The article explains how the terms “neural network” and “deep learning” originated, why they were once treated as distinct branches of AI, and how historical biases and naming politics eventually merged them into a single research direction.
This article presents concise English summaries of four recent arXiv papers that explore AI-driven trading frameworks, dual‑view risk‑relation identification from 10‑K filings, multimodal language models for financial forecasting, and credit‑spread prediction enhanced by non‑financial data, highlighting their methods, datasets, and performance results.
This article compiles and categorizes the most significant Text‑to‑SQL and NL2SQL datasets released up to 2025, detailing their origins, sizes, domains, and evaluation benchmarks, while providing quick links to papers and dataset repositories for researchers and developers.
This article presents a curated collection of five open‑source AI‑related projects—including a comprehensive AI agent directory, a MacBook‑based digital scale, an invisible desktop assistant, a collaborative trading simulator, and Microsoft’s Magentic‑UI web helper—each with brief descriptions and GitHub links.
In this talk, Volcano Engine’s technical expert Chen Shuo explains the evolution of the Data Agent platform, the four‑quadrant framework for AI‑driven analytics, real‑world deployment challenges, architectural upgrades from pipeline to intelligent scheduling, and key lessons for building reliable, enterprise‑grade AI agents.
The DataFun 2025 GenAI Summit brings together top AI researchers and industry leaders to explore the latest breakthroughs in large‑model technology, generative recommendation, AI‑driven finance, video understanding, legal AI, and marketing, featuring five heavyweight forums, live demos, and a QR‑code registration for free access.
Recent AI research is tackling the challenge of decoding animal communication, from chimpanzee vocal combinations to whale acoustic patterns, revealing complex structures and prompting new debates about the nature of language across species.
This article explores how integrating AI‑driven AIOps with the TiDB distributed database can automate monitoring, enable proactive anomaly detection, streamline root‑cause analysis, and optimize capacity planning, ultimately shifting database operations from manual firefighting to intelligent, data‑driven management.
The DataFun Summit 2025 showcases leading experts presenting the latest breakthroughs in generative AI, large‑model research, and industry‑specific applications—from financial analysis and e‑commerce recommendation to video understanding and intelligent agents—offering attendees actionable insights to accelerate AI adoption across sectors.
Jeff Huber, founder of Chroma, discusses the startup’s mission to turn AI demos into production‑grade applications, critiques the hype around RAG, emphasizes the importance of Context Engineering, and explains how Chroma’s open‑source vector database and cloud service aim to simplify AI search for developers.
Since 2016, Alipay's security team has built the Alipay‑SDL 1.0 framework and now integrates AI and large‑model technologies to automate risk identification, enhance security tools, and streamline operations across the entire software development lifecycle, addressing rising business complexity and engineer workload.
Google DeepMind, together with researchers from NYU, Stanford and Brown, applied a machine‑learning framework and a high‑precision Gauss‑Newton optimizer to systematically discover new unstable singularities in three fluid equations, achieving solution accuracy that significantly surpasses existing work and revealing an empirical formula linking blow‑up rate to instability order.
The article recounts how a risk‑control algorithm engineer progressively refined a generic large language model through four stages of prompt engineering—role‑playing, business knowledge injection, deeper analysis, and a double‑hypothesis decision framework—to transform it into a precise e‑commerce fraud detection expert.
This article details Alipay's AI4SDL framework, describing how AI-driven tools and multimodal models automate risk identification, enhance code analysis, and streamline security operations across the entire software development lifecycle, while also outlining current challenges, systematic solutions, and future directions for secure, rapid product innovation.
This article presents a comprehensive set of 40 machine‑learning interview questions covering fundamental concepts such as the F1 score, logistic regression, activation functions, bias‑variance trade‑off, ensemble methods, feature scaling, cross‑validation, PCA, and hyper‑parameter optimization, each followed by concise, explanatory answers.
This article explains how Baidu Maps' ETA system evolved from simple static calculations to AI‑driven predictive models, detailing the four development stages, the underlying pre‑trained traffic large model, end‑to‑end route prediction techniques, and real‑world applications such as commuting, airport transfers, event management, and holiday travel.
This tutorial walks through building a music‑genre classification system using the GTZAN dataset, extracting MFCC features, implementing a K‑Nearest Neighbors classifier in Python, and achieving roughly 70% accuracy on test data.
