OpenAI’s newly unveiled Sora system can generate 60‑second, high‑quality videos from plain text prompts, leveraging a data‑driven physical engine trained on synthetic data from Unreal Engine 5, with contributions from researchers like Tim Brooks and Bill Peebles, marking a major AI video‑generation breakthrough.
The article introduces STAN, a multi‑task recommendation framework that leverages user lifecycle segmentation to jointly optimize CTR, stay‑time, and CVR, detailing the business context, key challenges, solution architecture, offline and online evaluations, and future research directions.
This article explains the fundamental differences between static and dynamic computation graphs, compares their memory and performance characteristics, shows how automatic differentiation works in each paradigm, and provides a step‑by‑step implementation of a toy static‑graph AutoDiff engine with Python code examples.
This article reviews the evolution from GANs to diffusion models, explains the components of Stable Diffusion—including the CLIP text encoder, VAE, and UNet—and provides step‑by‑step Python code using HuggingFace's Diffusers library to generate images from text prompts.
This article reviews distributed training techniques for large deep‑learning models, covering data parallelism, model parallelism (including pipeline and tensor parallelism), gradient bucketing and accumulation, 3D parallelism, and practical implementations such as Megatron‑LM and 360AI platform optimizations.
This article explains the principle of LoRA (Low‑Rank Adaptation) for large language models, demonstrates how to decompose weight updates into low‑rank matrices, and provides a complete PyTorch implementation that fine‑tunes a small VGG‑19 network on a custom goldfish dataset.
This article explains the fundamentals of graph neural networks and graph representation learning, outlines how graphs enhance recommendation systems, and details OPPO's practical implementation of a hybrid dual‑tower and graph sub‑network model to improve recall and ranking performance.
This article reviews the latest research from Alibaba DAMO Academy on real-world image quality problems, covering spatial, temporal, and color enhancement methods, advanced quality assessment metrics, multimodal diffusion models, and future directions toward large‑model integration and lightweight deployment.
This article walks through the core LLaMA implementation in HuggingFace’s Transformers library, detailing the inheritance hierarchy, configuration defaults, model initialization, embedding and stacked decoder layers, the RMSNorm‑based attention and MLP modules, and the forward pass that produces normalized hidden states.
This article explains how a deep‑learning‑driven AI platform tackles the complex challenges of multilingual and multi‑timezone testing for a rapidly expanding international app, detailing the architecture, data pipelines, model training, and the resulting efficiency, accuracy, and coverage gains.
Bilibili’s AI‑driven frame‑interpolation pipeline upgrades low‑frame-rate videos to smooth high‑frame-rate 1080p playback by optimizing optical‑flow models for large motion, texture and text artifacts, pruning for speed, and deploying via the BVT SDK across on‑demand and live streams.
This guide provides comprehensive techniques for optimizing PyTorch training performance and efficiency, covering all model types such as CNNs, RNNs, GANs, and transformers, and applicable across domains like computer vision and natural language processing, targeting AI/ML platform engineers, data engineers, backend developers, MLOps, SREs, architects, and machine learning engineers.
This article reviews the growing interest in causal learning within machine learning, explaining what causal learning is, its advantages over purely correlational methods, and detailing two main paradigms—learning with known causal structures and learning via causal discovery—along with examples, challenges, and future directions.
Airbnb’s new computer‑vision pipeline trains a deep‑learning aesthetic model with an EMD loss to rank photos, automatically sorts new‑listing images by design and room type, and scales real‑time similarity search via HNSW‑based ANN on AWS OpenSearch, boosting click‑through, bookings, and enabling unsupervised visual recommendations.
A recent ETH Zurich paper shows that standard Transformer blocks can be drastically simplified by removing residual connections, LayerNorm, projection and value parameters, and even MLP sub‑block components, achieving up to 16% fewer parameters and comparable training speed and downstream performance on both GPT‑style decoders and BERT models.
