Tagged articles

658 articles

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Mar 27, 2020 · Artificial Intelligence

Understanding Data Product Layers: Business Value, Data, Algorithms, and Applications

The article explains how data products create business value through application, data, and algorithm layers, using examples like 5G infrared temperature screening and ImageNet, and discusses the roles of experimental design, causal inference, and reinforcement learning in building effective AI‑driven strategies.

Data Productartificial intelligencebusiness value

0 likes · 8 min read

Understanding Data Product Layers: Business Value, Data, Algorithms, and Applications

Alibaba Cloud Developer

Feb 14, 2020 · Artificial Intelligence

How Alibaba’s AI Voice Bots Revolutionized Customer Service During the Pandemic

This article explains how Alibaba leveraged AI‑powered voice robots to handle massive outbound call volumes during COVID‑19, detailing the technology stack, real‑world application scenarios across finance and retail, and the future potential of intelligent voice assistants in customer service.

AIcustomer-servicenatural language processing

0 likes · 11 min read

How Alibaba’s AI Voice Bots Revolutionized Customer Service During the Pandemic

Alibaba Cloud Developer

Feb 7, 2020 · Artificial Intelligence

Tackling Scalability, Data Scarcity, and Training Efficiency in Dialogue Management Models

This article reviews the evolution of dialogue management models from rule‑based systems to deep‑learning approaches, identifies three major challenges—poor scalability, limited annotated data, and low training efficiency—and surveys recent research solutions including semantic matching, knowledge distillation, hierarchical reinforcement learning, model‑based RL, and human‑in‑the‑loop methods.

Conversational AIdata annotationdialogue management

0 likes · 44 min read

Tackling Scalability, Data Scarcity, and Training Efficiency in Dialogue Management Models

Qunar Tech Salon

Feb 5, 2020 · Operations

Understanding Didi's Ride‑Hailing Dispatch Algorithms: Challenges, Models, and Future Directions

The article explains why Didi needs advanced dispatch algorithms, describes the complexities of order‑driver matching from simple one‑to‑one cases to large‑scale bipartite matching, and introduces batch matching, supply‑demand prediction, chain dispatch, and AI‑driven optimizations that together improve global efficiency and user experience.

AIDispatchOperations Research

0 likes · 16 min read

Understanding Didi's Ride‑Hailing Dispatch Algorithms: Challenges, Models, and Future Directions

Top Architect

Jan 16, 2020 · Artificial Intelligence

A Survey of Neural Architecture Search: Search Spaces, Optimization Strategies, and Recent Results

This article surveys neural architecture search, classifying existing methods, describing common search spaces—including global and cell‑based designs—detailing optimization strategies such as reinforcement learning, evolutionary algorithms, surrogate models, one‑shot and differentiable approaches, and highlighting recent results and trends in the field.

Evolutionary AlgorithmsNASNeural Architecture Search

0 likes · 13 min read

A Survey of Neural Architecture Search: Search Spaces, Optimization Strategies, and Recent Results

DataFunTalk

Jan 2, 2020 · Artificial Intelligence

Improving Zhihu Search: Query Understanding, Term Weighting, Synonym Expansion, Query Rewriting, and Semantic Retrieval

This article details Zhihu's search engineering advances over the past year, covering long‑tail query challenges, term‑weight calculation, synonym expansion, query rewriting with translation models and reinforcement learning, and semantic retrieval using BERT‑based embeddings, while outlining future research directions.

NLPQuery RewritingQuery Understanding

0 likes · 14 min read

Improving Zhihu Search: Query Understanding, Term Weighting, Synonym Expansion, Query Rewriting, and Semantic Retrieval

DataFunTalk

Dec 16, 2019 · Artificial Intelligence

A Comprehensive Overview of Sequential Recommendation Models and Techniques

This article provides an in-depth overview of sequential recommendation, defining the problem, discussing data preparation, and reviewing various neural architectures—including MLP, CNN, RNN, Temporal CNN, self‑attention, and reinforcement‑learning approaches—while offering practical guidance on model selection and implementation.

CNNDeep LearningRNN

0 likes · 36 min read

A Comprehensive Overview of Sequential Recommendation Models and Techniques

DataFunTalk

Dec 10, 2019 · Artificial Intelligence

Applying Deep Reinforcement Learning (DQN) to the 2048 Game: Experiments and Insights

This article details a series of reinforcement‑learning experiments on the 2048 game, from random baselines through DQN implementations, classical value‑iteration methods, network redesigns, and Monte‑Carlo tree search, highlighting challenges such as reward design, over‑estimation, and exploration while achieving scores up to 34 000 and tiles of 2048.

