Marketing^AI

We discuss the shift in artificial intelligence from general models to personalized systems, emphasizing that successful alignment depends on user diversity rather than just algorithms. Mathematical frameworks reveal that platforms achieve perfect alignment only when their user base is sufficiently heterogeneous, allowing a shared representation to be refined by diverse feedback. This creates a unique informational network effect where the high cost of providing free AI services is justified by the valuable, varied data these users generate. By subsidizing free users as "exploratory agents," companies can deliver hyper-personalized results to paying customers with minimal error. Consequently, AI platforms must evolve into market designers that meticulously manage user demographics to ensure statistical efficiency and maintain a competitive edge. This transformation redefines the microeconomics of inference, turning user variety into a critical factor of production.

We explore how the inherent rigidity of property law and fiduciary responsibility prevents artificial intelligence from ever fully replacing human leadership. Because the legal system requires human accountability for asset ownership and executive decisions, AI is relegated to a tool for execution rather than an entity capable of holding power. This structural reality creates a bifurcated labor market where entry-level roles vanish through automation while the executive class becomes more insulated and influential. Consequently, the document argues that elite business schools provide a critical strategic advantage by offering the credentials and exclusive alumni networks necessary to secure these high-stakes, un-automatable positions. Ultimately, while technology evolves rapidly, the legal architecture of liability ensures that human judgment remains the final authority in a digital economy.

We explore how the agentic economy is transforming digital marketing from a competition for human attention to a struggle for algorithmic preference. As AI assistants increasingly handle search and transactions, companies like Semrush are pivoting to provide a measurement layer that tracks brand visibility within AI-generated responses. This strategic shift involves the creation of new metrics, such as AI share of voice, and the adoption of the Model Context Protocol to allow agents to query marketing data directly. The sources emphasize that in a world of agent-mediated decisions, success depends on maintaining a high-quality reputation and appearing as a cited authority in AI answers. Ultimately, the text outlines how traditional SEO is evolving into Generative Engine Optimization to ensure brands remain discoverable in both open and restricted AI ecosystems.

We discuss a 1986 lecture by Richard Hamming, a renowned scientist who explores why certain researchers achieve extraordinary breakthroughs while others are eventually forgotten. Hamming argues that significant contributions are not merely products of luck or high intelligence, but result from deliberate habits such as working on the most important problems in one's field. He emphasizes the necessity of unwavering commitment, the courage to challenge established norms, and the ability to effectively communicate and sell one's ideas to the broader community. The text also highlights how personal traits, including a tolerance for ambiguity and a willingness to adapt to organizational systems, play a vital role in sustaining a productive career. Ultimately, the source serves as a strategic guide for individuals seeking to maximize their professional impact and leave a lasting legacy in science or engineering.

We describes a fundamental transition from the Attention Economy to an Agentic Economy, where autonomous AI agents increasingly handle market transactions. In this new landscape, **marketing** is reimagined as a technical discipline called **Data Source Engineering**, focusing on the rigorous collection of human preference data rather than simple brand persuasion. This evolution prioritizes **Preference Alignment** and **Active Learning**, using consumer interactions to refine the AI models that drive decision-making. The report highlights emerging **infrastructure protocols** and **elicitation frameworks** that allow brands to communicate directly with digital assistants. Ultimately, the role of the marketer shifts toward managing a **"Digital Twin"** of the market to ensure business agents accurately reflect and serve user intent. These sources argue that firms must master **technical signaling** and data integrity to remain visible to the algorithmic buyers of the future.

This paper proposes a novel approach to the challenging problem of forecasting consumer demand for new brands where preference is largely driven by intangibles. The central framework integrates a structural demand model, which initially estimates existing brand utilities, with a fine-tuned large language model (LLM). By training the LLM on textual descriptions of products and markets, the resulting model can successfully predict consumer preferences ($\delta_{jt}$) for items it has never encountered, significantly outperforming conventional models based on text embeddings. The author analyzes the internal mechanics of the LLM using techniques like sparse autoencoders to identify interpretable features influencing choices, thus offering guidance on brand positioning. Furthermore, the methodology allows researchers to perform advanced market simulations, such as solving for optimal pricing by combining LLM predictions with causal estimates derived from instrumental variable methods.