The article showcases a series of humorous and surprising AI‑generated image expansions from Douyin, explains the underlying outpainting technology, and discusses why such tools are both entertaining and useful despite occasional odd results.
This article details HuoLala's journey from a weekly release cycle to a cloud‑based record‑and‑replay mobile UI automation platform, covering background challenges, industry analysis, technical design—including deep‑learning based control detection, SIFT image matching, script generation, playback handling, and platform features—while demonstrating significant testing efficiency gains and future AI‑driven enhancements.
This guide explains how to implement reverse image search by extracting visual features with a multilingual CLIP model, storing the vectors in Elasticsearch 8.x, and using its k‑NN plugin to retrieve similar images, covering architecture, tools, code snippets, and results.
This article reviews recent progress in advertising recommendation technologies, covering deep learning‑based ranking, sequence modeling, self‑supervised learning, online and reinforcement learning, multimodal recommendation, and fairness, and details four key breakthroughs—data‑driven incremental learning, dynamic group parameter modeling, bilateral interactive graph convolution, and a relation‑aware diffusion model for poster layout generation, along with experimental results and future challenges.
Learn step-by-step how to create, compile, train, evaluate, and deploy convolutional neural networks in Python using TensorFlow and Keras for general image classification and a practical face‑recognition example, complete with code snippets and data‑preprocessing techniques.
This article explains the Transformer model, comparing it with RNNs, detailing its encoder‑decoder structure, multi‑head and scaled dot‑product attention, embedding layers, feed‑forward networks, and the final linear‑softmax output, supplemented with diagrams and code examples.
The article analyzes the limitations of traditional speech‑driven lip‑sync methods and explains how Wav2Lip introduces a pretrained multi‑frame expert sync discriminator, a two‑stage GAN training pipeline, and a specialized generator architecture to produce high‑quality, audio‑aligned facial videos.
The article surveys sequential recommendation models from early non-deep approaches like FPMC, through RNN-based GRU4Rec and CNN-based Caser, to Transformer-based methods such as SASRec, BERT4Rec, TiSASRec, and recent contrastive-learning techniques, recommending SASRec or its variants for production use.
Renowned AI pioneer Geoffrey Hinton cautions that rapidly advancing artificial intelligence may surpass human control in as little as five years, highlighting self‑modifying code, the "black‑box" problem, and the urgent need for robust safety regulations.
This article reviews animal recognition technology, covering its background, basic principles, image‑processing, feature extraction, machine‑learning and deep‑learning methods, dataset construction, preprocessing, and feature‑selection techniques, and provides Python code examples for implementing CNNs and traditional classifiers.
This article explains the principles and structures of LSTM networks, introduces the ELMO contextual embedding model with its two‑stage pre‑training and downstream usage, and provides an overview of the Transformer architecture, highlighting their roles in modern NLP tasks.
This article explains the mathematical definition of convolution, walks through discrete and continuous examples, demonstrates its use in signal smoothing with moving averages, and surveys its wide-ranging applications in signal processing, communications, computer vision, seismology, medical imaging, and statistics.
The DataFunSummit2023 online conference brings together experts from Tencent and Kuaishou to present cutting‑edge research on causal inference for large‑scale A/B testing, including deep‑learning‑based multi‑experiment effect estimation, a distributed causal inference framework (Fast‑Causal‑Inference), and strategies for evaluating long‑term policy impacts.
The paper introduces QIN, a query‑driven user interest network that combines a Relevance Search Unit and a Fused Attention Unit to effectively leverage full‑history user behavior for personalized search, demonstrating significant performance gains in offline benchmarks and online A/B tests.
This article compiles Meituan's cutting‑edge computer‑vision research and engineering achievements—including CVPR award‑winning segmentation, YOLOv6 releases, GPU inference optimizations, the Food2K dataset, and numerous paper digests—to provide practical insights for visual AI practitioners.