2048AIDQN

0 likes · 8 min read

Applying Deep Reinforcement Learning (DQN) to the 2048 Game: Experiments and Insights

DataFunTalk

Nov 27, 2019 · Artificial Intelligence

Applying Reinforcement Learning and Graph Embedding for Intelligent User Operations in Didi Ride‑Sharing

This article describes how Didi Ride‑Sharing leverages reinforcement learning and graph‑embedding techniques to model and optimize user‑operation marketing, detailing system architecture, algorithm design, experimental ROI improvements, and personalized message delivery for enhanced conversion and cost efficiency.

DidiROIgraph embedding

0 likes · 11 min read

Applying Reinforcement Learning and Graph Embedding for Intelligent User Operations in Didi Ride‑Sharing

AntTech

Oct 30, 2019 · Artificial Intelligence

Financial Graph Machine Learning, AutoML, and Multi‑Agent Reinforcement Learning at Ant Financial

Professor Song Le presented at the Cloudwise Conference how Ant Financial leverages large‑scale graph neural networks, automated machine‑learning platforms, and multi‑agent reinforcement learning to model complex financial networks, improve risk control, and drive diverse fintech applications.

Ant FinancialLarge-Scale Graphgraph neural networks

0 likes · 12 min read

Financial Graph Machine Learning, AutoML, and Multi‑Agent Reinforcement Learning at Ant Financial

DataFunTalk

Oct 25, 2019 · Artificial Intelligence

Advances and Challenges in Human‑Machine Dialogue: Open‑Domain and Task‑Oriented Systems

This article reviews recent progress and open research problems in human‑machine dialogue, covering both open‑domain chat and task‑oriented systems, with focus on reply quality, decoding, retrieval‑augmented generation, controllable and personalized responses, multi‑turn modeling, reinforcement‑learning strategies, low‑resource NLU, and data augmentation techniques.

Dialogue SystemsResponse Generationnatural language processing

0 likes · 16 min read

Advances and Challenges in Human‑Machine Dialogue: Open‑Domain and Task‑Oriented Systems

Tencent Cloud Developer

Oct 11, 2019 · Cloud Computing

Large-Scale Distributed Reinforcement Learning Solution Based on TKE

The project replaces cumbersome manual management of thousands of heterogeneous CPU and GPU nodes for large‑scale reinforcement learning with a TKE‑based, containerized actor‑learner architecture that automates batch start/stop, provides elastic autoscaling, fault‑tolerant processes, shared model storage, and CI‑driven image deployment, cutting costs by up to two‑thirds while dramatically speeding experiment cycles.

CI/CDCloud NativeDistributed Training

0 likes · 14 min read

Large-Scale Distributed Reinforcement Learning Solution Based on TKE

DataFunTalk

Sep 30, 2019 · Artificial Intelligence

Reinforcement Learning for Recommender Systems: Challenges, Solutions, and Key Papers

This article reviews recent advances in applying reinforcement learning to recommendation systems, explains the fundamental RL concepts, discusses the specific challenges such as large action spaces, bias, and long‑term reward modeling, and summarizes two influential YouTube papers along with practical insights and future directions.

Top‑Klong-term rewardoff‑policy

0 likes · 13 min read

Reinforcement Learning for Recommender Systems: Challenges, Solutions, and Key Papers

DataFunTalk

Sep 19, 2019 · Artificial Intelligence

Alibaba Cloud Xiaomai Dialogue System: Architecture, NLU, Dialogue Management, and User Simulator

This article presents Alibaba's Xiaomai intelligent dialogue platform, detailing its general system architecture, three-tier NLU approaches for zero‑, few‑, and many‑shot scenarios, platform‑centric dialogue management with TaskFlow, robustness and continuous learning mechanisms, and a user simulator for large‑scale data generation and dialogue diagnosis.

dialogue systemmeta-learningnatural language understanding

0 likes · 13 min read

Alibaba Cloud Xiaomai Dialogue System: Architecture, NLU, Dialogue Management, and User Simulator

DataFunTalk

Sep 18, 2019 · Operations

Understanding Didi's Ride‑Hailing Dispatch Algorithm: Challenges, Models, and Strategies

This article explains why modern ride‑hailing platforms need advanced dispatch algorithms, describes the underlying order‑allocation problem, explores simple and complex matching scenarios, and introduces batch matching, supply‑demand prediction, chain dispatch, and AI‑driven techniques used by Didi to improve efficiency and fairness.