This paper presents a systematic study of **Generative Engine Optimization (GEO)** in the e-commerce sector, a practice now vital as LLMs deploy conversational shopping agents that rerank products. To address the lack of data and systematic methods in this emerging field, the authors introduce **E-GEO**, a novel benchmark dataset comprising over 7,000 realistic, intent-rich consumer queries paired with product listings. The research evaluates 15 existing content rewriting heuristics but finds that framing the process as an **optimization problem** yields much greater results. Specifically, an iterative prompt-optimization algorithm consistently delivers **superior ranking improvements** for products within the generative engine's output compared to relying on ad hoc rules. This successful systematic approach indicates the existence of a **stable, generally effective GEO strategy** that could be applied across various product domains.

This paper describes an effort to improve the accuracy of historical U.S. Census transcriptions using a machine learning model. The authors focused on correcting errors in name transcriptions from the 1940 census for Rhode Island, specifically targeting records where independent human transcriptions from Ancestry.com and FamilySearch.org disagreed. The improved transcriptions significantly increased the rate of linking individuals across census records, particularly benefiting records with low original legibility where human transcribers typically struggle. This approach promises to enhance the utility of historical census data for economic and social research by creating a higher quality, linked dataset across multiple periods.

This is from a working paper that analyzes the regulatory distortion in investment incentives within the United States natural gas pipeline network. The authors develop and estimate a structural model to compare the marginal social value of pipeline capacity—tied to regional gas price differences—against the financial incentives of firms operating under fixed rate-of-return regulation. The paper finds that firms' incentives to invest frequently exceed the social value of capital, emphasizing the critical role of the costly regulatory approval process as a secondary tool to control investment and prevent overcapitalization. Ultimately, the authors suggest a welfare-improving reallocation of regulatory costs, recommending streamlining approval in the Northeast while tightening scrutiny in the Southeast and Mountain West regions to address a persistent spatial misallocation of capital.

This paper examines the challenges of conditional ignorability, a key assumption in causal inference used to identify causal effects from observational data. It argues that assessing this assumption is more complex than often perceived, as it implicitly requires evaluating numerous structural configurations within covariate sets. To address this, the authors propose a new framework using Cluster Causal Diagrams (CG(3)), which abstracts the internal structure of covariates into three blocks: treatment (X), outcome (Y), and adjustment covariates (Z). This approach introduces structural ignorability, a concept evaluated using a modified back-door criterion on CG(3) diagrams, offering a more transparent and practical method for assessing causal assumptions. The paper highlights that while conditional ignorability cannot be reliably assessed at this level of abstraction, structural ignorability provides a principled middle ground between the traditional potential outcomes (PO) framework and comprehensive structural causal models (SCMs).

We explore the imminent shift in the digital economy from a defensive model, where websites block automated agents, to an open, transactional "agent economy." This transformation is driven by the realization that website content is a valuable capital good for AI models, leading to a move towards monetized access via APIs. We detail the unsustainable "arms race" between scrapers and blocking technologies, advocating for APIs as an economically rational solution to monetize information goods. It then introduces the rise of autonomous AI agents as new consumers, necessitating machine-centric API design, new platforms (marketplaces, A2A commerce), and evolving business models like Agent-as-a-Service and outcome-based pricing. Finally, We highlight critical challenges, including the need for a micropayment infrastructure, widespread standardization, and robust regulatory frameworks to address data privacy, intellectual property, and potential algorithmic collusion.

This discussion offers a comprehensive examination of the strategic importance of advanced analytics for driving business expansion. It highlights how organizations often struggle to leverage data effectively due to poor data quality, cultural resistance, and a lack of strategic alignment, despite the immense potential for growth. It differentiates between traditional Business Intelligence (BI) and forward-looking advanced analytics, emphasizing techniques like predictive and prescriptive modeling, Machine Learning (ML), and Artificial Intelligence (AI) for uncovering insights and recommending actions. It further illustrates the tangible value of analytics through case studies in customer personalization, marketing ROI optimization, and cross-functional operational improvements, while also addressing the significant challenges and ethical considerations such as data privacy and algorithmic bias. Ultimately, it proposes a phased strategic blueprint for implementing a sustainable analytics capability, focusing on executive alignment, data governance, and fostering a data-informed culture.

