Microsoft’s vision of AI PCs, highlighted by the Copilot+ concept, details how integrated NPU hardware, local large‑language models, and the Windows Copilot Runtime enable on‑device AI inference, reducing data‑center load and offering developers a unified platform for building next‑generation AI applications.
This article explores JD Retail's AI‑driven multi‑agent system that mimics real‑world merchant decision processes, detailing the ReAct paradigm, agent roles, workflow, training methods, monitoring, and future directions for building intelligent e‑commerce assistants.
This article showcases the 13 award‑winning open‑source AI projects featured in the 2024 GitHub Accelerator, highlighting each project's purpose, founders, key technologies, and how they advance machine‑learning, model training, deployment, and innovative AI applications across various domains.
This article reviews recent advances in generative recommendation, highlighting challenges such as item representation and multimodal fusion, and summarizing four key research papers that propose novel tokenization, collaborative integration, and transformer-based multimodal approaches to improve recommendation performance.
The article explains how AI code reviews suffer from missing context, and improves them by embedding the codebase, using Retrieval‑Augmented Generation to fetch relevant snippets, and adding a function‑calling tool that lets the model autonomously request additional code, resulting in precise, bug‑detecting feedback.
The article explains how greedy search, beam search, Top‑K, Top‑p (nucleus) sampling, and temperature each shape large language model generation, comparing their effects on repetition, diversity, and creativity, and provides concise TensorFlow‑based code examples illustrating these inference‑time strategies.
This article reviews the NoteLLM paper, which leverages Llama 2 to create richer text embeddings and automatically generate tags and categories for note recommendation, addressing cold‑start issues through a multitask prompt design, generative‑contrastive learning, and collaborative supervised fine‑tuning, and demonstrates strong offline and online gains.
The article examines the opportunities and risks of applying large language models (LLMs) to cybersecurity, outlines fourteen practical recommendations for assessing their real‑world capabilities, and concludes with an invitation to the upcoming R&D Efficiency Conference covering AI, product management, and related topics.
The article explains how zero‑intrusion eBPF provides full‑stack, high‑quality observability data that, when combined with large language models, enables AI‑driven agents to automate ticket handling, change impact analysis, and vulnerability triage, dramatically improving operational efficiency.
This tutorial walks through creating a Coze bot that uses multi‑agent orchestration, memory variables, triggers, and large‑language‑model integration to upload bookmark files, extract and clean data, classify sites, generate importable HTML bookmarks, and provide AI‑driven search functionality, complete with Python code examples and deployment tips.
This article introduces Dify, an open‑source LLM application platform, outlines its core features such as workflows, model support, RAG pipelines, agents, and observability, compares it with alternatives, and provides step‑by‑step deployment instructions using Docker Compose and Helm for local and Kubernetes environments.
The article describes JD Retail's AI‑driven multi‑agent platform that models real‑world merchant decision‑making with ReAct‑based LLM agents, detailing the system architecture, agent roles, reasoning loops, workflow examples, training pipelines, monitoring, and future directions for e‑commerce support.
This article explains how a large‑language‑model‑driven tool can automatically create standardized Git commit messages by extracting change summaries, applying customizable plugins, measuring performance with MSE and adoption rate, and optimizing prompts, data pipelines, and fine‑tuning strategies.
This article presents a comprehensive overview of JD Retail's AI‑driven multi‑agent architecture for e‑commerce assistance, detailing how real‑world merchant decision processes are modeled with ReAct‑based LLM agents, the hierarchical workflow, training pipelines, monitoring mechanisms, and future directions for scalable intelligent commerce support.
Researchers propose a black‑box method to expose test‑set leakage in large language models by comparing log‑probability shifts when test items are shuffled, using Monte‑Carlo estimation and a sharded likelihood test, and demonstrate its effectiveness on several models including Mistral‑7B.
This guide explains how to download a GGUF‑format Llama 3 model, create a Modelfile, use Ollama commands to build and run the model locally, test it, and interact via the built‑in REST API, including useful Docker and model‑management tips.
This article walks through setting up a cloud‑native test environment using the open‑source NextChat UI and Higress API gateway to let Qwen‑2.5 masquerade as GPT‑4o, enabling a side‑by‑side comparison of their responses while showcasing Higress’s streaming, hot‑update, and security features for AI workloads.
This article explores why large‑model Agent technology is essential for AIOps, explains single‑ and multi‑Agent architectures, memory and tool integration, and demonstrates practical applications such as anomaly detection, fault diagnosis, automated remediation, ChatOps, and future directions for intelligent, autonomous operations.
