The article explains why large language models generate hallucinations—due to data errors, training conflicts, and inference uncertainty—and outlines data‑cleaning, model‑level feedback, knowledge augmentation, constraint techniques, and post‑processing methods such as the “Truth‑seeking” algorithm to mitigate the issue.
This article reviews Alibaba DAMO Academy's research on information extraction, covering background concepts, PLM-era extraction paradigms, few‑shot extraction techniques, and the emerging LLM‑era approaches, while also sharing practical insights, benchmark results, and future directions.
The article surveys the rise of LLM‑powered AI agents, defining them as LLM + memory + planning + tool use, contrasting fully autonomous agents with human‑guided autopilot/copilot variants, outlining their benefits, risks such as hallucinations and unsafe actions, and urging modular frameworks and oversight for reliable enterprise deployment.
The 2023 Pujiang Innovation Forum highlighted the rapid rise of generative AI, its associated security and privacy risks, and presented Ant Group's multi‑stage, multi‑layered approach—including data, training, and inference controls and three core defense technologies—to achieve safe, reliable, and open knowledge sharing in the era of large language models.
This article analyzes upgrade paths of major LLMs—ChatGLM, LLaMA, Baichuan—detailing performance, context length, and architectural changes, then examines essential capabilities, data cleaning, tokenizer and attention design, and offers practical guidance for balanced scaling and efficient model construction.
This article reviews the development of AIGC and generative AI technologies—including image, text, and molecular generation—explains key model advances such as diffusion and large language models, discusses their impact on drug discovery, and outlines current challenges, opportunities, and future directions.
This article examines how large language models and knowledge graphs complement each other, discussing their respective strengths, integration techniques such as prompt engineering and knowledge editing, and outlining future research directions for building large knowledge models that combine linguistic understanding with structured knowledge representation.
A leaked Google internal document titled “We have no moat, OpenAI also has none” reveals that both companies are losing the AI arms race to rapidly advancing open‑source models, which achieve comparable performance at a fraction of the cost, prompting a strategic rethink for Google.
This article examines how the rapid rise of generative AI reshapes application engineering by outlining AI's core characteristics, the challenges developers face, the evolution of prompt and chain-of-thought techniques, the emergence of agents and tool integration, and the future direction toward AI‑centric computing architectures.
The article examines how large‑language models, test‑case generation technologies, and model‑driven testing are reshaping software testing, discusses the challenges of applying AI to testing, and outlines future directions and skill sets for professionals seeking to leverage AI in quality assurance.
In this talk, Zhang Pengtao, a Sina Weibo technical expert with a Ph.D. in computer applications, explores how the strong memory capabilities of NLP large language models inspire the design of independent memory mechanisms for recommendation systems, covering model concepts, HCNet & MemoNet, experimental results, and practical takeaways for enhancing recommendation model performance.
This article details how Zhihu's internal Bridge platform integrates large language models for business analysis, knowledge taxonomy, natural‑language‑to‑filter conversion, and ad‑hoc data queries, describing the workflow, technical hurdles, iterative improvements, and future directions.
This article presents a comprehensive overview of data engineering practices, pre‑training data composition, automated model evaluation techniques, and the synergistic use of knowledge graphs within large‑scale AI model research, highlighting pipelines, quality criteria, and practical case studies.
The article explains Bilibili's self‑developed customer‑service platform, describing its modular architecture, core workflows, and implementation of features such as intelligent QA with Faiss vector search, Redis‑based seat scheduling, a robust workstation, permission control, and exploration of large language models, highlighting improvements in interception rate, satisfaction, and handling time.
This article provides programmers with a comprehensive overview of large language models—including their evolution, core concepts, data pipelines, model architectures, training techniques such as 3D parallelism, supervised fine‑tuning, RLHF, open‑source recipes, and emerging application ecosystems—while also highlighting current challenges and future directions.
This article explains the fundamentals of modern large language models, outlines the top five most powerful LLMs—including GPT‑4, Claude 2, Llama 2, Orca, and Cohere—and provides practical guidance on selecting and applying them across business and development use cases.
The article details how Baidu's Fengchao team leverages large language models, including a transition from DNN embeddings to ERNIE, introduces multi‑level tokenization and discrete core‑word inputs, and applies prompt learning and AIGC techniques to improve search advertising satisfaction and industry‑specific relevance modeling.