This academic paper from Meta Platforms introduces **AdLlama**, a novel large language model (LLM) designed to enhance generative advertising text on Facebook. The core innovation is **Reinforcement Learning with Performance Feedback (RLPF)**, a post-training method that utilizes historical ad performance data, specifically click-through rates (CTR), as a reward signal to fine-tune the LLM. Unlike traditional methods relying on human preferences, RLPF optimizes for measurable real-world outcomes. A large-scale A/B test involving nearly 35,000 advertisers demonstrated that AdLlama significantly improved advertiser-level CTR by 6.7% and increased the number of ad variations created, showcasing the tangible economic impact of this new reinforcement learning approach.

We explore the **evolution of marketing** from AI-assisted to **AI-autonomous functions**, highlighting the profound implications for Chief Marketing Officers (CMOs). We argue that while AI currently boosts efficiency in tactical tasks, the future involves **specialized AI agents** operating with a high degree of autonomy, necessitating a shift in the CMO's role to that of a **systems architect and ethical steward**. We argue the crucial role of **multi-objective reward models** in guiding AI behavior and managing **inherent systemic risks** like algorithmic bias and "reward hacking." Ultimately, we outline a **phased roadmap** for organizations to transition to this advanced, AI-driven marketing ecosystem, stressing the importance of **human oversight and ethical governance**.

We explore **transformative shift in marketing** due to AI, moving from AI-assisted to **AI-autonomous functions**. It highlights that while AI excels at **tactical tasks** like content optimization and performance marketing, **human oversight** remains crucial for strategic areas such as brand management and crisis communication. The text emphasizes the evolving role of the **Chief Marketing Officer (CMO)**, who will become the **architect and ethical steward** of these autonomous systems. This new paradigm necessitates the CMO's understanding of **Reinforcement Learning (RL)** and the critical importance of **designing reward models** to align AI actions with complex, **multi-objective business goals**. Finally, the source addresses significant **risks associated with autonomous AI**, including the "black box" problem, algorithmic bias, and the potential loss of human intuition, while outlining a **strategic roadmap for organizations** to navigate this transition effectively.

We analyze the urgent need for a radical overhaul in marketing education due to the rise of Artificial Intelligence (AI) and Large Language Models (LLMs)**. It argues that traditional MBA programs are facing a **"crisis of relevance," highlighted by student dissatisfaction at institutions like Stanford GSB**, because their curricula fail to address the AI-driven transformation of the marketing industry. The report **details how AI is reshaping every aspect of marketing**, from hyper-personalization to content creation, and **outlines the new "AI-augmented marketer" profile**—a strategic orchestrator requiring skills beyond mere technical proficiency. Finally, it **proposes a comprehensive three-pillar framework for curriculum redesign**, emphasizing a redefined core, advanced specializations, and applied pedagogy to cultivate leaders capable of strategically leveraging AI.

We outline **transformation of Artificial Intelligence (AI) in marketing**, moving from its current role as an **efficiency-boosting tool** to its future as an **autonomous, decision-making agent**. It categorizes this evolution into distinct phases: the present, where AI augments human tasks like content creation, and the impending **"agentic inflection point,"** where AI systems will independently execute complex, multi-step marketing goals. The text also emphasizes the **critical need for educational reform** to prepare marketers for this shift, highlighting new competencies like data fluency and ethical AI application, and it concludes with **strategic recommendations for both businesses and academic institutions** to navigate this profound change.