DispatchRide Hailingdynamic VRP

0 likes · 15 min read

Understanding Didi's Ride‑Hailing Dispatch Algorithm: Challenges, Models, and Strategies

Didi Tech

Sep 13, 2019 · Artificial Intelligence

Understanding Didi's Ride‑Hailing Dispatch Algorithms: Challenges and Strategies

Didi’s ride‑hailing dispatch system has progressed from a simple greedy, first‑come‑first‑served matcher to sophisticated batch, chain, and predictive algorithms that use deep‑learning demand forecasts and reinforcement‑learning optimization to assign drivers under complex business rules, boosting response rates and serving over 30 million daily requests.

AIRide Hailingmatching

0 likes · 17 min read

Understanding Didi's Ride‑Hailing Dispatch Algorithms: Challenges and Strategies

Alibaba Cloud Developer

Aug 28, 2019 · Artificial Intelligence

Exact‑K Recommendation: Graph Attention Networks and RL from Demonstrations Explained

This article introduces the Exact‑K recommendation problem, highlights its differences from traditional Top‑K approaches, and presents a novel solution combining Graph Attention Networks (GAttN) with Reinforcement Learning from Demonstrations (RLfD), backed by extensive experiments showing superior performance on real-world datasets.

exact-kgraph attention networksmachine learning

0 likes · 14 min read

Exact‑K Recommendation: Graph Attention Networks and RL from Demonstrations Explained

Tencent Cloud Developer

Aug 14, 2019 · Artificial Intelligence

From Atari to AI: The Evolution of Video Games and Artificial Intelligence

From Steve Jobs’s early work at Atari to modern DeepMind breakthroughs, the article traces how video games have grown into a multibillion‑dollar industry that serves as a testbed for AI research, while highlighting current AI techniques for smarter agents, procedural content generation, and the collaborative challenges shaping the future of game development.

Game DevelopmentMonte Carlo Tree Searchartificial intelligence

0 likes · 25 min read

From Atari to AI: The Evolution of Video Games and Artificial Intelligence

DataFunTalk

Jul 31, 2019 · Artificial Intelligence

Key Characteristics and Practical Improvements of Recommendation Technologies

This article discusses the fundamental traits of recommendation technologies, compares UserCF and ItemCF models, explains matrix factorization and FM, explores negative sampling, CTR/CVR modeling, ensemble methods, and practical considerations such as reinforcement learning and exploration strategies for improving recommendation performance in real-world systems.

matrix factorizationreinforcement learning

0 likes · 11 min read

Key Characteristics and Practical Improvements of Recommendation Technologies

AntTech

Jul 21, 2019 · Artificial Intelligence

Alipay’s SIGIR 2019 Papers: Reinforcement Learning for User Intent Prediction and Unsupervised QUEST for Complex Question Answering

At SIGIR 2019 in Paris, Alipay presented two AI research papers—one applying reinforcement learning to predict user intent in customer‑service bots and another introducing the unsupervised QUEST method that builds noisy quasi‑knowledge graphs for answering complex multi‑document questions.

AIKnowledge GraphUnsupervised Learning

0 likes · 5 min read

Alipay’s SIGIR 2019 Papers: Reinforcement Learning for User Intent Prediction and Unsupervised QUEST for Complex Question Answering

iQIYI Technical Product Team

Jul 12, 2019 · Artificial Intelligence

Real-Time Evaluation System for Adaptive Bitrate (ABR) Algorithms and Controlled Bitrate Distribution

RESA is a real‑time evaluation platform that continuously tests multiple Adaptive Bitrate (ABR) algorithms on live user traffic, introduces a multi‑user QoE metric derived from viewing behavior, reveals trade‑offs between clarity and bandwidth, and proposes the RL‑based ABSbc algorithm to steer bitrate distribution and balance user experience with network cost.

ABRBandwidth ControlQoE

0 likes · 23 min read

Real-Time Evaluation System for Adaptive Bitrate (ABR) Algorithms and Controlled Bitrate Distribution

Alibaba Cloud Developer

Jun 27, 2019 · Artificial Intelligence

Generating Personalized E‑commerce Review Replies with Product Information

This paper presents a sequence‑to‑sequence model that fuses product‑detail tables with customer comments, using gated multimodal attention, copy mechanisms and reinforcement learning to automatically produce high‑quality, context‑aware replies for e‑commerce platforms, and validates the approach with extensive experiments on a large Taobao dataset.

