model zoo size by 51x. The framework incorporates rank loss and Fréchet Inception Distance (FID) loss to capture inter-model and inter-dataset relationships, improving alignment in the meta-space and outperforming ImageNet pre-trained DL models and SOTA NAS methods.
Inception, Cerebras, and MBZUAI have released Jais 2, a 70 billion parameter open-weight Arabic LLM. Jais 2 is trained on an Arabic-first dataset and features a redesigned architecture for stronger reasoning and fluency across Arabic dialects and English. It integrates a safety-first framework and demonstrates capabilities in understanding Arabic poetry, culture, and social media tone. Why it matters: Jais 2 addresses the historical underrepresentation of Arabic in AI by providing a culturally and linguistically faithful model, potentially accelerating innovation across the region.
This article discusses MBZUAI's efforts in advancing Arabic language AI, including the development of advanced linguistic models using deep learning techniques. Key initiatives include Jais, a 13B parameter Arabic LLM developed in collaboration with G42's Inception, and Atlas-Chat, which understands the Moroccan dialect. The university is also incorporating Arabic in practical AI solutions like BiMediX2, a healthcare multi-modal model that understands medical queries in both English and Arabic. Why it matters: These initiatives are crucial for preserving Arabic cultural heritage, enabling future discovery, and addressing linguistic challenges specific to the Arabic language in AI applications.
G42's Inception has open-sourced Jais, a 13-billion parameter Arabic large language model (LLM). Jais was trained on a 395-billion-token Arabic and English dataset and outperforms existing Arabic models. The model is a collaboration between Inception, MBZUAI, and Cerebras Systems, and was trained on the Condor Galaxy supercomputer. Why it matters: This release establishes a new standard for Arabic language AI, providing over 400 million Arabic speakers access to generative AI and fostering innovation in the region.
G42 has launched Nanda 87B, an open-source Hindi-English LLM developed by MBZUAI in collaboration with Inception and Cerebras. Nanda 87B is built upon Llama-3.1 70B and trained on a dataset with over 65 billion Hindi tokens. The model is engineered for real-world use being fluent in formal Hindi, casual speech, and Hinglish, and is designed for translation, summarization, instruction-following, and transliteration tasks. Why it matters: This release marks a major advancement in creating inclusive AI technology tailored for one of the world's largest linguistic communities.