This article examines the challenges of video copyright protection, critiques traditional visible and invisible watermark methods, and presents a deep‑learning based AI digital watermark solution that balances invisibility and robustness, detailing its network architecture, degradation layer, loss functions, block encoding, anchor calibration, and large‑scale experimental results.
This article presents a comprehensive case study of NIO's Power swap‑station ecosystem, detailing the business context, key forecasting challenges, the evolution from classical statistical models to deep‑learning architectures with specialized embeddings, and the practical outcomes and future plans for improving prediction accuracy.
This article introduces Social4Rec, a video recommendation algorithm that tackles user cold‑start problems by extracting and integrating social interest information through coarse‑ and fine‑grained interest extractors, attention‑based fusion, and extensive offline and online experiments demonstrating significant CTR improvements.
BILIVQA is Bilibili’s deep‑learning, no‑reference video quality assessment system that trains on a proprietary 5,000‑video UGC dataset, extracts spatial and temporal features via MobileNet‑V2 and X3D, uses mixed‑dataset regression for strong generalization, and deploys a GPU‑optimized TensorRT pipeline with percentile‑based scoring for reliable quality monitoring and downstream applications.
This article surveys the development of natural language processing from early rule‑based systems through statistical n‑gram models, neural language models, RNNs, LSTMs, ELMo, Transformers and BERT, and then details the architecture, training methods, advantages and limitations of the GPT‑1, GPT‑2, GPT‑3, ChatGPT and GPT‑4 models, concluding with a discussion of future challenges and references.
DVIS introduces a decoupled video instance segmentation framework that splits the task into segmentation, tracking, and refinement modules, achieving state-of-the-art performance across VIS, VPS, and VSS benchmarks while maintaining low computational overhead, and demonstrates robustness in both online and offline settings.
FlashAttention‑2 is an IO‑aware exact attention algorithm that cuts GPU HBM traffic through tiling and recomputation, optimizes non‑matmul FLOPs, expands sequence‑parallelism and warp‑level work distribution, delivering up to 2× speedup over FlashAttention, near‑GEMM efficiency, and enabling longer‑context Transformer training and inference for AIGC with fastunet and negligible accuracy loss.
The paper introduces ROPE, the first large‑scale cross‑domain product recognition dataset covering detail pages, short videos and live streams, and proposes COPE, a dual‑tower multimodal model that learns unified product embeddings using contrastive and classification losses, achieving superior retrieval and few‑shot classification performance across domains.
This paper introduces LPR4M, a 4‑million‑pair multimodal dataset for livestreaming product recognition, and proposes the RICE model that combines instance‑level contrastive learning with patch‑level cross‑view semantic alignment, demonstrating state‑of‑the‑art performance on both LPR4M and MovingFashion benchmarks.
The paper presents an AI‑driven invisible video watermarking system that combines a convolutional encoder/decoder with SE blocks, a simulated‑JPEG degradation layer, multi‑term loss, block‑wise processing, anchor‑based alignment and redundancy voting, achieving high visual fidelity and robust recovery after double‑compression in large‑scale platforms like Bilibili.
This article details how AutoHome's homepage pop‑up leverages precise targeting, extensive feature engineering, and multi‑stage DeepFM‑based models with attention and LHUC modules to accurately identify car‑buying users, improve vehicle‑series recommendations, and achieve a 355% conversion rate increase.
From Turing’s 1950 imitation game to GPT‑4’s multimodal vision‑language capabilities, the field has evolved from simple rule‑based programs like ELIZA and PARRY, through statistical learning and the 2017 Transformer breakthrough, to large-scale generative models that achieve fluent conversation yet still grapple with hallucination and true understanding.
This article introduces the open‑source Pai‑Megatron‑Patch tool from Alibaba Cloud, explains its non‑intrusive patch architecture, enumerates supported models and features such as weight conversion, Flash‑Attention 2.0, FP8 training with Transformer Engine, and provides detailed command‑line examples for model conversion, pre‑training, supervised fine‑tuning, inference, and RLHF reinforcement learning pipelines.