Tencent Games tackled the low accuracy of AI‑generated SQL in production by combining large language models with a StarRocks lake‑warehouse, introducing a semantic layer, async materialized views, and an agent‑based multi‑intelligence framework, ultimately raising one‑shot SQL correctness to 89% and cutting delivery time from 2 hours to 0.33 hours.
This article offers a detailed theoretical interpretation of the DPO algorithm, showing how large language models can be viewed as Q‑functions, unifying sequence‑wise and step‑wise decision perspectives, and discussing the resulting implications for reinforcement‑learning‑based alignment research.
This guide explains ByteHouse’s high‑performance vector search capabilities, covering the background of vector retrieval for LLMs, the limitations of its existing skip‑index architecture, the new vector‑index design with HNSW and IVF, query‑time optimizations, performance benchmarks against Milvus, and future development plans.
This article details the design, implementation, and evaluation of an AI‑powered tool that automatically creates compliant Git commit messages by leveraging large language models, custom plugins, and performance‑focused optimizations to improve developer productivity and commit quality.
This talk explores how large‑model AI can give game NPCs personality, outlines the opportunities and challenges of intelligent NPCs, presents a case study of the "Jue Zhi An Nuan" NPC, and discusses future directions, safety compliance, and real‑time multimodal interaction solutions.
This article highlights four cutting‑edge open‑source AI initiatives—Meta’s Llama 3 large language model, the MaxKB knowledge‑base Q&A system, the Phidata framework for building AI assistants, and the RAGFlow retrieval‑augmented generation engine—detailing their capabilities, licensing, and where to access the code.
Amidst a growing trend that narrows performance gaps between large and small language models, researchers highlight the efficiency, adaptability, and specialized advantages of small language models (SLM), while also discussing the high costs, hallucinations, and security concerns that still challenge large‑scale LLMs.
This roundup covers SenseTime's new SenseNova 5.0 model rivaling GPT‑4 Turbo, Apple's ReALM model that outperforms GPT‑4, the free‑to‑try Meshy 3 3D generator, Lamini's $25 M funding for enterprise generative AI, and OpenAI's upcoming ChatGPT‑powered search engine challenging Google.
This article provides concise technical explanations for 25 common questions about Transformer models, covering scaled dot‑product attention scaling, separate Q/K projections, feed‑forward network design, attention variants, normalization, LoRA versus full‑parameter training, KV‑cache, pre‑ and post‑norm, computational cost analysis, and advanced position‑encoding techniques.
This article provides a comprehensive overview of how large language models (LLMs) are integrated into recommendation systems, detailing two main paradigms—LLM as a component and LLM as a standalone system—while discussing their impact on retrieval, ranking, prompting, and outlining future research challenges such as multimodal recommendation, hallucination mitigation, bias reduction, and agent‑based approaches.
This article examines whether the rapid growth of large‑language‑model context windows can eliminate the need for retrieval‑augmented generation, presenting experimental needle‑in‑a‑haystack tests, analysis of model performance across token lengths and needle positions, and practical guidance using an open‑source evaluation tool.
This article provides a comprehensive overview of large language models (LLMs), explaining their core concepts, transformer architecture, training stages, known shortcomings such as hallucination and reversal curse, and highlights emerging security threats like prompt injection and jailbreaking, offering guidance for safe deployment.
This article explores how developers can integrate large language models into applications by comparing fine‑tuning and context learning, detailing each method’s advantages and drawbacks, and presenting a four‑layer LLM tech stack—data, model, orchestration, and operations—with practical tooling examples.
This comprehensive guide explores the complexities of building enterprise‑level Retrieval‑Augmented Generation (RAG) systems, detailing common failure points, architectural components such as authentication, input guards, query rewriting, document ingestion, indexing, storage, retrieval, generation, observability, caching, and multi‑tenant considerations, and provides actionable best‑practice recommendations for developers and technical leaders.
Alibaba unveiled the 110‑billion‑parameter Qwen1.5‑110B model, featuring GQA, 32k context and multilingual support, and benchmark results show it matches or surpasses Llama‑3‑70B and Mixtral‑8x22B on a range of tasks, with notable gains in chat evaluations.
Apple’s OpenELM introduces a layer‑wise scaling Transformer family ranging from 270 M to 3 B parameters, provides a full open‑source training framework, and demonstrates superior zero‑shot and few‑shot performance over existing open LLMs despite using less public data, while also analyzing inference bottlenecks and PEFT results.
