Marketing^AI

The paper "The Limitations of Large Language Models for Understanding Human Language and Cognition" from "Open Mind: Discoveries in Cognitive Science" argues that Large Language Models (LLMs) offer limited insights into human language and cognition , particularly concerning acquisition and evolution. The authors, Christine Cuskley, Rebecca Woods, and Molly Flaherty, contend tha...

We give a comprehensive analysis of the "agentic era" in Artificial Intelligence, highlighting a shift from reactive generative AI to autonomous, goal-driven AI agents capable of planning, reasoning, and executing complex, multi-step tasks. It contrasts these agents with simpler bots and AI assistants, detailing their "sense-think-act" operational loop and core components like...

2504.03601 This paper introduces APIGen-MT , a novel two-phase framework designed to generate high-quality, verifiable, and diverse multi-turn interaction data for training AI agents . The first phase focuses on creating detailed task blueprints with validated ground-truth actions, utilizing an agentic pipeline and LLM review committees with feedback loops. The second phase transforms these bluepr...

We offer a comprehensive analysis of Microsoft's TRELLIS , a generative 3D AI framework designed to revolutionize marketing. It explains how TRELLIS, built upon a proprietary Structured LATent (SLAT) representation , enables the efficient creation of high-fidelity 3D assets from various inputs, solving critical interoperability challenges by generating multiple output formats like meshes, Neur...

We explore the evolving field of Large Language Model (LLM) alignment , shifting from traditional, static training-time methods like RLHF to more dynamic test-time approaches . It introduces four distinct test-time alignment frameworks: Alignment as Reward-Guided Search (ARGS) , Adaptive Best-of-N (ABoN) , Controlled Decoding (CD) , and Test-Time Alignment via Hypothesis Reweighting (HyRe) . The a...

The research explores two methods for improving large language models' ability to solve complex, multi-step mathematical problems: outcome supervision (OS) , which provides feedback only on the final answer, and process supervision (PS) , which offers feedback on each intermediate step. The authors demonstrate that process supervision significantly outperforms outcome supervision , particularl...

This research paper from the **Marketing Science Institute** investigates the financial implications of a firm's **strategic emphasis** on either **value creation** (innovation, R&D) or **value appropriation** (extracting profits, advertising, brand building). Authors Natalie Mizik and Robert Jacobson examine how shifts in this emphasis, measured by the ratio of advertising to R&D expendit...

This podcast outlines a  venture capitalist's perspective  on the  revolutionary impact of Artificial Intelligence  on the  marketing industry by 2025 , highlighting a  "sea change"  that presents  unprecedented opportunities  for startups. It argues that AI will  transform marketing from a human-driven cost center into an autonomous, technology-driven profit center , shifting from s...

We explore marketing science through advanced analytical models , examining how businesses optimize their strategies in digital environments. Several articles investigate consumer behavior and decision-making , particularly regarding advertising effectiveness, search engine marketing (SEM), and online reviews, often employing multinomial logit models, hidden Markov models (HMMs), and multi-armed b...

This academic paper introduces a novel condition for identifying average treatment effects (ATE) and average treatment effects on the treated (ATT) in observational studies, extending beyond traditional assumptions like unconfoundedness and overlap. The authors propose an "Identifiability Condition" that is both sufficient and necessary for these causal effects to be determined, integrat...

This paper introduces Conformal Bayesian Optimization (Conformal BayesOpt) , a novel approach designed to enhance Bayesian Optimization (BayesOpt) by integrating conformal prediction sets . Traditional BayesOpt often faces challenges like unreliable predictions due to model misspecification and covariate shift , particularly when selecting new data points. Conformal BayesOpt addresses these issues...

This paper introduces Conformal Bayesian Optimization (Conformal BayesOpt) , a novel approach designed to enhance Bayesian Optimization (BayesOpt) by integrating conformal prediction sets . Traditional BayesOpt often faces challenges like unreliable predictions due to model misspecification and covariate shift , particularly when selecting new data points. Conformal BayesOpt addresses these issues...