This article reviews two recent AI research papers from NetEase Cloud Music Lab: TG‑Critic, a timbre‑guided, reference‑free singing evaluation model that classifies vocal performance using only audio, and TrOMR, a Transformer‑based end‑to‑end polyphonic optical music recognition system that improves note‑sequence prediction and dataset realism.
This tutorial walks you through implementing a full Transformer model in PyTorch, starting from basic linear‑regression code, adding attention mechanisms, multi‑head attention, encoder‑decoder architecture, training loops, and inference, all reinforced with practical debugging tips.
This article reviews the fundamentals, feature extraction methods, evaluation metrics, and common datasets of content‑based image retrieval (CBIR), discussing traditional low‑level features, local descriptors, unsupervised and supervised learning approaches, and recent deep‑learning models for improving retrieval performance.
DeepQueueNet combines discrete‑event and continuous simulation with deep neural networks to deliver highly accurate, generalizable, and GPU‑scalable network performance estimates at packet‑level granularity, outperforming existing DNN‑based estimators across diverse topologies and traffic scenarios.
This article introduces the PanoViT model, a vision‑transformer‑based approach for indoor layout estimation from panoramic images, covering its research background, architectural components, experimental results on public datasets, and step‑by‑step usage within ModelScope.
This article introduces neural style transfer, explains its underlying principles using VGG19 feature extraction, content and style loss definitions, and provides a complete PyTorch implementation with code for loading images, extracting features, computing Gram matrices, and optimizing the output image.
This article explains the background, core principles, preprocessing, feature extraction, matching algorithms, and practical application scenarios of face recognition search, and provides detailed reference implementations with Java and OpenCV code examples for building a complete system.
The Multi‑Granularity Attention Model (MGAM) improves group recommendation by extracting subset, group, and superset preferences through hierarchical attention and graph neural networks, fusing them via self‑attention, and achieves state‑of‑the‑art offline results and a 1.2% online CTR lift in Alibaba’s local‑life services.
An end‑to‑end AI platform integrates feature processing, model training, deployment, and decision orchestration across offline and online layers, leveraging automated pipelines such as AutoML (feature engineering, hyper‑parameter optimization, neural architecture search) built on Ray Tune and NNI, which have already boosted CTR in real‑world advertising and aim to make every user an algorithm engineer.
The paper presents Action Sensitivity Learning (ASL), a framework that models frame‑wise importance at both class‑level (via learnable Gaussian distributions) and instance‑level (using quality scores), integrates these weights into classification and regression losses, adds a contrastive InfoNCE term, and achieves state‑of‑the‑art temporal action localization performance across six benchmark datasets.
AI painting leverages deep‑learning models to analyze and recreate color palettes, enabling machines to mimic famous artists, generate novel hues, and express emotions, while also facing challenges such as limited creativity, human‑AI interaction, and fine‑detail rendering.
The paper introduces StEN, a spatiotemporal-enhanced network for CTR prediction in location-based services, combining static spatiotemporal feature activation, dynamic preference activation, and target attention, achieving state-of-the-art offline results and a 1.6% CTR lift in online tests.
This article provides a comprehensive overview of OCR technology, detailing the evolution from traditional hand‑crafted methods to modern deep‑learning approaches, describing image preprocessing, text detection and recognition pipelines, summarizing classic machine‑learning algorithms, and presenting a practical OpenCV implementation with Python code.
This article provides a comprehensive overview of Optical Character Recognition (OCR), covering its definition, historical development, classification, real‑world applications, technical pipeline, common challenges, mitigation strategies, popular datasets, model performance comparisons, and leading open‑source platforms.
The paper introduces PEPNet, a plug‑and‑play network that tackles the domain‑seesaw and task‑seesaw problems in multi‑scenario recommendation by using a gated personalization module (GateNU) together with embedding‑level (EPNet) and parameter‑level (PPNet) personalization, and demonstrates its superiority through extensive offline and online experiments on Kuaishou data.