This article details the design and implementation of an LLM‑driven multi‑agent AI customer‑service assistant for vehicle selection at HuoLala, covering system architecture, algorithmic solutions, retrieval‑augmented generation, NLU/NLG agents, performance improvements, and future outlooks.
Microsoft unveiled the open‑source Phi‑3 series, a lightweight family of large language models that outperform larger rivals, run offline on smartphones, and cost a fraction of comparable AI models, opening new possibilities for edge and mobile AI applications.
Large language models still suffer from limited memory, constrained context windows, outdated knowledge, inability to control external systems, and poor domain expertise, but the article outlines two main remedies—fine‑tuning (Model‑as‑a‑Service) and prompt‑engineering—detailing their mechanisms, suitable scenarios, and trade‑offs.
The article chronicles Microsoft’s transformation from a stagnant software behemoth to the world’s most valuable company by embracing artificial intelligence, detailing Sophia Velastegui’s pivotal role, the strategic partnership with OpenAI, Azure OpenAI services, product integrations, associated risks, and future outlook.
The article showcases Justine Tunney’s remarkable engineering feats—from the RedBean web server that runs the same binary on six operating systems, to the cosmopolitan libc, a 512‑byte sectorLisp, the Blinkenlights visual debugger, the RoseHub security effort, and the llamafile tool that packages large language models into a single portable executable.
This article introduces the LangChain framework for large language model integration, explains its key components and advantages, and provides step‑by‑step Python examples for setting up environment variables, creating prompts, chaining models, and using embeddings, completions, and chat models.
The article examines how large language models can streamline advertising copy creation by addressing format diversity, creativity, and new media demands, detailing model evaluation, fine‑tuning of Chinese‑adapted LLMs—ultimately selecting QWen 1.5‑7B—and showing that deployment boosts copy quality, click‑through and conversion rates while outlining future personalization and data‑efficient scaling.
The article examines how LLM‑driven knowledge‑base QA and personal‑assistant agents struggle with context management, token limits, multimodal data, and tool‑parameter parsing, reviews open‑source frameworks such as LangChain, AutoGen and MetaGPT, and argues that fine‑tuning (e.g., LoRA) is essential for domain‑specific, scalable solutions.
This article explains how to build an offline knowledge base for IT operations by combining large language models with Retrieval‑Augmented Generation (RAG) using LangChain, covering document loading, chunking, embedding, vector storage, and query‑time retrieval with concrete code examples.
This article examines how large language models (LLMs) are being integrated into DevOps workflows, detailing practical implementations, organizational adoption, efficiency‑boosting techniques, underlying principles, limitations, and future directions for software engineers seeking to leverage AI as a reliable development partner.
Senior developer Jon Olson shares how he created an AI resume assistant using GPT‑4/3.5, LangChain, LlamaIndex, and retrieval‑augmented generation, detailing prompt engineering, backend integration, and future routing features to help job seekers showcase their skills.
This article reviews the common business and technical architectures used in large language model (LLM) applications, explains AI Embedded, AI Copilot, and AI Agent modes—including single‑ and multi‑agent systems—and offers guidance on selecting appropriate technology stacks such as prompt‑only, function‑calling agents, RAG, and fine‑tuning.
This article recounts the author's participation in a Shenzhen AI Hackathon, detailing the development of an interactive novel‑character chatbot using the Coze platform, describing the workflow design, technical challenges, model choices, knowledge‑base construction, and the final demo and award outcomes.
This article explains the motivation behind Self‑RAG, describes its core workflow—including conditional retrieval, enhanced generation, and self‑evaluation tokens—details the four evaluation metrics (Retrieve, IsRel, IsSup, IsUse), and provides a Python scoring example using log‑probabilities.
Early‑Stopping Self‑Consistency (ESC) dynamically halts sampling once a sliding‑window answer distribution reaches zero entropy, cutting the number of required LLM reasoning samples by 34‑84 % across arithmetic, commonsense, and symbolic benchmarks while preserving accuracy and offering a theoretically‑bounded, robust, budget‑adaptive alternative to traditional Self‑Consistency.
LangChain is an open‑source framework that provides Python and JavaScript SDKs, templates, and services such as LangServe and LangSmith to compose models, embeddings, prompts, indexes, memory, chains, and agents via a concise expression language, enabling rapid prototyping, debugging, and deployment of LLM‑driven applications.