This report, based on Professor Xiao Yanghua’s presentation at the World AI Conference, examines why vertical domains need general large models, outlines their key capabilities such as open‑world understanding, combinatorial innovation, evaluation, complex instruction execution, task planning, and symbolic reasoning, and discusses current limitations and optimization strategies for domain‑specific deployment.
The article examines fundamental challenges of applying large language models to software engineering—such as scale limits, lack of abstract reasoning, hidden tacit knowledge, and maintenance difficulties—and proposes practical recommendations for integrating AI with disciplined development practices.
This article explains the evolution from traditional machine learning to the current large‑model era, introduces LMOps concepts and key technologies, compares them with MLOps, and showcases Baidu Cloud's Qianfan platform as a practical solution for building, deploying, and managing large language models in industry.
This article shares recent AIGC practices in e‑commerce, detailing product copy generation using GPT‑based models, image creation with Stable Diffusion, the evolution of large language models, technical solutions, experimental results, and future opportunities for AI‑driven automation in online retail.
Baidu’s chief architect Li Shuanglong outlined how AIGC, driven by advanced large‑language and multimodal models, is already powering commercial tools such as automated copywriting, 2D digital‑human video creation and lead‑generation chatbots, while emphasizing future progress in engineering scalability, algorithmic fidelity, data quality, and scenario‑focused applications.
This article provides a detailed overview of large language models (LLMs), tracing their evolution from statistical and neural language models to modern pre‑trained transformers, discussing scaling, training, adaptation, utilization, evaluation methods, available resources, and outlining current challenges and future research directions.
This comprehensive guide explores prompt engineering for large language models, covering its background, fundamental concepts, prompt formats, construction principles, advanced techniques like few‑shot, zero‑shot, and chain‑of‑thought prompting, as well as practical examples, evaluation metrics, and future directions.
This article explains the fundamentals of vector retrieval, its role in enhancing large language models through embedding and prompt engineering, and details the algorithms, system architecture, and Baidu's engineering practices for building high‑performance vector databases.
The interview with ZhongAn Insurance CTO Jiang Jiyun discusses how the company leverages AIGC technologies such as large language models, embeddings, and prompt engineering to enhance marketing, intelligent customer service, and data security, while highlighting practical challenges and best practices for AI adoption in the insurance sector.
This article introduces LangChain—a modular framework for constructing large‑language‑model applications—covering its core components, asynchronous support, prompt engineering, memory handling, chain and agent workflows, token considerations, embedding techniques, and a step‑by‑step Python example that culminates in a Gradio‑based conversational chatbot.
The article explains how LoRA and its 4‑bit QLoRA extension dramatically reduce trainable parameters and GPU memory for fine‑tuning large language models, while GPTQ post‑training quantization compresses weights for cheap inference, and shows how KubeAI integrates these techniques into a one‑click workflow for 7 B, 13 B, and 33 B models from data upload to API deployment.
This article analyzes the Prophet framework, which leverages a traditional VQA model to generate answer candidates and in‑context examples that prompt GPT‑3, achieving state‑of‑the‑art performance on the challenging OK‑VQA and A‑OKVQA benchmarks.
In this expert roundtable, leading researchers and engineers discuss the current state of recommendation systems, how large language models can reshape the field, the technical and practical challenges involved, and practical advice for practitioners looking to adopt AI‑driven personalization solutions.
This article presents OPPO Research Institute's recent advances in large‑model AI, detailing the CHAOS pretrained model that topped the CLUE leaderboard, the knowledge‑enhanced training pipeline, and the GammaE model for multi‑hop reasoning over knowledge graphs, together with experimental results and practical training tips.
This article explains what GPT is, how it is trained, its strengths and limitations, the various risks it poses, and provides practical guidance on safely adopting large language models like ChatGPT within development teams, including code‑level analysis examples.
Meta’s chief AI scientist Yann LeCun warned that current large language models still fall short of human and even dog intelligence, citing their lack of real‑world understanding, while Meta unveiled three new generative AI models—Voicebox for speech, MusicGen for music, and I‑JEPA for image reasoning—showcasing both progress and remaining limitations.
This article explains the fundamentals of recommendation systems, their digital representation, how ChatGPT and large language models are applied to enhance recommendation performance, and highlights emerging trends such as conversational recommendation and a recommended book on the subject.