This article explains the challenges of processing sequential data, introduces LSTM as a solution to long‑term dependency problems in RNNs, details its cell state and gate mechanisms, showcases its architecture, and provides Python code examples for time‑series forecasting using Keras.
This article provides a comprehensive overview of deep neural network families, introducing twelve major architectures—including Feedforward, CNN, RNN, LSTM, DBN, GAN, Autoencoder, Residual, Capsule, Transformer, Attention, and Deep Reinforcement Learning—explaining their principles, structures, training methods, and offering Python/TensorFlow/PyTorch code examples.
This tutorial demonstrates how to download the Fashion‑MNIST dataset, build a four‑layer fully connected neural network with PyTorch, and train it using loss functions, Adam optimizer, learning‑rate strategies, and Dropout to achieve high‑accuracy multi‑class image classification.
This article explains the Slide-Transformer paper, describing how the proposed Slide Attention replaces inefficient Im2Col‑based local attention with depthwise convolutions and a deformable shift module, achieving high efficiency, flexibility, and hardware‑agnostic performance for Vision Transformers.
This article provides a comprehensive tutorial on building the core components of a Transformer model—including multi‑head attention, layer normalization, feed‑forward networks, encoder and decoder layers—and assembles them into a complete PyTorch implementation, supplemented with explanatory diagrams and runnable code.
This article introduces ten widely used Python libraries—NumPy, Pandas, Matplotlib, Scikit‑learn, Requests, Beautiful Soup, Selenium, Pygame, Flask, and PyTorch—each accompanied by a concise real‑world project and complete code examples to help readers understand and apply them effectively.
This article provides an in‑depth, English‑language overview of Vision Transformer (ViT), covering its Transformer‑based architecture, patch‑to‑token conversion, token and position embeddings, fine‑tuning strategies such as 2‑D interpolation, experimental results versus CNNs, and the model’s broader significance for multimodal AI research.
This paper introduces HairStep, a novel intermediate representation combining Strand Maps and Depth Maps, and demonstrates how it reduces domain gap and improves single‑view 3D hair reconstruction accuracy across multiple algorithms, supported by new annotated datasets (HiSa, HiDa) and fair evaluation metrics.
This article explains the architecture, data layers, prediction modules, and deep‑learning framework behind Gaode’s driving ETA service, detailing how static speed profiles, linear models, and the H‑STGCN model combine to forecast travel times and evaluate their accuracy.
This article introduces Alibaba Cloud's PAI‑DLC, a cloud‑native deep learning platform that integrates machine‑learning capabilities, containerized services, AI‑aware scheduling, GPU virtualization, elastic training with EasyScale, data access, and observability, and discusses its architecture, key features, and future directions.
This article explores the rapid evolution of Baidu's semantic search models, the large GPU consumption they entail, and how extensive INT8 quantization, sensitivity analysis, calibration data augmentation, hyper‑parameter auto‑tuning, and advanced methods like Quantization‑Aware Training and SmoothQuant dramatically improve inference performance while preserving business metrics.
ShaderNN 2.0 is a lightweight mobile deep learning inference engine supporting OpenGL and Vulkan, offering texture‑based zero‑copy I/O, hybrid shader implementation, and achieving significant latency and power reductions versus TensorFlow Lite and MNN, thereby enabling real‑time graphics‑AI tasks such as style transfer, denoising, super‑sampling, and Stable Diffusion on smartphones.
To eliminate data fragmentation, feature inconsistencies, and multi‑language implementation challenges, Bilibili built a unified real‑time advertising feature platform that aligns offline, hourly, and online pipelines via a shared C++ library and JNI, boosting CTR prediction accuracy, cutting training costs, and increasing ad revenue by over 1 %.