The article explains linear regression and logistic regression fundamentals, covering loss functions such as mean‑squared error and cross‑entropy, analytic solutions, feature expansion for non‑linear separability, and provides Python code examples to illustrate the concepts.
AI is reshaping software quality assurance by augmenting testers with intelligent agents that automate test case creation, predict failures, and enable data‑driven analysis, while demanding new skills in data fluency, security, and cross‑team collaboration to stay competitive in the evolving testing landscape.
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.
This review surveys how artificial intelligence—through machine learning, deep learning, and large language models—enables researchers to characterize, predict, and reconstruct complex quantum systems, outlines a unified learning framework, discusses current breakthroughs and challenges, and envisions a future "quantum GPT" that could transform quantum science.
This article presents six key regression evaluation metrics—RMSE, MAPE, MSE, MAE, R² Score, and Weighted MAPE—explaining their formulas, advantages, and limitations, and shows how each helps assess model performance in machine learning tasks.
This article explores how AIOps transforms traditional IT operations by using AI for anomaly detection, root‑cause analysis, capacity forecasting, and self‑healing, offering a step‑by‑step roadmap, real‑world code examples, toolchain recommendations, common pitfalls, and future trends for building intelligent, automated operations.
This concise guide presents twelve core AI algorithms—from gradient boosting and deep neural networks to decision trees and K‑nearest neighbors—detailing their strengths, typical applications such as fraud detection, image classification, and price forecasting, and offering practical tips for selecting the right model.
This article provides a comprehensive overview of key machine‑learning algorithms—including supervised methods like linear regression, SVM, Naive Bayes, logistic regression, k‑NN, decision trees, random forests, GBDT, and unsupervised techniques such as k‑means, hierarchical clustering, DBSCAN, and PCA—explaining their principles, strengths, and typical use cases.
This article walks through ten lesser‑known Scikit‑learn utilities—including FunctionTransformer, custom estimators, TransformedTargetRegressor, HTML estimator visualisation, QuadraticDiscriminantAnalysis, Voting and Stacking ensembles, LocalOutlierFactor with UMAP, QuantileTransformer, and a PCA‑tSNE/UMAP workflow—showing concrete code examples, performance numbers and practical tips for more efficient and robust machine‑learning pipelines.
Data augmentation, while popular for single-source domain generalization, often induces severe out-of-distribution performance swings during training; the PEER framework combats this by employing dual-model collaboration, entropy regularization, periodic parameter averaging, and dynamic augmentation, achieving state-of-the-art robustness across multiple benchmark datasets.
This article introduces Ant Group’s new Ray‑based distributed agent framework Ragent, outlines its background and motivation, and details the four core modules—Profile, Memory, Planning, and Action—that together enable sophisticated LLM‑driven AI agents for large‑scale applications.
This article defines what constitutes a high‑quality AI dataset, explains why such datasets are crucial—especially given the dominance of English resources and the scarcity in Chinese—and outlines the scientific evaluation framework covering completeness, accuracy, balance, timeliness, consistency, relevance, and other key dimensions.
Privacy-Preserving Machine Learning (PPML) integrates cryptographic techniques such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation to enable model training and inference on encrypted or distributed data, thereby breaking data silos while safeguarding privacy across sectors like healthcare, finance, and advertising.
This article compiles a comprehensive list of high‑quality, publicly available datasets across domains such as general platforms, education, finance, health, text, and vision, providing URLs, key features, and practical usage tips to help researchers and practitioners quickly find the right data for their AI and data‑science projects.
This article introduces the book “Intelligent Supply Chain: Data Science Theory and Practice”, outlining how data science drives end‑to‑end supply‑chain optimization through real‑world case studies, covering topics from data preprocessing to advanced modeling, delivery efficiency, and customer‑service forecasting.
This digest summarizes nine recent arXiv papers covering quantum portfolio optimization, thematic investing with semantic stock representations, multi‑indicator reinforcement learning for trading, attention‑based asset pricing, ESG variable selection, deep neural networks for return distribution forecasting, a foundation model for financial time‑series, a multi‑agent trading system with self‑reflection, and dynamic weighting machine‑learning stock selection strategies.
The article recounts how a risk‑control algorithm engineer transformed a generic large language model into a specialized e‑commerce fraud detector by iteratively designing prompts, injecting business rules, structuring I/O, and introducing a dual‑hypothesis decision framework to achieve accurate, automated risk analysis.