The article surveys modern open‑source AI agent frameworks, defining agents as autonomous perception‑planning‑action systems, outlining core modules (inference, memory, tools, action), comparing single‑agent designs like BabyAGI and AutoGPT with multi‑agent platforms such as MetaGPT and AutoGen, and discussing their benefits, trade‑offs, and future research directions.
This article reviews recent scaling‑law research on large‑language‑model fine‑tuning and RLHF, explaining how data quantity, model size, PET parameters, reward‑model size and KL‑penalty affect downstream performance and offering practical insights for efficient training.
This guide walks AI engineers through a rapid 10‑minute boot‑strap of LangChain, explaining its purpose, core concepts, design questions, environment setup, and step‑by‑step code examples that cover APIs, chains, memory, retrieval‑augmented generation, tools, agents, and the overall architecture.
This article surveys the emerging field of large‑language‑model (LLM) based agents, detailing their modular architecture—including profiling, memory, planning, and action components—while discussing critical challenges such as role‑playing, memory design, reasoning, multi‑agent collaboration, and outlining promising research directions and practical case studies.
This article investigates how large language models can be applied to recommendation tasks, describing two usage strategies, various ranking approaches, experimental evaluations on multiple datasets, comparisons with traditional models, and analyses of prompt design, cost, and cold‑start capabilities.
This article provides a practical, side‑by‑side review of three major large‑model application development platforms—Baidu AppBuilder, Alibaba Bailei, and ByteDance Coze—detailing their creation workflows, configuration options, SDK capabilities, plugin ecosystems, workflow orchestration, and overall strengths and limitations for building AI agents.
This article outlines ten practical principles for designing applications with large language models, emphasizing a model‑first mindset, precision through interactive disambiguation, clear division of code and model responsibilities, data quality, handling uncertainty, and recognizing the limits of LLMs to build robust, maintainable software.
Jamba, a hybrid Mamba‑Transformer model from AI21 Labs, combines state‑space and attention layers with Mixture‑of‑Experts to deliver up to three times the throughput of comparable 52‑billion‑parameter LLMs on 128k context windows while maintaining high output quality and low memory usage.
This article provides an in‑depth overview of the Coze low‑code AI bot platform, covering its core features, product comparisons, step‑by‑step bot creation, RAG implementation, plugin usage, memory mechanisms, cron jobs, agent design, advanced workflow techniques, quality management, and future prospects.
This article reviews the paper “Data Mixing Laws: Optimizing Data Mixture by Predicting Language Modeling Performance”, explaining how the authors quantify the impact of data mixture ratios on LLM loss, propose a simple predictive model, validate it on RedPajama and multi‑domain mixes, and outline a scaling‑law procedure for continual pre‑training.
This article examines the evolution of Retrieval‑Augmented Generation (RAG) architectures for large language models, outlines the challenges they face, introduces the modular RAG Flow concept with four workflow paradigms, and provides a step‑by‑step implementation using LangChain and LlamaIndex with code examples.
This article reviews how the rapid rise of large language models, exemplified by ChatGPT, is transforming recommendation systems by addressing traditional ID‑centric limitations, introducing prompt‑based and ID‑free representations, discussing recent research advances, practical challenges, and future research directions.
The AutoMotion platform streamlines UI automation by recording user actions through a Chrome extension, converting them into Cypress scripts, isolating test data in a sandbox, and employing LLM‑driven self‑healing selectors, while offering open‑API integration and scalable containerized execution for reliable, low‑maintenance testing.
This guide compares Ollama and Hugging Face for running large language models locally, detailing API and local execution methods, installation steps, model selection, resource requirements, integration with AnythingLLM, container deployment, embedding and vector store setup, and practical observations on performance and limitations.
This guide explains the architecture of an interactive digital‑human system, walks through 3D avatar creation with Unreal Engine, details the AI controller that combines ASR, LLM and TTS, and provides step‑by‑step instructions for deploying the open‑source Fay project.
The diagram contrasts a Base LLM, which merely predicts the next word from training data and can continue stories or answer simple facts but may generate unsafe text, with an Instruction‑Tuned LLM that is fine‑tuned via RLHF to understand and follow commands, delivering more accurate, useful, and safe responses.
QCon Shanghai 2023 highlighted LLM‑driven frontend debugging, the emerging WebNN API for accelerated browser inference, AI‑native UI patterns with evaluation‑driven development, LLM‑enhanced developer bots using RAG and fine‑tuning, and a HarmonyOS round‑table exploring ArkUI’s declarative framework and opportunities for frontend engineers.