This research introduces a novel framework for Visual In-Context Learning (VICL), a method where artificial intelligence models learn from provided visual examples. The primary focus is on optimizing the selection of these "in-context examples," which significantly impacts the model's performance on tasks like image segmentation, object detection, and colorization. The authors propose a transformer-based list-wise ranker to identify the most relevant examples, overcoming limitations of previous pair-wise ranking methods that often rely on visual similarity. Furthermore, a consistency-aware ranking aggregator is introduced to synthesize more reliable global rankings from the partial predictions of the ranker. Extensive experiments demonstrate that this new approach consistently outperforms existing methods, leading to state-of-the-art results across various visual tasks.

This paper introduces GEPA (Genetic-Pareto), a novel prompt optimizer designed for large language models (LLMs) and compound AI systems. Unlike traditional reinforcement learning (RL) methods that rely on numerical rewards and extensive "rollouts" (tens of thousands), GEPA leverages natural language reflection to learn high-level rules from trial and error, significantly reducing the required number of rollouts. It achieves this by analyzing system trajectories, diagnosing problems, proposing prompt updates, and combining effective lessons through a Pareto frontier search. This paper presents evidence that GEPA outperforms existing RL and prompt optimization techniques in sample efficiency and generalization across various benchmarks, while also producing shorter, more efficient prompts compared to other methods like MIPROv2.

We introduce and defend the "prediction policy problems" (PPP) framework, which posits that many public policy and economic challenges have an often-overlooked predictive element that machine learning (ML) can significantly enhance. The document addresses key criticisms, arguing that the framework doesn't seek to replace causal inference but rather to improve the predictive "bricks" within complex policy decisions, which inherently include prediction, causal inference, and normative judgment. It emphasizes that accurate prediction is crucial for efficient and equitable resource allocation and that the framework has spurred the development of causal ML methods that integrate prediction with causal analysis. Furthermore, the text contends that challenges like target-construct mismatch and dynamic systems are inherent to quantitative policy analysis and that the PPP framework offers a more transparent and adaptable approach than traditional methods. Finally, it stresses that responsible implementation requires a robust "institutional wrapper" encompassing transparency, human oversight, and contestability, asserting that the proper comparison for algorithmic systems is not perfection, but the often-flawed human-centric status quo.

This academic article critiques the widespread deployment of "predictive optimization" algorithms, which use machine learning to make decisions about individuals based on future predictions. The authors argue that despite claims of accuracy, efficiency, and fairness, these systems inherently fail on their own terms due to seven recurring shortcomings. These issues include inability to translate predictions into optimal interventions, mismatches between intended outcomes and measurable data, biased training data, limitations in predicting social outcomes, unavoidable disparate performance across groups, lack of effective contestability, and vulnerability to strategic manipulation (Goodhart's Law). The research analyzes eight real-world case studies across diverse domains like criminal justice and healthcare, demonstrating that these critiques apply broadly and are not easily resolved through minor design changes, ultimately challenging the legitimacy of such deployments. The paper concludes by providing a rubric of critical questions for assessing these systems and advocating for alternative decision-making approaches.

This research introduces a framework for integrating human expertise into algorithmic predictions, specifically focusing on instances where algorithms deem inputs "indistinguishable." The authors propose a method for selectively incorporating human judgment in these cases, demonstrating its proven ability to enhance the performance of any feasible algorithmic predictor. Empirical studies, including X-ray classification and visual prediction tasks, reveal that even when algorithms generally outperform humans, human input significantly improves predictions on specific, identifiable instances, which can constitute a substantial portion of the data. Furthermore, the paper explores how this framework can lead to algorithms that are robust to varying levels of user compliance, providing near-optimal predictions even when users selectively defer to the algorithm. Ultimately, the work advocates for human-AI collaboration to mitigate algorithmic monoculture by leveraging diverse human perspectives in prediction tasks.

This academic paper investigates the critical issue of data leakage in applying machine learning (ML) to panel data, which combines cross-sectional and time-series observations. The authors explain that standard ML practices, when unsuited for panel data's inherent structure, can lead to temporal leakage (future information affecting past predictions) and cross-sectional leakage (information sharing across training and testing units). This leakage results in inflated model performance and misleading policy recommendations, as empirical applications, particularly for income prediction in U.S. counties, vividly demonstrate. To counter this, the paper offers practical guidelines for practitioners, emphasizing the importance of clearly defining research goals—whether for cross-sectional prediction or sequential forecasting—and implementing appropriate data splitting and cross-validation strategies to ensure robust and realistic ML model evaluation.