Sequence-to-Sequencecopy mechanisme‑commerce

0 likes · 21 min read

Generating Personalized E‑commerce Review Replies with Product Information

Ctrip Technology

Jun 19, 2019 · Artificial Intelligence

Applying Reinforcement Learning to Hotel Ranking at Ctrip: Challenges, Solutions, and Preliminary Results

This article examines the limitations of traditional learning‑to‑rank for Ctrip hotel sorting, introduces reinforcement learning as a remedy, outlines three progressive implementation plans (A, B, C) with algorithm choices and engineering trade‑offs, and presents early experimental findings that demonstrate RL's potential to improve conversion rates.

CtripRLhotel

0 likes · 15 min read

Applying Reinforcement Learning to Hotel Ranking at Ctrip: Challenges, Solutions, and Preliminary Results

AntTech

Jun 10, 2019 · Artificial Intelligence

Generative Adversarial User Model for Reinforcement Learning‑Based Recommendation Systems

This article presents a model‑based reinforcement learning framework for recommendation systems that uses a generative adversarial user model to simultaneously learn user behavior dynamics and reward functions, enabling efficient Cascading‑DQN policy learning and achieving superior long‑term user rewards and click‑through rates in experiments.

Cascading DQNGenerative Adversarial Networksartificial intelligence

0 likes · 9 min read

Generative Adversarial User Model for Reinforcement Learning‑Based Recommendation Systems

Alibaba Cloud Developer

Apr 1, 2019 · Fundamentals

Must-Read Technical Books Recommended by Alibaba Experts

Alibaba’s senior engineers share their curated list of essential technical books—from software testing and design patterns to AI, machine learning, reinforcement learning, Rust programming, and database architecture—explaining why each title is valuable for developers seeking deeper knowledge and practical insights.

AIDesign PatternsRust

0 likes · 9 min read

Must-Read Technical Books Recommended by Alibaba Experts

DataFunTalk

Mar 8, 2019 · Artificial Intelligence

Alibaba's Intelligent Service Bot (Ali Xiaomì): Platform Overview, Intent Recognition, Machine Reading Comprehension, Multi‑turn Recommendation, and Transfer Learning

The article presents an in‑depth overview of Alibaba's intelligent service bot Ali Xiaomì, covering its platform evolution, core NLP techniques such as intent recognition and machine reading comprehension, multi‑turn recommendation strategies, transfer‑learning approaches across domains and languages, and future technical challenges.

AImachine reading comprehensionnatural language processing

0 likes · 11 min read

Alibaba's Intelligent Service Bot (Ali Xiaomì): Platform Overview, Intent Recognition, Machine Reading Comprehension, Multi‑turn Recommendation, and Transfer Learning

Tencent Cloud Developer

Jan 17, 2019 · Artificial Intelligence

Deep Learning for Big Data Recommendation Systems: Tencent's Industrial Practice

Tencent’s industrial practice shows how a large‑scale offline‑nearline‑online “Shield” recommendation architecture, powered by the DeepR framework built on RCaffe, uses deep semantic embeddings, massive neural networks and reinforcement‑learning decisions to handle billions of daily requests, demonstrating that data richness and engineering capability, not model depth alone, drive performance in big‑data recommendation systems.

Big DataDeep LearningNeural Network

0 likes · 13 min read

Deep Learning for Big Data Recommendation Systems: Tencent's Industrial Practice

Alibaba Cloud Developer

Jan 15, 2019 · Artificial Intelligence

How Alibaba Engineers Boost SEO with Reinforcement Learning and Attention Models

This article details Alibaba.com engineers' application of reinforcement learning, attention mechanisms, and weakly supervised techniques to extract product summaries, improve content quality, and significantly raise SEO rankings, supported by offline experiments, online A/B testing, and future research directions.

AlibabaSEOattention model

0 likes · 16 min read

How Alibaba Engineers Boost SEO with Reinforcement Learning and Attention Models

DataFunTalk

Jan 9, 2019 · Artificial Intelligence

Reinforcement Learning in Natural Language Processing: Concepts, Challenges, and Applications

This article introduces reinforcement learning fundamentals, contrasts it with supervised learning, and explores its challenges and advantages in natural language processing, including applications such as text classification, relation extraction from noisy data, and weakly supervised topic segmentation, while summarizing key insights and experimental results.