Amid tightening financial regulations, ICBC's software team proposes a multimodal AI anti‑fraud framework that combines image, video, and structured data to detect deep‑fake, mask, and forged‑document attacks, enriches verification with cross‑modal cues, and outlines future expansion to text and speech modalities.
This article explains the concept of image‑based search, why it matters, and provides a step‑by‑step guide to implement image search using Elasticsearch 8.x, feature‑extraction libraries, and the multilingual CLIP‑ViT‑B‑32 model, including code snippets and architecture overview.
GoldMiner, a new system from Alibaba Cloud’s PAI platform, elastically scales deep learning data pre‑processing pipelines, dramatically improving training performance up to 12.1× and GPU cluster utilization by 2.5×, and its underlying research was accepted at SIGMOD 2023.
At ICASSP 2023, Kuaishou’s audio team presented a low‑latency hierarchical fusion network for full‑band acoustic echo cancellation, detailing its multi‑stage design, asymmetric windowing, loss functions, training strategy, and achieving second place in the non‑personalized AEC Challenge, with real‑world deployment results.
This article introduces the Paraformer model released by Alibaba DAMO Academy on ModelScope, detailing its non‑autoregressive architecture, training strategies, performance on benchmark datasets, and step‑by‑step guidance for fine‑tuning and deploying the model using FunASR and ModelScope pipelines.
The article chronicles the career of Chinese AI pioneer Huang Xuedong, detailing his education, rise at Microsoft, leadership of Azure AI, groundbreaking human‑level speech recognition breakthroughs, the engineering feats behind them—including a ten‑network model and the CNTK framework—and his recent move to Zoom.
In 2023 the Network Intelligent Research Center celebrated its largest PhD graduating class—seven scholars whose dissertations span deep‑vision hand‑gesture estimation, multi‑scenario network transmission, graph alignment, interactive streaming, knowledge‑defined networking, wireless body‑area networking, and more—showcasing significant AI‑driven advances and high‑impact publications.
CF‑Font introduces a content‑fusion module that linearly mixes base‑font content features using a font‑level distance metric, combined with iterative style refinement and a projection character loss, achieving state‑of‑the‑art few‑shot Chinese font generation that outperforms prior methods by over 5% on L1 and FID and is already used to create proprietary Alibaba‑Mama fonts.
This article reviews the evolution of face detection, covering the Widely‑Face dataset, major research directions such as feature fusion, label assignment, auxiliary supervision, anchor‑free methods, NAS‑based designs, summarizes key papers from S3FD to MogFace, introduces ModelScope implementations, and outlines future challenges and opportunities.
This article introduces Huawei's Domain‑Specific Ascend AI architecture, detailing its four‑layer full‑stack design, the five‑layer abstract and three‑layer logical structures of the CANN heterogeneous computing framework, and the AscendCL programming interface with its advantages and application scenarios.
This guide details the end‑to‑end process of installing GPU drivers, setting up a Python environment, deploying the open‑source Alpaca‑LoRA model, fine‑tuning it with Chinese data on a multi‑GPU server, and performing inference, while highlighting practical challenges and performance observations.
This article provides a comprehensive analysis of the Transformer model behind ChatGPT, covering its origin, core mechanisms such as embedding, positional encoding, self‑attention, multi‑head attention, a step‑by‑step translation example, and the broader implications for AI research and industry.
This article explains what ChatGPT is, how it builds on natural-language-processing and the Transformer-based GPT series, details its model-size growth, architectural enhancements, multilingual support, and walks through the tokenization-to-generation pipeline that enables coherent AI-driven conversations.
This article presents Weibo's comprehensive multimedia content understanding pipeline, covering video summarization techniques, quality assessment models, OCR advancements, video embedding strategies, and the performance benefits of CV‑CUDA acceleration, while highlighting real‑world applications and engineering trade‑offs.
AdaSparse introduces an adaptive sparse network that learns a dedicated sub‑network for each advertising scenario, balancing shared and specific knowledge while keeping computational cost low, and achieves +4.63% CTR and -3.82% CPC improvements in Alibaba’s external ad system, as validated on both public and massive production datasets.