This paper introduces the concept of "prediction policy problems," arguing that not all policy decisions require causal inference; many benefit significantly from accurate predictions. The authors distinguish these from traditional "causal inference" problems through examples, such as deciding whether to take an umbrella (prediction) versus whether a rain dance causes rain (causal). They explain how machine learning (ML) excels in prediction by effectively managing the bias-variance trade-off and allowing for flexible models, unlike conventional methods like Ordinary Least Squares (OLS) that prioritize unbiasedness. An illustrative application in healthcare demonstrates how ML can identify and reduce "futile surgeries" by predicting patient mortality, leading to substantial savings and improved patient outcomes. The text concludes by highlighting the widespread applicability and importance of prediction problems across various policy domains, suggesting they warrant greater attention and reorientation in economic research.

We explore the evolving landscape of immersive marketing across Meta's virtual and augmented reality platforms, specifically focusing on Quest headsets and Ray-Ban smart glasses. They detail how advancements in hardware, AI, and sensor technology will enable deeply personalized, context-aware, and interactive advertising experiences that go beyond traditional 2D formats. The texts also highlight the critical ethical and privacy considerations associated with collecting sensitive biometric and first-person data, emphasizing the need for transparency and user trust. Finally, the sources discuss the strategic shifts required for brands and marketers to adapt to these new "dimensional" realities, including developing 3D assets, cultivating new skillsets, and redefining how marketing ROI is measured.

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 that while LLMs can functionally imitate human writing, their underlying mechanisms and developmental processes are fundamentally different from how humans acquire and use language. They employ an ethological "four questions" framework to highlight these distinctions, emphasizing that LLMs lack true meaning, multimodality, and the diverse, interactive aspects characteristic of human language. Ultimately, the report concludes that LLMs should be viewed as tools for specific, carefully constructed research questions rather than comprehensive models for understanding the full scope of human linguistic behavior.

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 LLM "brains," memory, and tool use. The source explores the competitive landscape, examining the strategies of major players like OpenAI, Google, Anthropic, and Meta, and discusses the profound implications of agentic AI across the marketing mix, from market research and content creation to sales funnel automation and customer service. Finally, it addresses the strategic advantages, ROI measurement, technical hurdles, and ethical considerations associated with this technology, including data privacy and bias, while forecasting a future where human marketers transition to roles focused on strategic oversight and system design in an increasingly "Agent-on-Agent" economy.

2504.03601This 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 blueprints into realistic conversational trajectories through simulated human-agent interplay. This approach leads to the development of xLAM-2-fc-r models, which demonstrate superior performance on benchmarks like BFCL v3 and τ-bench, especially in multi-turn settings, and exhibit enhanced consistency compared to other frontier models. The project aims to open-source its generated data and models to foster further AI agent research.

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, Neural Radiance Fields (NeRFs), and 3D Gaussian Splats. The report highlights TRELLIS's potential to disrupt content supply chains, enable generative market research through rapid asset variation, and power immersive customer experiences like virtual try-ons. Ultimately, it positions TRELLIS as a strategic tool for future-proofing digital asset management and transforming marketing from a static content creation process into a dynamic, AI-driven generation engine.

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 analysis compares these methods across various axes, including their point of intervention (e.g., token-level, post-generation), required alignment signal (e.g., reward models vs. labeled examples), computational profile (training vs. inference costs), and their flexibility in handling multiple objectives or adapting to distribution shifts. Ultimately, the text argues that selecting the optimal test-time alignment method depends on the specific application's needs, resources, and tolerance for complexity, indicating a move towards a versatile toolkit for AI alignment rather than a single, universal solution.

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, particularly on challenging datasets like MATH, leading to more reliable models. They also introduce active learning as a method to enhance the efficiency of collecting human feedback for process supervision and release a large dataset, PRM800K, to support further research in this area. Ultimately, the paper argues that process supervision not only yields better performance but also promotes more interpretable and safer AI reasoning, highlighting its potential benefits for AI alignment.