The provided sources discuss  artificial intelligence's transformative impact on the advertising industry , highlighting a significant power shift from traditional agencies to tech giants that control platforms and data. They examine how  AI is disrupting traditional revenue models  by automating creative production, prompting agencies like WPP, Publicis Groupe, and Omnicom to invest heavily i...

This scientific article,  published online November 21, 2024 , explores the application of  in-context learning (ICL)  with  multimodal large language models (LLMs) , specifically  GPT-4V , for  classifying cancer pathology images . The authors demonstrate that  ICL can improve the accuracy of these models  in medical image analysis,  matching or surpassing specialized neural networks  trained for...

The paper introduces  Compare2Score , a novel no-reference image quality assessment (NR-IQA) model built upon large multimodal models (LMMs). This framework addresses the challenge of converting discrete comparative image quality judgments into continuous scores, a significant hurdle in combining diverse IQA datasets. Compare2Score trains LMMs to mimic human-like visual quality comparisons by gene...

This paper introduces a novel Stepwise Context Search (SCS) method designed to enhance In-Context Learning (ICL) based image segmentation. Traditional ICL methods often require extensive annotations or rely on simple similarity sorting for visual prompt selection , which the authors demonstrate can lead to inconsistent performance. The SCS method addresses these limitations by constructing a small...

This paper introduces  DepictQA-Wild , a novel  Vision Language Model (VLM)  designed for  Image Quality Assessment (IQA) , which aims to align with human perception by leveraging language descriptions. The paper addresses limitations in existing VLM-based IQA methods, specifically their  limited functionality  across various scenarios (e.g., single-image vs. multi-image comparison, image restorat...

This paper introduces PromptIQA , a novel framework for no-reference image quality assessment (NR-IQA) that addresses the challenge of adapting to diverse assessment requirements without time-consuming fine-tuning . Unlike typical NR-IQA models, PromptIQA utilizes Image-Score Pairs (ISPs) as prompts to guide its predictions, significantly reducing reliance on extensive datasets for new requirement...

This paper introduces  DepictQA , a novel approach to  Image Quality Assessment (IQA)  that moves beyond traditional  score-based methods  by leveraging  Multi-modal Large Language Models (MLLMs) . Unlike conventional IQA that outputs numerical scores, DepictQA provides  language-based, human-like evaluations , describing  image content and distortions descriptively and comparatively . To achieve...

This academic survey  comprehensively examines Image Quality Assessment (IQA) , a critical area in image processing and computer vision. It categorizes and discusses both  general and specialized IQA methods , ranging from  traditional statistical and machine learning approaches  to  cutting-edge deep learning models like CNNs and Transformers . The document highlights the  advantages and limitati...

This source explores  in-context learning  for  large vision models , a novel approach for  model adaptation  that avoids parameter updates by incorporating  domain-specific input-output pairs , known as  in-context examples  or  prompts , alongside test data. The authors highlight that the selection of these examples significantly  impacts downstream performance . To address this, they propose a ...

This research introduces Partial2Global , a novel framework for Visual In-Context Learning (VICL) , focusing on the critical task of selecting optimal "in-context examples" to enhance the performance of visual foundation models. The paper highlights that randomly chosen examples often yield poor results , and that visual similarity alone is an unreliable metric for selection. Partial2Glo...

We explore two distinct approaches to explainable AI (XAI): internalist (mechanistic) and externalist (phenomenological) . Attention-Based Causal Discovery (ABCD) , representing the internalist view, focuses on understanding a specific model's internal computational logic by analyzing its self-attention mechanisms to uncover learned, often non-obvious, dependencies. Conversely, Prompt-Based La...

This paper introduces an  interlocked hypergraph neural network framework  designed to understand and model how consumers organize their collections, particularly music playlists. The research utilizes  multimodal data , including user-generated tags and acoustic features, to create  probabilistic embeddings  of consumers, playlists, and songs within a unified space. The paper demonstrates the mod...

This academic paper investigates the implications of AI agents automating negotiations and transactions in consumer markets . The authors establish an experimental framework where different Large Language Models (LLMs) act as buyer and seller agents for real-world products, evaluating their performance and identifying potential risks. Key findings indicate significant disparities in negotiation ca...