This article chronicles the evolution of artificial intelligence over eight decades, spotlighting seminal figures such as Alan Turing, Allen Newell, Marvin Minsky, John McCarthy, Yoshua Bengio, Geoffrey Hinton, Andrew Ng and Yann LeCun, and explains how their groundbreaking work shaped modern AI.
A trio of freshly graduated undergraduates formed a team just two days before the deadline, overcame early setbacks by reordering feature engineering, leveraged score‑driven decisions, and ultimately surged to fourth place in Tencent's highly competitive algorithm contest, illustrating resilience and practical AI teamwork.
This comprehensive guide explains the full RLHF training pipeline, the mathematical foundations of reward modeling and PPO, and introduces DPO and KTO algorithms—including their implementations, advantages, limitations, and practical tuning strategies—for building aligned large language models.
This article explains a Python‑based rolling random‑forest classifier that uses a 30‑day training window and selected technical indicators to forecast whether Bitcoin’s price will rise or fall over the next seven days, detailing the methodology, code, back‑test results, and limitations.
A comprehensive overview of open‑source AI testing tools—from CodeXGLUE and AutoMLTestGen to DeepPerf and Atheris—highlights their key features, supported languages, and how they improve test efficiency, reliability, and ethical AI deployment across various domains.
This article details Dewu's shift from manual product checks to an AI‑driven, dynamically configurable audit system that improves review speed, accuracy, and scalability while reducing manual effort through rule engines, HTTP‑based algorithm calls, and extensive data analysis.
This article explores the concept of embodied intelligence, detailing its definition, historical development, various robot categories, essential technologies, and the technical, data, safety, and funding challenges facing its advancement, while also examining industry trends, policy support, and future market prospects for researchers and practitioners.
The article analyzes the poor generalization of supervised fine‑tuning (SFT) for large language models, reveals its gradient as a high‑variance inverse‑probability policy gradient, proposes a one‑line Dynamic Fine‑Tuning correction, and shows substantial gains on challenging math and offline RL benchmarks.
Chain‑of‑Thought (CoT) enables large language models to solve complex tasks by breaking problems into sequential reasoning steps, improving accuracy in mathematics, commonsense, code generation, business strategy, and medical diagnosis, while highlighting its principles, advantages, challenges, and future prospects.
In a WAIC 2025 interview, Zhu Yibo, co‑founder of Jiejie Xingchen, shares deep insights on AI infrastructure, covering its evolution, the need for tight model‑hardware co‑design, cost‑efficiency metrics, industry challenges, and future directions for large‑scale AI systems.
This article explains how Alibaba Cloud’s big‑data platform leverages Flink ML to build an intelligent‑operations service that tackles stability, cost and efficiency challenges through time‑series anomaly detection, forecasting and streaming log‑clustering, dramatically reducing latency, complexity and operational overhead.
Meituan’s retail advertising introduces a novel ‘fundraising auction’ mechanism for joint marketing, enabling multiple parties to co‑bid on the same ad slot, evolving through rule‑based, model‑based, and holistic stages, and leveraging AI‑driven models like JAMA, JRegNet, and JTransNet to boost ROI and platform revenue.
Google DeepMind's AlphaEarth Foundations integrates massive multi‑modal satellite data into unified embedding vectors, delivering 10‑meter resolution global maps, dramatically reduced storage, and superior accuracy that outperforms existing AI mapping systems by 24%, opening new possibilities for climate, agriculture, and urban planning.
Google’s T5‑based machine‑learning tool transforms code‑review comments into automatic edit suggestions, reducing the average 60‑minute per‑review effort, boosting resolution rates from 4.9 % to 7.5 %, and delivering multi‑role efficiency gains that translate into hundreds of person‑years saved annually.
This article examines how artificial intelligence is being used as a collaborative partner in experimental physics, from optimizing LIGO's sensitivity with novel optical cavities to reinventing quantum entanglement experiments and enhancing data analysis in high‑energy physics, highlighting concrete results and future potential.
This roundup highlights ten recent AI breakthroughs—including Perplexity’s Comet browser, Google’s T5Gemma series, xAI’s Grok‑4, a revamped PNG format, Bilibili’s Code‑H creator, JD’s AI social apps, ByteDance’s AI doctor, Vivo’s edge‑side multimodal model, Tencent’s payment‑enabled platform, and ByteDance’s Xverse image generator—showcasing rapid advances across browsing, modeling, multimedia, and commerce.