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 expenditures relative to assets, affect **stock return**. Their findings suggest that investors generally favor an increased focus on **value appropriation**, particularly when a firm's earnings are strong, even in high-technology sectors. However, the study also notes that in certain situations, such as when a firm is performing poorly or has already heavily invested in value appropriation, emphasizing **value creation** is viewed more positively by the market.

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 simply assisting marketers to AI becoming the marketer. The text identifies three critical vulnerabilities of incumbent marketing giants: the collapse of traditional measurement (due to cookie deprecation), the commoditization of creative content production through generative AI, and the fragmented nature of current MarTech stacks, all of which pave the way for "agentic" AI workflows that automate entire marketing functions. The author then introduces specific "atomic eggs" or non-consensus investment opportunities in companies like Prescient AI (predictive measurement), Hedra (generative storytelling with characters), and FirmPilot (agentic marketing for niche SMBs), emphasizing the need to back "Prime Mover" founders who possess a unique vision and the ability to create new categories and disrupt existing business models.

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 bandit approaches. Key themes include measuring advertising's long-term impact, understanding cross-channel advertising effects (e.g., TV on search), and optimizing website content through dynamic morphing based on user engagement. The sources also address the challenges of data sparsity in paid search campaigns and the importance of accounting for consumer heterogeneity and underlying motivations in various marketing contexts.

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, integrating concepts from statistical learning theory. The research demonstrates how this condition applies to various scenarios, including those where unconfoundedness or overlap are violated, such as in Regression Discontinuity designs and studies with extreme propensity scores. Furthermore, the paper provides finite sample estimation guarantees for these complex scenarios, offering concrete algorithms for practical application and bridging previously disconnected areas of causal inference and learning theory.

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 by directing queries towards regions where model predictions are statistically guaranteed to be valid, even with imperfect models, and includes a mechanism to correct for covariate shift. The research demonstrates that this method significantly improves the reliability of query outcomes while maintaining comparable sample-efficiency in various optimization tasks, including drug design.

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 by directing queries towards regions where model predictions are statistically guaranteed to be valid, even with imperfect models, and includes a mechanism to correct for covariate shift. The research demonstrates that this method significantly improves the reliability of query outcomes while maintaining comparable sample-efficiency in various optimization tasks, including drug design.

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 in proprietary AI platforms and strategic acquisitions to remain competitive. The sources also detail the offensive strategies of tech giants like Google, Meta, and Amazon, who are integrating AI tools directly into their ad-buying platforms to offer seamless, automated campaign creation. Finally, the text explores the evolving roles within agencies, the potential for job displacement due to automation, the quantifiable business benefits of AI in terms of speed and ROI, the global variations in AI adoption and regulation, and the critical ethical considerations surrounding AI, such as bias, privacy, and the need for human oversight to maintain authenticity and trust.keepSave to notecopy_alldocsAdd noteaudio_magic_eraserAudio OverviewflowchartMind Maparrow_downwardJump to bottom

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 specific tasks, and doing so with minimal data requirements. The research highlights GPT-4V's ability to classify tissue subtypes, colon polyps, and breast tumor detection in lymph nodes, suggesting a potential to democratize AI access for medical experts. Ultimately, the study advocates for the potential of generalist AI models to perform complex medical image processing, reducing the need for extensive retraining and specialized models in the future.keepSave to notecopy_alldocsAdd noteaudio_magic_eraserAudio OverviewflowchartMind Map

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 generating scaled-up comparative instructions from existing IQA datasets. It then employs an innovative soft comparison method to translate these qualitative comparisons into precise quantitative quality scores. Experiments demonstrate Compare2Score's superior generalization across various distortion types and its ability to enhance the rating accuracy of other general-purpose LMMs.keepSave to notecopy_alldocsAdd noteaudio_magic_eraserAudio OverviewflowchartMind Maparrow_downwardJump to bottom

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 smaller, more diverse candidate pool of examples through a clustering and sampling strategy, significantly reducing annotation costs. Furthermore, it incorporates an adaptive search module that dynamically selects the most appropriate visual prompts for specific test images, thereby improving the accuracy and stability of segmentation results across various real-world scenarios.