In a Stanford eCorner interview, Ilya Sutskever, chief scientist of OpenAI and architect of GPT, reflects on the history of large language models, compares AI to human cognition, debates open‑source versus closed‑source models, discusses regulation, training strategies, and offers guidance for aspiring AI researchers.
DataFun will host the DataFun Summit 2023 on June 17‑18, featuring three chairs and eight presenters who will discuss core topics such as large language model research, multimodal generation, reinforcement learning, tool learning, distributed training, and industry applications, with free registration via QR code.
The article recaps a Beijing offline salon where experts and open‑source contributors discussed ChatGPT desktop applications, the development and deployment of ChatGPT‑Next‑Web, large‑language‑model challenges, the VisualGLM multimodal model, and product design considerations, providing technical insights and community perspectives on AI advancements.
This article explains prompt engineering for large language models like ChatGPT, covering its definition, essential techniques such as diverse prompting strategies, problem restatement, background provision, gradient prompting, example inclusion, role‑playing, and the importance of systematic experimentation and quantitative evaluation to achieve high‑quality, task‑specific AI outputs.
Leading experts discuss how causal inference intersects with large language models, exploring opportunities, challenges, industry applications, and future research directions, while sharing personal journeys into causal reasoning and offering practical advice for practitioners.
Researchers from Peking University and Huawei present a multiscale positive‑unlabeled learning framework that significantly improves detection of AI‑generated short and long texts, addressing the difficulty of distinguishing AI‑written content from human writing and outperforming existing baselines on multiple benchmarks.
Recent reports indicate a noticeable decline in ChatGPT Plus’s GPT‑4 performance, especially in coding accuracy, prompting speculation about model scaling pain, AI alignment trade‑offs, and OpenAI’s GPU‑limited roadmap that includes cheaper models, longer context windows, finetuning, and multimodal extensions.
An online CSA seminar on May 30 examined ChatGPT’s security impact, presenting a whitepaper and four AI‑security interaction dimensions, while experts discussed telecom‑operator security‑GPT models, safe vertical‑domain large‑model training, and future industry implications.
The panel examines how large language models like ChatGPT reshape software engineering, covering safety risks, model training stages, prompt‑engineering challenges, teaching reforms, and future human‑AI collaboration, while weighing technical trade‑offs and practical solutions.
This article explains why effective prompt design—covering system messages, few‑shot learning, non‑dialogue scenarios, explicit instructions, output shaping, syntax cues, task decomposition, chain‑of‑thought, and real‑world context—is essential for reliable large language model results and provides practical examples and tips.
The article explains how ChatGPT’s surprising step‑by‑step reasoning, known as Chain of Thought, emerged as a technical breakthrough, links it to model scaling, cognitive System 1/2 theory, and the influence of code data in training large language models.
The article critically examines GPT‑4 and similar transformer models, highlighting persistent hallucinations, outdated knowledge, insufficient domain coverage, lack of planning and memory, and proposes architectural extensions inspired by fast‑slow thinking and differentiable modules to overcome these fundamental constraints.
This article provides a comprehensive overview of the development of large language models, reviewing classic papers from GPT‑1 through GPT‑4, discussing open‑source implementations such as LLaMA, Alpaca, GLM, and ChatGLM, and analyzing training methods, datasets, and future research directions.
The article details how Kuaishou’s multimodal AI research, including its K7 trillion‑parameter model and VLUA algorithm, partners with Renmin University’s Gaoling AI Institute to launch a joint lab, produce cutting‑edge papers such as WebBrain and ChatImg, and advance recommendation and search technologies across the short‑video ecosystem.
This presentation introduces the Mengzi lightweight model technology stack, covering large‑scale pre‑training, motivations for lightweight models, detailed techniques such as knowledge and sequence‑relation enhancement, training optimization, model compression, retrieval‑augmented pre‑training, multimodal extensions, open‑source releases, and real‑world applications.
This article provides a comprehensive overview of major large language models—including GPT series, T5, LaMDA, LLaMA, BLOOM, and others—detailing their architectures, parameter scales, open‑source status, and the evolution of instruction‑fine‑tuning techniques that improve zero‑shot and few‑shot performance.
This article reviews the emergence phenomena observed in large language models, explains how model scale, in‑context learning and chain‑of‑thought prompting contribute to sudden performance gains, discusses small‑model alternatives, and explores the relationship between emergence and the training‑time Grokking effect.