Weak Supervisionnatural language processingreinforcement learning

0 likes · 11 min read

Reinforcement Learning in Natural Language Processing: Concepts, Challenges, and Applications

Alibaba Cloud Developer

Nov 20, 2018 · Artificial Intelligence

How Reinforcement Learning Powers Interactive Search in E‑Commerce

This article explains how reinforcement learning can be modeled and deployed to enable intelligent, interactive product search on e‑commerce platforms, detailing problem definition, system architecture, training methodology, online results, and future research directions.

Deep Learningdialogue systeme‑commerce

0 likes · 17 min read

How Reinforcement Learning Powers Interactive Search in E‑Commerce

iQIYI Technical Product Team

Nov 16, 2018 · Artificial Intelligence

How Reinforcement Learning Transforms Adaptive Bitrate Streaming

This article explains the principles of adaptive bitrate streaming, compares traditional ABR algorithms with a reinforcement‑learning‑based approach, describes its system architecture and training process, and presents QoS evaluation results that show RL‑driven streaming can improve video quality and smoothness.

ABR algorithmsAIQoS evaluation

0 likes · 8 min read

How Reinforcement Learning Transforms Adaptive Bitrate Streaming

Alibaba Cloud Developer

Nov 16, 2018 · Artificial Intelligence

How Alibaba’s Search Engine Evolved Over a Decade of Double‑11: From Offline Models to Real‑Time AI

This article traces the ten‑year evolution of Alibaba’s e‑commerce search system, detailing four major stages—from the early Pora streaming engine to dual‑link real‑time architectures, the integration of deep and reinforcement learning, and the shift to large‑scale online deep learning—while highlighting the technical drivers and future AI‑enabled search vision.

Online LearningSearche‑commerce

0 likes · 16 min read

How Alibaba’s Search Engine Evolved Over a Decade of Double‑11: From Offline Models to Real‑Time AI

Meituan Technology Team

Nov 15, 2018 · Artificial Intelligence

Reinforcement Learning for Meituan's "Guess You Like" Recommendation Ranking

Meituan enhanced its homepage “Guess You Like” recommendation slot by modeling user‑item interactions as a Markov Decision Process and applying an improved DDPG reinforcement‑learning agent that adjusts the ranking trade‑off parameter, uses advantage‑based Q decomposition, shares actor‑critic weights, and runs in a real‑time TensorFlow pipeline, delivering consistent lifts in click‑through, dwell time, and depth.

DDPGMDP ModelingOnline Learning

0 likes · 21 min read

Reinforcement Learning for Meituan's "Guess You Like" Recommendation Ranking

Tencent Cloud Developer

Oct 18, 2018 · Artificial Intelligence

10 Machine Learning Algorithms You Should Know to Become a Data Scientist

This article outlines the essential role of a data scientist and introduces ten fundamental machine‑learning algorithms—including PCA/SVD, OLS and polynomial regression, regularized linear models, K‑Means, logistic regression, SVM, feed‑forward, convolutional and recurrent neural networks, CRFs, ensemble trees, and reinforcement‑learning methods—while linking to popular Python libraries and tutorials.

AlgorithmsDecision TreesNeural Networks

0 likes · 10 min read

10 Machine Learning Algorithms You Should Know to Become a Data Scientist

Sohu Tech Products

Oct 10, 2018 · Artificial Intelligence

Optimizing News Recall with DDPG Reinforcement Learning and Transformer Architecture

This article explains how reinforcement learning, specifically the DDPG algorithm combined with Transformer-based networks, is applied to improve large‑scale news recall systems, detailing the business scenario, algorithm selection, model architecture, speed optimizations, training challenges, and observed online performance gains.

AIDDPGTransformer

0 likes · 13 min read

Optimizing News Recall with DDPG Reinforcement Learning and Transformer Architecture

DataFunTalk

Sep 27, 2018 · Artificial Intelligence

Applying Machine Learning in Shumei's Business: Supervised, Unsupervised, and Reinforcement Learning Cases

The article presents a comprehensive overview of how Shumei Technology leverages machine learning—including supervised, unsupervised, and reinforcement learning methods—across its credit scoring, fraud detection, advertising, and audio content moderation services, highlighting practical challenges, model fusion techniques, and future research directions.

Model Fusionreinforcement learning

0 likes · 12 min read

Applying Machine Learning in Shumei's Business: Supervised, Unsupervised, and Reinforcement Learning Cases

JD Tech

Sep 12, 2018 · Artificial Intelligence

JD Autonomous Delivery Robots: Technologies, Patents, and Future Challenges

The article details JD's third‑generation autonomous delivery robots, covering their multi‑sensor fusion localization, deep‑learning perception, reinforcement‑learning motion control, extensive patent portfolio, and upcoming technical hurdles such as high‑precision mapping and lidar cost, while also inviting public voting for patent awards.