Terence Tao highlights that AI is transitioning from achieving headline‑making qualitative milestones, like winning IMO‑level contests, to a phase where quantitative metrics—resource costs, success rates, and scalability—must be transparently reported, urging standardized benchmarks and careful comparison between lightweight and heavyweight AI systems.
This article systematically explores the principles and mathematics behind ARIMA, XGBoost, LSTM, and Transformer models, compares their strengths and weaknesses, and demonstrates a complete end‑to‑end workflow for detecting CPU resource anomalies in a cloud service environment.
This article presents a data‑fusion framework that combines long‑term observational data and short‑term randomized experiments to identify and estimate long‑term heterogeneous dose‑response curves under continuous treatments and unobserved confounders, using reweighting, optimal transport, and balanced representation learning.
This article analyzes the fundamental differences between state space models (SSM) and Transformer architectures, highlighting their three core components, training efficiency, memory handling, tokenization impact, and empirical performance trade‑offs, and argues why SSMs can outperform Transformers on many sequence tasks.
This article introduces LCRON, a novel end-to-end training framework for cascade ranking systems that aligns training objectives with overall recall, addresses stage interaction challenges, and demonstrates significant performance gains on public benchmarks and in Kuaishou’s commercial advertising platform.
This article showcases five JD Retail Technology research papers accepted at SIGIR 2025, covering graph‑based cohort modeling, causal optimal transport post‑event modeling, an autonomous domain‑oriented relevance engine, a multi‑objective bidword generation model, and hierarchical long‑term user behavior modeling, all advancing e‑commerce CTR prediction and advertising.
This tutorial walks developers through the complete workflow of building a house‑price regression model—from problem definition, data collection and preprocessing, feature engineering, and model selection, to training, hyper‑parameter tuning, evaluation, optimization, deployment as a Flask service, and ongoing monitoring—using Python, pandas, scikit‑learn, and visualisation libraries.
This article explains the fundamentals of causal inference, distinguishes it from correlation, introduces major theoretical frameworks such as structural causal models and potential outcomes, and demonstrates practical uplift modeling techniques—including meta‑learners, double machine learning, and deep causal networks—through a financial credit‑limit use case.
JD’s algorithm engineer recounts how his team transformed e‑commerce search by developing the PODM‑MI re‑ranking framework, uncovering a novel “hourglass” bottleneck in generative retrieval, and implementing lightweight solutions that boosted diversity, relevance, and order volume, culminating in a SIGIR publication.
This article explains how modern PHP can retain information across requests using state persistence methods such as file logging, Redis caching, and machine‑learning integration, and provides concrete code examples for building intelligent response optimizers and secure, evolvable architectures.
JD’s algorithm engineer describes how a three‑layer PODM‑MI re‑ranking framework, combining Gaussian preference modeling, mutual‑information optimization, and utility‑matrix fusion, overcame the hourglass bottleneck in generative retrieval, dramatically improving search diversity, user experience, and generating over ten million additional orders.
The paper introduces ResULIC, a residual‑guided ultra‑low‑bitrate image compression framework that combines semantic residual coding, a compression‑aware diffusion model, and perceptual fidelity optimization to dramatically improve visual quality and outperform prior diffusion‑based methods on standard benchmarks.
This article presents ten concise, practical one‑line Python code snippets—ranging from loading CSV data with Pandas to building sophisticated Scikit‑learn pipelines—that streamline common machine‑learning tasks such as data cleaning, encoding, splitting, scaling, model training, evaluation, cross‑validation, and prediction.
Kuaishou has had eight cutting‑edge papers accepted at the International Conference on Machine Learning 2025, covering breakthroughs in multimodal emotion modeling, monotonic probability learning, causal effect generalization, cascade ranking, multimodal LLM alignment, ultra‑low‑rate image compression, and visual autoregressive super‑resolution, with links to each work and accompanying code repositories.
This article explains what overfitting is, introduces dropout as a regularization technique, describes how dropout randomly deactivates neurons during training and rescales outputs during inference, discusses its limitations, and outlines why large language models may use alternative strategies.
This article explains what loss functions are, outlines their three essential components, categorizes them for regression, classification, and generation tasks, reviews five classic loss functions with their noise resistance and gradient traits, and offers practical guidelines for selecting the right loss for AI models.