This article provides a comprehensive technical guide on Retrieval‑Augmented Generation (RAG), open‑source large language models such as LLaMA, fine‑tuning methods, evaluation metrics, memory‑optimization tricks, and attention‑related optimizations for modern AI systems.
The 2024 KDD Cup introduces the ShopBench benchmark, a large‑scale LLM competition that simulates real‑world online shopping with 57 tasks, over 20,000 questions, and multiple tracks covering concept understanding, knowledge reasoning, user‑behavior alignment, multilingual ability, and an all‑round track, all evaluated with task‑specific metrics and a hidden test set.
The article explains that current large language models lack true self‑awareness and act as assistants, so achieving convincing role‑playing behavior requires dedicated system prompts, specialized data, careful balance of continue pre‑training and general SFT, and evaluation methods to detect dissonance and preserve base capabilities.
The article analyzes the compute, memory, and communication resources required to train and run large language models, quantifies bottlenecks such as the massive FLOP demand, terabyte‑scale GPU memory, and high‑bandwidth interconnect needs, and evaluates parallelism strategies and bandwidth estimates to guide hardware and software design for scaling LLMs.
This step‑by‑step guide explains how to implement a lightweight Retrieval‑Augmented Generation system—Tiny‑RAG—by creating embedding classes, loading and chunking documents, building a simple vector store, performing similarity search, and integrating a large language model for answer generation, complete with runnable Python code.
This article explains how LangChain turns large language models into modular LEGO‑like building blocks, covering its core concepts, practical travel‑assistant and face‑recognition troubleshooting examples, and explores the rapid evolution of AI agents such as Gorilla, ToolLLaMa, MetaGPT and ChatDev.
The article examines common operational pain points such as cumbersome release processes, lack of standardization, and weak security controls, then explores how AI‑powered SRE tools and automation can address these challenges and guide teams toward more efficient, standardized, and resilient operations.
This article explains how to use LangGraph—a graph‑based extension of LangChain—to implement a corrective RAG (C‑RAG) pipeline that evaluates retrieved documents, rewrites queries when needed, performs web search, and generates accurate answers, complete with code snippets and a runnable example.
This article presents SLIM, a knowledge‑distillation framework that transfers the reasoning abilities of large language models to compact models for sequential recommendation, enhancing item representation, user profiling, and bias mitigation while achieving comparable performance with far lower computational resources.
Google has introduced preview vector search to its Cloud SQL for MySQL service, positioning it ahead of Oracle, while industry analysts note the growing importance of vector capabilities for generative AI applications across major database platforms.
This overview summarizes six WWW 2024 industry papers that apply large language models to e‑commerce search, personalized query suggestion, article recommendation, collaborative filtering, and lifelong sequential behavior understanding, highlighting their methods, experimental results, deployment status, and emerging trends in LLM‑driven search and recommendation.
This guide shows how to quickly and cheaply deploy the open‑source Google Gemma large language model on Alibaba Cloud Function Compute GPU using the new idle‑billing mode, covering prerequisites, Docker image creation, function setup, idle reservation, testing, monitoring, and cost estimation.
The article recounts Yi Tay’s experience founding Reka and building large language models from scratch, highlighting the unpredictable quality of GPU clusters, the challenges of multi‑cluster orchestration, code‑base choices, and how startups must rely on fast, intuition‑driven experimentation to succeed.
The article outlines how Sohu’s Hybrid AI Engine enables on‑device deployment of a distilled GPT‑2 model by converting it to TensorFlow Lite, detailing the setup, customization with Keras, inference workflow, and core SDK calls, and argues that this approach offers fast, private, and cost‑effective AI for mobile devices despite typical LLM constraints.
This article explains how LangChain simplifies building and integrating large language model applications by providing modular components such as models, prompts, indexes, tools, memory, chains, and agents, illustrated with practical use cases like travel assistants, face‑recognition troubleshooting, and multi‑agent workflows.
The article examines why large language models (LLMs) cannot fully replace human programmers, compares major models like Gemma, Code Llama, GPT‑4 and Claude, discusses trust and copyright concerns, and explores how smaller, specialized LLMs may shape the future of software development.
This article examines JD Retail's end‑to‑end large language model framework that combines supervised fine‑tuning, retrieval‑augmented generation, and ReAct‑based AI agents to overcome retail‑specific challenges, improve model accuracy, reduce hallucinations, and enable autonomous multi‑step business workflows.
