Amap shares four conference talks detailing how they built trustworthy AI agents for business decisions, covering semantic governance, causal evaluation, evaluation-driven development, and a product architecture shift that lifted accuracy from 50% to 95% by separating deterministic computation from model reasoning.
This article analyzes eight Palantir Foundry deployments across aerospace, consumer goods, energy, and insurance, showing how ontology-based data integration enables real-time decision-making, cuts costs by millions, and accelerates digital transformation without replacing existing ERP or MES systems.
The article analyzes Semantica’s open‑source “Palantir” project, which reached GitHub Trending #1, and explains how its Ontology layer extends agents beyond question answering to decision‑making and execution by modeling business objects, relationships, rules, and actions.
Semantica, an open‑source project dubbed the ‘open‑source Palantir’, builds a context graph that records each AI decision as a first‑class node, links entities with typed edges, timestamps changes with a hash‑chain ledger, and provides deterministic Datalog reasoning, enabling full traceability and auditability of AI‑driven outcomes.
The article analyzes why AI has struggled to enter core enterprise decision processes, proposes that the missing piece is accountable, context‑aware AI, and details how DecideX’s decision‑intelligence platform addresses this gap through a layered architecture, real‑world case studies, and a 5A implementation methodology.
The article argues that while ontology is important for AI‑driven decision making, it is not the sole solution; a combined architecture of ontology, semantic layers, Skills, data assets, and action loops is needed to achieve practical, enterprise‑level intelligent decisions.
This case study examines a normative analysis framework that combines operations research, statistical learning, and large language models to automate end‑to‑end decision making in a ChatBI product, demonstrating up to 20‑fold efficiency gains, cost reductions, and enhanced interpretability across structured optimization and semi‑structured pricing scenarios.
The article examines how large AI models, integrated with big data through a "6D" framework, help bank staff overcome data‑access bottlenecks, enable natural‑language‑to‑SQL queries, build discriminative AI for credit forecasting, and support digital twins and decision‑intelligence agents for smarter digital‑finance operations.
At NeurIPS 2024, Alibaba's AliMama workshop introduced AIGB, a generative-model based auto-bidding solution, and released AuctionNet benchmark with billions of records; the competition attracted 1,861 participants across generative and uncertainty tracks, with winners earning prizes and internships, highlighting broader applications beyond advertising.
Alibaba’s Alimama showcases Intelligent Commerce 2.0 by combining knowledge‑driven multimodal product detection, trillion‑parameter model serving, logical‑reasoning decision‑intelligence frameworks for AI‑generated bidding, and creative diffusion‑based marketing tools such as virtual try‑ons and font style transfer, all powered by a massive training platform and academic collaborations.
At the ACM China Turing SIGAI Forum, Alibaba’s CTO Zheng Bo unveiled “Intelligent Commerce 2.0,” a knowledge‑driven AI era that shifts commerce from statistical data to causal decision‑making, leveraging generative large‑model, multimodal and AI Serving4LM technologies for B2B/B2C marketing, analytics, risk control, asset management, and is supported by the Peking‑University‑Alibaba PAAI lab’s research on graph models, decision intelligence and AI‑generated music.
This article provides a comprehensive overview of decision intelligence, distinguishing predictive and decision tasks, classifies decision environments, and delves into reinforcement learning fundamentals, algorithms such as SARSA, deep reinforcement learning, and discusses current applications, challenges, and future research directions.
This article introduces Didi’s real‑time online marketplace, explains its fundamental principles, network effects, and social efficiency benefits, and examines key technical areas such as mechanism design, decision intelligence, operations research, reinforcement learning, and causal inference that drive its advanced matching and dispatch strategies.
The article introduces Didi’s large‑scale ride‑hailing marketplace, explaining how mechanism design, decision intelligence, operations research, reinforcement learning, and causal inference are combined to create fair, efficient, data‑driven matching, routing, and incentive systems that tackle massive two‑sided market challenges.
The DataFun Summit 2022 featured a series of expert talks on data‑driven decision intelligence for large‑scale manufacturing networks, covering inventory safety stock optimization, AI‑based production planning, industrial solver applications, and real‑world case studies to help attendees improve supply‑chain efficiency and adopt intelligent manufacturing technologies.
This article explores how decision‑intelligence and operations‑research techniques are applied across the automotive supply chain, detailing the industry’s structure, optimization methods at strategic, planning and execution levels, implementation difficulties, and practical lessons drawn from real‑world projects.
This article explains how decision‑intelligence and operations‑research techniques are applied across the automotive supply chain, describing the industry structure, optimization methods at strategic, planning and execution levels, implementation difficulties, real‑world case studies, and lessons learned from a decade of data‑analysis practice.