The paper introduces SASS, a scenario-adaptive self-supervised recommendation model that uses contrastive pre-training and multi-layer gating to expand global samples and transfer scene-aware parameters, enabling a single model to deliver personalized recommendations across diverse Taobao ‘SuoSuo’ scenarios while mitigating data sparsity and cross-domain challenges.
This article explains the Transformer model—from its encoder‑decoder structure and self‑attention mechanism to multi‑head attention, positional encoding, residual connections, training loss, and inference strategies—providing a clear, visual walkthrough for readers new to modern NLP architectures.
This article describes the design, algorithmic improvements, and engineering performance optimizations of Bilibili's massive video duplicate detection (collision) system, covering challenges of low‑edit‑degree reposts, two‑stage retrieval, self‑supervised feature extraction, GPU‑accelerated preprocessing, and the resulting gains in accuracy and throughput.
Richard Sutton’s “The Bitter Lesson” argues that over the past seven decades the most powerful driver of AI progress has been general‑purpose compute and large‑scale search, which consistently surpasses methods that rely on human‑engineered knowledge across domains such as chess, Go, speech recognition, and computer vision.
This tutorial walks through the complete process of fine‑tuning a Vision Transformer (ViT) model for 14‑class flower image classification on ModelScope, covering dataset preparation, model loading, training configuration, evaluation, and inference with practical code examples.
Bilibili built a massive video‑duplicate detection platform that trains a self‑supervised ResNet‑50 feature extractor, removes black borders, and uses a two‑stage ANN‑plus‑segment‑level matching pipeline accelerated by custom GPU decoding and inference, boosting duplicate rejection 7.5×, recall 3.75×, and cutting manual misses from 65 to 5 per day.
This article introduces OneFlow Coop, a memory‑optimization technique that jointly optimizes dynamic‑graph recomputation strategies and GPU memory allocation by analyzing existing DTR limitations, proposing recomputable in‑place, op‑guided tensor allocation, and layout‑aware eviction modules, and demonstrating superior experimental results.
In this talk, AI expert Wu Jun explains why ChatGPT has caused widespread fear, traces the historical development of language models from the 1970s to today, clarifies the massive computational and data requirements, and discusses the real impact and opportunities of large‑scale AI systems.
This article explores how ChatGPT’s remarkable abilities stem from the Transformer architecture, reinforcement learning from human feedback, and the insights presented in the fourth edition of "Artificial Intelligence: A Modern Approach," highlighting key AI milestones and technical foundations.
This article introduces the basic architectures of recurrent neural networks (RNN), LSTM, and GRU, explains PyTorch APIs such as nn.RNN, nn.LSTM, nn.GRU, details their parameters, demonstrates code examples for building and testing these models, and provides practical insights for deep learning practitioners.
In a GTC fireside chat, Nvidia CEO Jensen Huang and OpenAI co‑founder Ilya Sutskever discuss GPT‑4's multimodal advances, the evolution of deep learning from early neural networks to large‑scale models, the pivotal role of GPUs and datasets like ImageNet, and their vision for more reliable, scalable artificial intelligence.
This article chronicles Ilya Sutskever’s two‑decade rise from a young researcher to a leading figure in artificial intelligence, highlighting his early mentorship, breakthroughs in image recognition, language translation, the founding of OpenAI, and the development of GPT and DALL‑E models.
This article provides a comprehensive overview of deep UPLIFT models, covering their fundamentals, key technical challenges such as confounding bias and inductive bias, the evolution of meta‑learner and deep architectures, and practical case studies in financial technology marketing.
The article provides a detailed overview of PyTorch 2.0, highlighting its stable and beta features such as torch.compile, accelerated transformers, MPS backend, new quantization support, and prototype parallelism tools, while emphasizing performance improvements for dynamic shapes, distributed training, and CPU/GPU inference.