The article examines the rapid proliferation of large language models, weighing both the opportunities for experimentation and the drawbacks of noise, and argues that establishing authoritative Chinese LLM evaluation benchmarks is essential to guide meaningful progress in the field.
The article surveys major large language models—including GPT‑3, T5, LaMDA, Jurassic‑1, MT‑NLG, Gopher, Chinchilla, PaLM, U‑PaLM, OPT, LLaMA, BLOOM, GLM‑130B, and ERNIE 3.0 Titan—explains their architectures, scaling trade‑offs, and then details instruction‑fine‑tuned variants such as T0, FLAN, GPT‑3.5, ChatGPT, GPT‑4, Alpaca and ChatGLM, providing references for further study.
In this interview, Jina AI’s founder Shao Han examines the relationships among generative, perceptual, and decision AI, compares single‑modal and multimodal approaches, discusses large language model development, and evaluates the impact of ChatGPT on search and future AI commercialization.
Microsoft open‑sourced DeepSpeed‑Chat, a toolkit that streamlines the end‑to‑end training and inference of ChatGPT‑like large language models using RLHF, delivering up to fifteen‑fold speedups and dramatically lower costs, even on a single GPU.
This article examines how large language models such as GPT‑1 through GPT‑4 succeed largely due to high‑quality, large‑scale training data, and explains the Data‑centric AI framework—training data development, inference data development, and data maintenance—while discussing prompt engineering, data‑driven improvements, and future trends in AI.
This study systematically evaluates ChatGPT and other large language models on their ability to answer commonsense questions, assess their knowledge awareness, and utilize generated knowledge for reasoning, revealing strong QA performance but notable gaps in social and temporal commonsense and in leveraging contextual knowledge.
Amid the 2023 AI boom, veteran entrepreneurs, former tech executives, and academic teams in China are racing to launch large‑model ventures, each leveraging distinct funding, talent, and product strategies, while major tech giants simultaneously push their own AI offerings, reshaping the market dynamics.
This article reviews the rapid progress of large language models (LLMs), covering their historical development, scaling laws, emergent abilities, core technologies such as training and alignment, resource ecosystems, evaluation methods, safety concerns, and prospective research challenges.
The article analyses how foundation models like ChatGPT are reshaping software engineering, highlighting their impressive capabilities for small‑scale tasks, the challenges they face with large, complex systems, the importance of digitalizing development knowledge, and proposes an intelligent interaction engine to combine human expertise with AI tools.
The CCF C³ event at Xiaohongshu gathered leading researchers and industry experts to dissect the latest advances, challenges, and future opportunities in intelligent recommendation and search, including multimodal content handling, decentralized distribution, cold‑start solutions, and the impact of large language models.
A recent OpenAI study estimates that roughly 80% of U.S. workers will see at least 10% of their tasks affected by ChatGPT and similar large language models, with about 19% of occupations potentially losing half of their tasks, highlighting widespread economic implications across all wage levels.
The article explains how the massive compute and storage demands of today’s large language models create a “compute wall” and “storage wall,” and describes Baidu Intelligent Cloud’s four‑layer full‑stack infrastructure—combining advanced parallelism techniques, optimized GPU networking, static‑graph compilation, and cost‑model‑driven placement—to train trillion‑parameter models efficiently.
The article reviews the latest AI breakthroughs—including Baidu’s Wenxin Yiyan launch, OpenAI’s multimodal GPT‑4, Google’s PaLM API, Microsoft’s super‑computer investment—and also covers Docker’s paid‑plan warning, the US demand for TikTok’s sale, and a Stack Overflow technology survey.
The article summarizes the technical innovations behind GPT‑4, highlighting its multimodal abilities, improved alignment methods, scaling‑law‑based performance prediction, and remaining limitations, while referencing the official OpenAI technical report and community analyses.
Microsoft Germany's CTO announced the imminent release of a multimodal GPT‑4, highlighting its ability to process text, images and video, while executives liken the breakthrough to an “iPhone moment” for AI, emphasizing new capabilities, industry disruption, and responsible data use.
This article presents a comprehensive overview of Baidu's document intelligence technologies—including the ERNIE‑Layout multimodal large model, the prompt‑based DocPrompt extraction system, layout and table understanding techniques, and PaddleNLP open‑source integration—detailing their architectures, challenges, solutions, performance benchmarks, and real‑world application cases across multiple industries.