This roundup highlights ten recent AI developments, from Apple’s new Liquid Glass design language and OpenAI’s powerful o3‑Pro model to Meta’s generative video editor, Volvo’s AI‑adaptive seatbelt, and groundbreaking AI applications in translation, pet monitoring, and sign‑language conversion.
This article introduces the integration of the open‑source distributed AI framework Ray with Alibaba Cloud’s PAI platform, detailing its advantages, architecture, fault‑tolerance, resource management, and provides a comprehensive step‑by‑step tutorial—including configuration, command examples, and code snippets—to efficiently run Ray jobs on PAI‑DLC.
This article presents a comprehensive practice summary of building an intelligent digital‑human system, covering six core modules—LLM content generation, LLM interaction, TTS synthesis, visual driving, audio‑video engineering, and backend services—while detailing data collection, signal processing, ASR annotation, speaker clustering, model optimization (V1‑V4), evaluation metrics, and future research directions.
This article reviews the evolution of AutoML, explains its core principles, compares major Python AutoML libraries with code examples, provides a decision‑making framework and benchmark results, and offers practical guidance on selecting the most suitable tool for different machine‑learning projects.
This article explains unsupervised learning by defining its core concepts, comparing clustering, dimensionality reduction, and association techniques, and illustrating each with concrete examples—from restaurant dish grouping and housing decision simplification to convenience‑store product analysis—while offering hands‑on experiments and real‑world case studies such as Amazon, NASA, and 7‑Eleven.
This article compiles a curated list of widely used multimodal datasets—including CLEVR, Visual Genome, Pangea, Touch‑Vision‑Language, WIT, and more—providing download URLs, key statistics, and brief descriptions to help researchers quickly locate the right data for vision‑language and multimodal model training.
This article presents a collection of architecture diagrams that give a panoramic overview of general AI large‑model frameworks and showcase specialized designs for RAG knowledge bases, agriculture, e‑commerce, IoT, compliance, agent platforms, and CRM integration.
This guide shows how to install Gradio with MCP support, write a few lines of Python to expose a function as an MCP tool, launch the server, and integrate it with large language model clients such as Claude Desktop or Hugging Face Spaces.
This guide outlines a detailed AI application development learning roadmap, emphasizing the importance of clear goals and a structured path, and covering prerequisite knowledge, entry‑level, intermediate, and advanced skills, future specialization routes, and curated reference links.
This guide introduces the most popular Python libraries across web development, GUI creation, data analysis, machine learning, and automation, explaining their purposes, advantages, selection criteria, and providing concise code examples to help developers quickly adopt the right tools for their projects.
The article argues that despite rapid advances in large‑model benchmarks, the lack of real‑world data and suitable tasks creates a fundamental gap that will keep AI agents far from replacing 80% of white‑collar work for many years, making hype about imminent AGI unrealistic.
The article examines the current dominance of diffusion models in commercial video generation, contrasts them with autoregressive methods, and details how the open‑source MAGI‑1 model combines both paradigms to achieve longer, more controllable video synthesis while addressing scalability and quality challenges.
This article introduces essential AI concepts—including models, prompts and prompt templates, embeddings, tokens, structured output, and Retrieval‑Augmented Generation—explaining their meanings and relevance for effectively using Spring AI in real‑world applications.
Coupang’s AI platform replaces costly data‑copy steps with a distributed cache that automatically pulls data from a central lake, boosts GPU utilization across regions, cuts storage and operational expenses, and speeds up model training by up to 40% while simplifying deployment via Kubernetes.
This tutorial walks Java developers through using Oracle’s Tribuo library to load the UCI wine‑quality dataset, train a regression model with CART‑based random forests, evaluate its performance, save the model, and later load it for predicting wine quality scores.
The article presents an in‑depth overview of DeepInsight Copilot, an AI‑driven business intelligence product that streamlines data, information, insight, and decision‑recommendation stages, detailing its functional modules, intelligent agents, multi‑generation technical evolution, architecture, model fine‑tuning, and future challenges and solutions in data analysis.
The article explains why high‑quality, diverse data—rather than just advanced models—has become the decisive factor for enterprise AI success, outlining key dimensions of data quality, strategies for building diverse datasets, and practical steps for establishing a data‑first AI strategy.
In 2025, Python remains the leading language, and this article presents twelve indispensable libraries—ranging from data manipulation with Pandas to deep learning with TensorFlow and PyTorch—detailing their key features and providing code examples to help developers master essential tools across various domains.