AI navigationDeep LearningJD Logistics

0 likes · 8 min read

JD Autonomous Delivery Robots: Technologies, Patents, and Future Challenges

Sohu Tech Products

Sep 5, 2018 · Artificial Intelligence

Reinforcement Learning Theory Overview and Its Application to News Recommendation

This article reviews reinforcement learning fundamentals, contrasts it with supervised learning, surveys major RL algorithms such as DDPG and DQN, and details how these methods can be modeled for sequential news recommendation, including system architecture, state‑action definitions, and practical challenges.

AIDDPGDQN

0 likes · 15 min read

Reinforcement Learning Theory Overview and Its Application to News Recommendation

Alibaba Cloud Developer

Jul 13, 2018 · Artificial Intelligence

How We Won OpenAI’s Retro Contest: Joint PPO Mastery on Sonic Games

This article analyzes OpenAI’s Retro Contest on Sonic the Hedgehog, explains why reinforcement learning generalization is crucial for AGI, and details the winning team’s joint PPO pipeline, engineering optimizations, training strategies, and final performance compared to human baselines.

OpenAI Retro ContestRL generalizationSonic game

0 likes · 21 min read

How We Won OpenAI’s Retro Contest: Joint PPO Mastery on Sonic Games

WeChat Backend Team

May 11, 2018 · Artificial Intelligence

How PhoenixGo Turned AlphaGo Zero into a Champion AI Using Cloud Resources

PhoenixGo, an open‑source Go AI built on AlphaGo Zero's reinforcement‑learning algorithm, leveraged Tencent's idle cloud servers to achieve professional‑level play, won the 2018 World AI Go Championship, and was released with a strong model for researchers and hobbyists alike.

AIGocloud computing

0 likes · 4 min read

How PhoenixGo Turned AlphaGo Zero into a Champion AI Using Cloud Resources

Alibaba Cloud Developer

Apr 23, 2018 · Fundamentals

Top Technical Books Recommended by Alibaba Experts for World Book Day

On World Book Day, nine Alibaba technology veterans share a curated list of essential technical books—covering software testing, design patterns, AI, machine learning, reinforcement learning, Rust, and database architecture—offering concise reasons why each title is valuable for developers and engineers.

Database ArchitectureDesign PatternsRust programming

0 likes · 10 min read

Top Technical Books Recommended by Alibaba Experts for World Book Day

Tencent Cloud Developer

Mar 15, 2018 · Artificial Intelligence

Learning Long-Horizon Surgical Robot Tasks via Transition State Clustering, SWIRL, and DDCO

The article surveys three recent approaches—Transition State Clustering, Sequential Windowed Inverse Reinforcement Learning, and Deep Discovery of Continuous Options—that automatically segment long‑horizon surgical‑robot demonstrations into sub‑tasks, learn hierarchical policies from limited data, and achieve markedly higher success rates on da Vinci cutting, tension, and needle‑picking tasks.

Roboticshierarchical learningimitation learning

0 likes · 18 min read

Learning Long-Horizon Surgical Robot Tasks via Transition State Clustering, SWIRL, and DDCO

Alibaba Cloud Developer

Feb 5, 2018 · Artificial Intelligence

How Alibaba’s AliMe Evolved in 2017: AI Architecture, Algorithms, and Real‑World Impact

In 2017 Alibaba's AliMe chatbot platform expanded from a single‑company solution to a multilingual, multi‑channel AI service, introducing platform‑level SaaS/PaaS capabilities, a seven‑layer front‑end architecture, modular back‑end design, advanced intent recognition, knowledge‑graph‑driven product management, reinforcement‑learning‑based recommendation, and machine‑reading comprehension for enterprise and consumer use cases.

AI PlatformAlibabaChatbot

0 likes · 23 min read

How Alibaba’s AliMe Evolved in 2017: AI Architecture, Algorithms, and Real‑World Impact

High Availability Architecture

Dec 11, 2017 · Artificial Intelligence

A Brief History of Computer Chess and Its Role in Artificial Intelligence

This article traces the evolution of computer chess from the 18th‑century automaton "The Turk" through early programs by Turing, Shannon, and McCarthy, to landmark systems like Deep Blue, AlphaGo, and AlphaZero, highlighting key algorithms, milestones, and their impact on AI research.