StarCoder2, the latest open-source large language model from ServiceNow, Hugging Face, and NVIDIA, offers three sizes—30B, 70B, and 150B parameters—delivering performance comparable to the original 150B StarCoder while being more efficient and freely accessible under the BigCode Open RAIL‑M license.
This article walks through using large language model agents to create a theme‑park queue planning assistant for Baidu's Qianfan competition, covering the problem definition, dynamic‑programming‑style solution, prompt engineering, Python code generation, and step‑by‑step deployment in AppBuilder.
The article details how JD.com leverages domain‑specific and generic knowledge graphs to enhance multimodal product information, improve controlled text generation, and boost LLM performance for e‑commerce copywriting, covering model architecture, copy‑only mechanisms, token‑type encoding, experimental results, and practical deployment scenarios.
This comprehensive guide walks AI developers through building a Retrieval‑Augmented Generation (RAG) chatbot on Alibaba Cloud PAI, covering architecture, vector store setup, model deployment, knowledge ingestion, multi‑modal retrieval, fusion, re‑ranking, prompt design, and end‑to‑end configuration with code examples.
With the rapid rise of ChatGPT and large language models, recommendation systems are undergoing a transformative shift, moving beyond traditional behavior‑based methods to leverage LLMs for improved generalization, representation, and prompt‑based learning, while addressing challenges such as scalability, interpretability, bias, and deployment costs.
This guide explains Vanna, an open‑source Python RAG framework for Text2SQL, covering its core concepts, RAG‑based architecture, step‑by‑step model training, code examples for customization, and how to deploy a conversational database chatbot with a Flask web UI.
GPT4All, an open‑source LLM ecosystem from Nomic AI, lets users run and customize large language models locally on CPUs or GPUs, offering features like GGUF support, multi‑platform installers, API access, and community contribution guidelines, making it a versatile tool for AI enthusiasts and developers.
The article introduces LangChain as a framework that unifies LLM providers through model I/O, connects external data via retrievers, composes workflows with chains, maintains context with memory, and enables tool use through agents, and demonstrates Java examples for TongYi embeddings, a ChatGLM‑6B RetrievalQA chain, and discusses agent registration and micro‑service‑based agent factories.
This guide explains how to use the Coze no‑code AI Bot platform to create a personal blog knowledge base by configuring plugins, data sources, persistence, workflow nodes, prompt engineering, and publishing the bot for seamless AI‑assisted content retrieval.
This article presents a head‑to‑head evaluation of four leading large language models—GPT‑4, CodeLlama 70B, CodeLlama 7B, and Mixtral 8x7B—across eight coding‑related tasks, revealing GPT‑4 as the overall winner while highlighting the trade‑offs of smaller models and emerging competitors like Google Gemini.
This article explains two production‑grade optimization techniques for large language model inference—KV‑cache reuse across multi‑turn dialogues and speculative sampling with a small draft model—detailing their design, implementation, and performance impact.
This collection compiles technical articles that explore diverse AI/ML applications, from deploying large language models on MacBooks and building e‑commerce recommendation engines, to leveraging the LangChain framework, creating AIGC‑driven fashion solutions, and implementing Stable Diffusion for image generation.
This article presents a comprehensive overview of large language model inference, detailing the prefill and decoding stages, key performance metrics such as throughput, latency and QPS, and a series of system-level optimizations—including pipeline parallelism, dynamic batching, specialized attention kernels, virtual memory allocation, KV‑cache quantization, and mixed‑precision strategies—to improve GPU utilization and overall inference efficiency.
Google’s Gemini 1.5, a new multimodal Mixture‑of‑Experts model, supports up to a million‑token context (10 million internally), can understand text, video, audio and code, learns a new language from a single prompt, and is already being used by Samsung, Jasper and Quora, positioning it as a direct challenger to OpenAI’s flagship models.
This article explores three key ways large language models and multimodal generative AI can enhance robotic process automation, from cognition‑boosted RPA and AI‑Agent collaboration to visual‑intelligent navigation, illustrating practical examples and future prospects for smarter digital workers.
The author reviews a year of rapid progress in large language models, highlighting breakthrough papers such as Positional Interpolation, StreamingLLM, Deja Vu, and RLCD, and discusses how AI‑guided optimization techniques like SurCo, LANCER, and GenCo are reshaping research and industry applications.