The article explores three emerging directions for next‑generation large language models—self‑generated training data, built‑in verification with external retrieval, and massive sparse‑expert architectures—highlighting recent research, practical challenges, and their potential to reshape AI development.
This article explains ChatGPT’s architecture, the distinction between model capability and consistency, how next‑token and masked‑language‑model training lead to inconsistencies, and how OpenAI’s supervised fine‑tuning, reward‑model training, and PPO reinforcement learning (RLHF) are combined to improve alignment while highlighting the method’s limitations.
The escalating competition between Microsoft and Google over large language models is driving new search experiences, reshaping AI research, and raising concerns about content quality, privacy, and the future of smaller AI innovators.
ChatGPT, OpenAI’s latest language model, builds on GPT‑3 but uses supervised fine‑tuning and Reinforcement Learning from Human Feedback (RLHF) to improve alignment, yet its training methods still cause consistency issues such as invalid help, hallucinations, bias, and limited explainability.
This article dissects how ChatGPT acquired its surprising capabilities by tracing the evolution from the original GPT‑3 model through instruction tuning, code‑based pre‑training, and reinforcement learning from human feedback, ultimately presenting a comprehensive technical roadmap for reproducing GPT‑3.5‑scale models.
The article outlines three promising research avenues—self‑generated training data, model‑driven fact‑checking, and sparse expert architectures—that could shape the next wave of large language model innovation and address current limitations such as data scarcity and hallucinations.
Within five days of its launch, ChatGPT attracted over one million users, prompting massive investment from Microsoft, sparking fierce competition with Google, and raising critical questions about the technology's scalability, reliability, and societal impact.
A recent Stanford paper shows that GPT‑3.5 and its predecessor can pass classic Theory of Mind tests at levels comparable to 7‑9‑year‑old children, sparking debate over whether these abilities are genuine understanding or emergent by‑products of scaling.
This article explains the inner workings of ChatGPT, detailing its evolution from GPT‑3, the role of reinforcement learning from human feedback (RLHF) in improving consistency, the training pipeline steps, and the limitations and evaluation methods of large language models.
This article reviews Anthropic's Claude assistant, explains the novel Constitutional AI (RLAIF) approach that replaces costly human‑feedback data with a set of natural‑language principles, compares Claude with ChatGPT across helpfulness and harmlessness, and details the supervision and reinforcement‑learning pipelines, data annotation, and experimental results that demonstrate superior safety performance.
This article provides a comprehensive technical overview of ChatGPT, covering its origins, underlying GPT architecture, reinforcement learning from human feedback, training stages, current limitations, and prospective improvements such as model compression, constitutional AI, and integration with AIGC technologies.
This article explains how ChatGPT builds on GPT‑3, describes the supervised‑plus‑reinforcement learning (RLHF) pipeline that fine‑tunes the model, compares model capability with consistency, and discusses the performance evaluation and remaining limitations of large language models.
This article reviews recent research on the emergent abilities of large language models—such as chain‑of‑thought reasoning, knowledge retrieval without external sources, and robustness to distribution shifts—examining scaling laws, model size thresholds, and the open questions surrounding a potential paradigm shift from fine‑tuning to in‑context learning.
ChatGPT, OpenAI’s latest language model, builds on GPT‑3 and improves performance through supervised fine‑tuning, human‑feedback reinforcement learning (RLHF), and PPO optimization, addressing consistency challenges such as misaligned outputs, bias, and hallucinations while evaluating helpfulness, truthfulness, and harmlessness.
ChatGPT, OpenAI’s latest language model, builds on GPT‑3 using supervised fine‑tuning and Reinforcement Learning from Human Feedback (RLHF) with PPO, addressing consistency issues by aligning model outputs with human preferences, while discussing training methods, limitations, and evaluation metrics.
This article analyses how ChatGPT and the GPT‑3.5 series evolved from the original GPT‑3 through large‑scale pre‑training, code‑based training, instruction tuning, and reinforcement learning from human feedback, identifying the origins of their language generation, in‑context learning, world knowledge, code understanding, chain‑of‑thought reasoning, and alignment capabilities while also outlining current limitations.
This article compiles three highly upvoted Zhihu answers that examine OpenAI's ChatGPT, discussing its breakthrough impact on NLP, visual in‑context learning, reinforcement‑learning‑from‑human‑feedback, and the broader implications for AI research and development.