AI historyAlphaZeroDeep Blue

0 likes · 19 min read

A Brief History of Computer Chess and Its Role in Artificial Intelligence

Hulu Beijing

Dec 6, 2017 · Artificial Intelligence

How Deep Reinforcement Learning Powers Video Game AI: From Q‑Learning to Atari Mastery

This article explains how deep reinforcement learning, built upon traditional Q‑learning and enhanced with techniques like experience replay, enables agents to play Atari video games directly from raw pixel inputs, illustrating the key differences, processing steps, and the significance of this breakthrough in AI.

AtariQ-Learningdeep Q‑learning

0 likes · 5 min read

How Deep Reinforcement Learning Powers Video Game AI: From Q‑Learning to Atari Mastery

Hulu Beijing

Dec 5, 2017 · Artificial Intelligence

What Is Reinforcement Learning? Core Concepts Explained

This article introduces the fundamental concepts of reinforcement learning, describing its origins, key components such as agents, environments, states, actions, and rewards, explaining the Markov decision process framework, and highlighting common algorithms like Q‑learning, policy gradients, and actor‑critic methods.

AIAlgorithmsMDP

0 likes · 4 min read

What Is Reinforcement Learning? Core Concepts Explained

Ctrip Technology

Oct 19, 2017 · Artificial Intelligence

Intelligent Human‑Computer Interaction: Technical Practices of Alibaba’s “Ali Xiaomi” Chatbot

This article presents a comprehensive overview of Alibaba’s intelligent chatbot “Ali Xiaomi”, covering industry context, e‑commerce deployment, NLU architecture, intent‑matching layers, deep‑learning‑based intent classification, reinforcement‑learning‑driven recommendation, knowledge‑graph‑enhanced services, and hybrid retrieval‑generation dialogue models, with future outlooks for AI‑driven interaction.

Deep LearningKnowledge Graphe‑commerce

0 likes · 18 min read

Intelligent Human‑Computer Interaction: Technical Practices of Alibaba’s “Ali Xiaomi” Chatbot

ITPUB

Sep 14, 2017 · Artificial Intelligence

How Salesforce’s Seq2SQL Turns Natural Language into SQL with Reinforcement Learning

Salesforce’s recent research introduces Seq2SQL, a reinforcement‑learning‑driven sequence‑to‑sequence model that translates natural‑language questions into SQL queries, eliminating the need to learn SQL, and includes the large WikiSQL dataset built from crowdsourced NL‑SQL pairs for training and evaluation.

AISQL generationSeq2SQL

0 likes · 6 min read

How Salesforce’s Seq2SQL Turns Natural Language into SQL with Reinforcement Learning

AntTech

Aug 4, 2017 · Artificial Intelligence

Key Insights from Ant Financial VP Dr. Qi Yuan’s Talk on the Development and Application of Financial Intelligence at CCAI 2017

The article summarizes Dr. Qi Yuan’s presentation at CCAI 2017, detailing Ant Financial’s AI‑driven solutions for financial services—including risk control, intelligent assistants, large‑scale machine learning, reinforcement‑learning marketing, a model‑service platform, and a computer‑vision damage‑assessment system—while highlighting technical challenges, platform architecture, and the company’s open‑tech philosophy.

FinTechartificial intelligencereinforcement learning

0 likes · 16 min read

Key Insights from Ant Financial VP Dr. Qi Yuan’s Talk on the Development and Application of Financial Intelligence at CCAI 2017

Alibaba Cloud Developer

Jul 13, 2017 · Artificial Intelligence

How STARK VRP Cuts Chinese Logistics Costs with AI‑Powered Routing

This article explains how Alibaba's Cainiao network built the STARK VRP engine—an AI‑driven, distributed vehicle‑routing solver that supports dozens of VRP variants, leverages metaheuristics, parallel island models, and deep reinforcement learning to dramatically reduce fleet size and travel distance in Chinese logistics.

AILogistics OptimizationMetaheuristics

0 likes · 8 min read

How STARK VRP Cuts Chinese Logistics Costs with AI‑Powered Routing

21CTO

Jun 29, 2017 · Artificial Intelligence

Why Machine Learning Mirrors Human Learning: From Features to Reinforcement

The article explores how machine learning models emulate human learning by converting diverse real‑world descriptions into numerical features, illustrating concepts such as one‑hot encoding, supervised, unsupervised, and reinforcement learning, and emphasizing the importance of mapping inputs to outputs for intelligent systems.