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 restoration vs. generation) and sub-optimal performance due to inadequate training data and fixed image resolutions. To overcome these issues, the authors constructed DQ-495K, a large-scale dataset featuring 35 diverse distortion types across 5 severity levels, with ground-truth informed responses generated by GPT-4V to enhance label quality. DepictQA-Wild is trained on this dataset, notably retaining original image resolution and incorporating confidence estimation to improve accuracy across tasks such as distortion identification, image assessment, and paired image comparison in both full-reference and non-reference settings.keepSave to notecopy_alldocsAdd noteaudio_magic_eraserAudio OverviewflowchartMind Maparrow_downwardJump to bottom

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 requirements. To enhance the model's ability to comprehend and learn from these prompts, the authors propose two data augmentation strategies: random scaling and random flipping. Experiments demonstrate that PromptIQA, trained on mixed datasets, achieves superior performance and generalization compared to existing state-of-the-art methods, proving its effectiveness in various IQA tasks.

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, the authors developed a hierarchical task framework (Quality Description, Quality Comparison, Comparison Reasoning) and created the M-BAPPS dataset, which includes detailed and brief text descriptions for image quality evaluation. The research demonstrates that DepictQA outperforms score-based methods and general MLLMs in aligning with human judgment, especially in complex scenarios involving image misalignment or multiple distortions, and can even be extended to non-reference applications.keepSave to notecopy_alldocsAdd noteaudio_magic_eraserAudio OverviewflowchartMind Map

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 limitations of current techniques, emphasizing the necessity for distortion-specific and application-tailored IQA solutions. Ultimately, the authors advocate for future IQA development to prioritize practicality, interpretability, and ease of implementation within specific application contexts.keepSave to notecopy_alldocsAdd noteaudio_magic_eraserAudio OverviewflowchartMind Maparrow_downwardJump to bottom

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 prompt retrieval framework that automates the selection process through unsupervised and supervised methods. Their experiments demonstrate that these methods, especially the supervised one, consistently outperform random selection across various computer vision tasks, indicating the framework's potential for black-box adaptation in visual foundation models.keepSave to notecopy_alldocsAdd noteaudio_magic_eraserAudio OverviewflowchartMind Map

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. Partial2Global addresses these challenges by employing a transformer-based list-wise ranker for a more comprehensive comparison of example alternatives, coupled with a consistency-aware ranking aggregator that ensures globally consistent rankings. Through experiments on tasks like foreground segmentation and object detection, the authors demonstrate that their method consistently outperforms existing techniques, achieving new state-of-the-art results by providing superior in-context examples. The framework effectively mitigates limitations of previous approaches, which struggled with incomplete information or inconsistent ranking predictions.

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 Large Language Model (LLM) Reasoning, the externalist approach, treats LLMs as knowledge repositories to generate plausible causal hypotheses about real-world phenomena, relying on generalized patterns rather than a model's specific internal states. A comparative case study involving movie recommendations illustrates how ABCD can explain a model's unique learned behavior, which LLMs, despite their vast knowledge, cannot, as they are designed to explain the world, not another model's specific reasoning. Ultimately, the source argues that these methods are complementary, not competing, and suggests integrating them for more robust and trustworthy AI explanations, where ABCD diagnoses the model and LLMs translate these technical insights for human understanding.

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 model's superior performance in predicting song and playlist preferences compared to existing methods, highlighting its ability to capture complex, higher-order relationships in consumer behavior. Furthermore, it showcases practical managerial applications such as generating personalized playlists, recommending new items, and adapting to evolving consumer tastes, extending the framework to other domains like food recipe collections.

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 capabilities among LLM agents, leading to an imbalanced game where users with less capable agents may face financial disadvantages. Furthermore, the study highlights critical behavioral anomalies in LLMs, such as constraint violations (overspending or selling below cost), excessive payments, negotiation deadlocks, and early settlements, demonstrating how these can translate into tangible economic losses for users.