This roundup highlights ten recent AI developments, from Apple's Matrix3D model that creates 3D scenes from photos, to Qwen's Deep Research assistant, Tencent's CodeBuddy 3.0, ByteDance's Seed1.5‑VL, Step Star's open‑source Step1X‑3D, Google's iOS icon refresh, Apple's eye‑tracking scrolling test, Chrome's upcoming Gemini AI assistant, Shanghai's AI Identity Ecosystem Alliance, and Kuaishou's Keling AI 2.0 topping the global video‑generation leaderboard.
This article introduces PySpark as the Python API for Apache Spark, explains Spark's core concepts and advantages, details PySpark's main components and a simple code example, compares it with Pandas, and outlines typical big‑data scenarios and further learning directions.
This article details how Vivo’s official app implements edge intelligence to dynamically rank and recommend hardware products on its homepage, covering problem abstraction, data collection, feature engineering, model design, TensorFlow‑Lite conversion, on‑device inference, and monitoring for a personalized user experience.
This article demystifies Principal Component Analysis by explaining its intuition, the role of variance, step‑by‑step visual analogies, the mathematical foundation, and a complete Python implementation using scikit‑learn, including data generation, scaling, fitting, scree plot visualization, component interpretation, and dimensionality reduction to two principal components.
This article examines how artificial‑intelligence techniques such as natural‑language processing, knowledge graphs, federated learning and automated ETL are applied across six core data‑governance scenarios—standardization, asset management, master data, data‑warehouse automation, security/privacy, and real‑time quality monitoring—showing measurable efficiency gains and business impact.
This article explains why Python has become the leading language for data analysis and machine learning, outlines the essential libraries and frameworks, provides practical code examples, describes typical application scenarios, suggests a staged learning roadmap, and forecasts future trends such as AutoML and federated learning.
The article walks through five iterative experiments using MySQL HeatWave AutoML on a golf league dataset, showing how data quality, feature selection, and over‑fitting affect predictive accuracy and emphasizing that high‑quality data is essential for reliable AI models.
This article provides a comprehensive, step‑by‑step analysis of Diffusion Policy for robot visuomotor control, covering its motivation, task characteristics, model design, dataset preparation, training pipeline, inference procedure, experimental results, and open research questions.
This article explains how to detect and handle outliers in datasets using PHP and machine-learning techniques, covering the statistical Z-Score method and the Isolation Forest algorithm, and providing code examples for both removal and replacement of anomalous values to improve data quality and model accuracy.
This article explains the Kullback‑Leibler (KL) divergence for discrete and continuous distributions, outlines its non‑negativity and asymmetry, walks through a uniform‑distribution example, provides a simple Python demonstration, and discusses key applications in variational autoencoders, reinforcement‑learning policy optimization, and other machine‑learning contexts.
At Meituan’s technical seminar, experts showcased AI-driven testing innovations—including multi‑agent UI automation, multimodal defect detection, Retrieval‑Augmented Generation for test case creation, and AI‑powered code change analysis—demonstrating how these techniques cut manual effort and boost quality in large‑scale systems handling billions of daily requests.
The article describes how a large‑scale dispatch system was re‑engineered with NVIDIA TritonServer to unify GPU‑accelerated operations‑research kernels and deep‑learning models, detailing a three‑stage architecture (in‑process, cross‑process, cross‑node), the performance, stability and memory challenges addressed, and future plans for heterogeneous GPU scaling.
The award‑winning WSDM 2025 paper reveals how recommendation models magnify popularity bias through spectral alignment of the rating matrix’s leading singular vector with item popularity, and proposes the ReSN regularization technique to mitigate this bias efficiently.
This tutorial explains how to build a Python-based automatic grading system for handwritten math equations by generating synthetic character images, training a convolutional neural network, segmenting input images using projection techniques, evaluating expressions with eval, and overlaying correctness indicators on the original image.
The article critiques AI giants’ claims of nearing human-level intelligence, highlighting research that shows current models rely on massive rule memorization rather than genuine reasoning, leading to brittleness in navigation, mathematics, and adaptability, and emphasizing the need to understand these limitations for future progress.
Meituan's AI recruitment page showcases diverse opportunities across AI infrastructure, intelligent interaction, visual intelligence, and intelligent products, featuring roles from algorithm engineers to product managers working on cutting-edge technologies including large models, intelligent agents, and multimodal systems.