AI conceptsfeaturesmachine learning

0 likes · 14 min read

Why Machine Learning Mirrors Human Learning: From Features to Reinforcement

Qunar Tech Salon

Apr 27, 2017 · Artificial Intelligence

LSTM‑Jump: Learning to Skim Text for Faster Sequence Modeling

The paper introduces LSTM‑Jump, a reinforcement‑learning‑trained LSTM variant that can dynamically skip irrelevant tokens, achieving up to six‑fold speed‑ups over standard sequential LSTMs while maintaining or improving accuracy on various NLP tasks such as sentiment analysis, document classification, and question answering.

LSTMNLPSequence Modeling

0 likes · 7 min read

LSTM‑Jump: Learning to Skim Text for Faster Sequence Modeling

21CTO

Apr 19, 2017 · Artificial Intelligence

How Alibaba Transformed E‑Commerce Search with Real‑Time AI and Reinforcement Learning

Alibaba’s e‑commerce search engine evolved over three years from offline batch models to a sophisticated AI-driven system that integrates real‑time feature ingestion, online learning, deep and reinforcement learning, enabling dynamic personalization and decision‑making that boosts conversion during high‑traffic events like Double 11.

AIOnline LearningReal‑Time Computing

0 likes · 15 min read

How Alibaba Transformed E‑Commerce Search with Real‑Time AI and Reinforcement Learning

Alibaba Cloud Developer

Mar 22, 2017 · Artificial Intelligence

Unlocking StarCraft AI Research with Gym StarCraft: A Python-Friendly RL Platform

StarCraft, a classic real‑time strategy game, has become a premier testbed for deep reinforcement learning and AI research, and Alibaba’s open‑source Gym StarCraft platform now bridges Python, TensorFlow, Keras and OpenAI Gym to simplify multi‑agent, macro‑strategy development and fair benchmarking.

AlibabaOpenAI GymPython

0 likes · 3 min read

Unlocking StarCraft AI Research with Gym StarCraft: A Python-Friendly RL Platform

Architect

Mar 10, 2016 · Artificial Intelligence

Monte Carlo Tree Search (MCTS): Principles, Algorithms, Advantages, and Applications

This article explains Monte Carlo Tree Search (MCTS), covering its origin in AlphaGo, fundamental algorithm steps, node‑selection strategies such as UCB, strengths and weaknesses, enhancements, historical background, and recent research developments in artificial intelligence.

MCTSMonte Carlo Tree SearchSearch Algorithms

0 likes · 12 min read

Monte Carlo Tree Search (MCTS): Principles, Algorithms, Advantages, and Applications

dbaplus Community

Mar 9, 2016 · Artificial Intelligence

How AlphaGo’s Deep Neural Networks Achieve Human‑Level Go Mastery

This article breaks down AlphaGo’s breakthrough architecture—four specialized neural‑network modules, Monte‑Carlo Tree Search, and deep reinforcement learning—to explain how the system moved from imitation learning to self‑improvement and ultimately defeated top human Go players.

AlphaGoDeep LearningGo AI

0 likes · 15 min read

How AlphaGo’s Deep Neural Networks Achieve Human‑Level Go Mastery

Architects Research Society

Oct 4, 2015 · Artificial Intelligence

Bayesian Thinking on Your Feet: Embedding Generative Models in Reinforcement Learning for Sequentially Revealed Data

This NSF‑funded project aims to develop algorithms that incrementally process partially observed data, integrating generative models with reinforcement‑learning policies to decide when to act, applied to simultaneous machine translation and quiz‑bowl style question answering.

Bayesian inferenceGenerative Modelsmachine translation

0 likes · 4 min read

Bayesian Thinking on Your Feet: Embedding Generative Models in Reinforcement Learning for Sequentially Revealed Data

Baidu Tech Salon

Sep 22, 2014 · Artificial Intelligence

How Baidu’s Bingo AI Cracked the Go Challenge with Novel Algorithms

After decades of being deemed a 'century‑long' AI challenge, Baidu’s Bingo system achieved amateur‑to‑professional level Go play by introducing optimized Monte‑Carlo tree search, a weakened Alpha‑Beta hybrid, and massive supervised learning, demonstrating how breakthroughs in game AI can ripple into broader Baidu products.

BaiduGo AIMonte Carlo Tree Search

0 likes · 8 min read

How Baidu’s Bingo AI Cracked the Go Challenge with Novel Algorithms