This article provides a comprehensive analysis of ChatGPT, covering its origins within the OpenAI GPT family, core technical features such as RLHF training and model compression, current limitations, future improvement directions, and the broader industry and investment opportunities generated by large‑language‑model AI.
This article reviews the evolution of dialogue systems from modular task‑oriented designs to end‑to‑end large‑scale models, detailing Baidu's PLATO series, their technical innovations, real‑world deployments, challenges such as inference efficiency and safety, and future research directions in conversational AI.
Amid growing concerns over plagiarism, critical thinking, and copyright, leading AI conferences, educational institutions, and developers are imposing bans and detection tools on ChatGPT-generated content, sparking a worldwide debate on the future of academic publishing and AI governance.
The article reviews the evolution of large language models from the pre‑GPT‑3 era to the present, analyzes the conceptual and technical gaps between Chinese and global research, and outlines key future research directions such as scaling laws, prompting techniques, multimodal training, and efficient model architectures.
This article examines the use of Mixture‑of‑Experts (MoE) sparse training for GPT models, detailing the architecture, training and inference efficiency gains, experimental comparisons with dense models, custom routing algorithms, and step‑by‑step deployment on Alibaba Cloud AI platforms.
During Xiaohongshu’s REDtech livestream, AI researchers examined ChatGPT’s rapid adoption, versatile task performance, and underlying large‑scale pre‑training with in‑context learning, while highlighting persistent hallucinations, weak reasoning, high costs, and limited search‑engine replacement potential, and emphasized the importance of RLHF‑driven human feedback for future multimodal AI research.
In a detailed conversation, LinkedIn co‑founder Reid Hoffman and OpenAI CEO Sam Altman explore the commercial potential of large language models, the emerging AI‑plus era for science and the metaverse, and their vision for the next decade of AI breakthroughs, societal impact, and governance.
In a candid conversation, Sam Altman and Reid Hoffman explore the next stage of AI, discussing commercial opportunities of large language models, the rise of AI‑plus applications in science and the metaverse, future directions such as multimodal and continuously learning models, and the societal challenges of AGI, wealth distribution and universal basic income.
The article reviews how the Transformer breakthrough, the rapid scaling of large language models such as GPT‑3, Switch Transformer, and Alibaba's AliceMind and M6, together with multimodal research, are shaping the next phase of artificial intelligence toward more general, collaborative, and open AI systems.
The DataFun 2022 NLP Summit, held on July 30, brings together top researchers and industry leaders from Alibaba, Baidu, Microsoft, Amazon, and more to present the latest advances in large‑scale pre‑training, multimodal perception, information extraction, dialogue interaction, machine translation, and practical AI deployments, with live streaming and free registration via QR code.
The article introduces a token‑level pipeline parallelism strategy that splits the sequence‑length dimension of Transformer‑based language models, explains why this approach is feasible, presents a dynamic‑programming formulation for optimal slicing, discusses engineering challenges, and evaluates its performance on large GPT models.
Microsoft's Jigsaw tool leverages post‑processing and user feedback to improve large language model code synthesis, especially for Python Pandas, achieving up to 80% accuracy and aiming to automate code verification and debugging for developers.
The Tsinghua‑Meituan Digital Life Joint Research Institute’s Academic Salon on March 23 featured Associate Professor Liu Zhiyuan presenting the latest advances and ten key challenges in large‑model technologies, aiming to foster industry‑academia collaboration and drive innovation in representation learning, knowledge graphs, and social computing.
The article introduces Volcano Engine's veGiantModel, a high‑performance large‑model training framework built on PyTorch, Megatron and DeepSpeed, details its distributed parallel strategies, hardware setups, benchmark results showing up to 6.9× speedup over Megatron and DeepSpeed, and provides open‑source links for further use.
This article presents the PLUG 270‑billion‑parameter Chinese language model and introduces a parameter‑efficient sparsity training (PST) framework that combines unstructured and structured pruning with low‑rank decomposition to dramatically reduce model size while preserving downstream performance.
The article reviews the 2021 AI landscape, highlighting the race for ever‑larger pretrained models, the dominance of Transformers across modalities, the promise and limits of large models, the rise of multimodal systems, regulatory considerations, and the still‑nascent progress in reinforcement learning.
