Technology is the core driver of Ant's future creation.
At the 2025 Inclusion·Bund Conference forum, leading scholars and industry experts revealed how high‑quality data and AI form a dual‑engine that reshapes model training, improves performance, and drives the next evolution of intelligent systems.
The Inclusion·外滩大会 forum explored how diminishing returns from massive models demand a shift toward cognitive reasoning, autonomous evolution, multi‑agent coordination, reinforcement learning, high‑quality data, and MoE diffusion models to bridge digital AI with the physical world.
The article introduces the AI Adoption Maturity Model (AIM²), a five‑level framework covering strategy, organization, data, technology, application, and business, and showcases industry case studies that illustrate how enterprises can assess their AI readiness, identify gaps, and accelerate AI‑driven transformation.
At the 2025 Inclusion·外滩大会 forum, leading academics and industry pioneers discussed rapid advances in AI agents, highlighting breakthroughs in multi‑agent systems, reinforcement learning, open‑source frameworks, and the practical challenges of cost, performance, and usability that still separate "usable" from truly "useful" technology.
Ling-mini-2.0, an open-source 16 B MoE language model that activates only 1.4 B parameters, achieves dense-level performance with 7× efficiency, generates over 300 tokens / s, and introduces the first FP8 mixed-precision training suite, offering multiple pre-training checkpoints for the AI community.
This article details the two‑month end‑to‑end development of Alipay's AI Travel Assistant, describing the product concept, technical background, adoption of the xUI interaction framework, gRPC streaming, Kotlin Multiplatform integration, migration challenges, performance metrics, and future roadmap.
Shanghai’s 2024 Science and Technology Award honored a joint effort by Shanghai Jiao Tong University and Ant Group for pioneering AI-driven technologies—multimodal hallucination mitigation, controllable data generation, integrated content security monitoring, and adversarial model protection—that set international standards in detecting harmful online media and AIGC content.
This systematic review, authored by Peking University and Ant Group researchers, examines efficient privacy‑preserving machine learning (PPML) across protocol, model, and system layers, identifies key optimization challenges, proposes a unified cross‑layer perspective, and outlines future research directions for large‑model privacy and performance.
The IJCAI 2025 paper showcase introduces the Mixture‑of‑Queries Transformer, a novel model that combines frequency‑domain feature enhancement with collaborative query decoding to achieve state‑of‑the‑art camouflaged instance segmentation across multiple datasets.
Ant Group's open‑source native GUI agent UI‑Venus leverages multimodal large‑model and reinforcement‑learning techniques to outperform prior models on grounding and navigation benchmarks, while using a high‑quality data pipeline and a self‑evolving alignment mechanism to push the limits of GUI automation.
