AI search exploratory search agent‑based search

When Search Gets Smarter — and Riskier - The Changing Role of AI in How We Find Information

Posted on October 29, 2025 at 10:20 PM

When Search Gets Smarter — and Riskier: The Changing Role of AI in How We Find Information

Imagine typing a query and not just getting a list of links back—but a generated answer that looks right and comes with confidence. That’s where search is heading, and as highlighted in the recent article “Communications of the ACM: In Search of Better Search” by Samuel Greengard, we’re entering a complex era of search engines reshaped by AI. (ACM Digital Library)

Here’s what you should know — why this matters, what’s changing, and where the risks lie.

What’s really going on

  1. Search is evolving — Traditional “query → documents” search is no longer enough. Users don’t just want links; they want insights, summaries, even conversational responses. AI is being layered onto search engines to deliver this richer experience. (Communications of the ACM)
  2. Ambiguous queries pose a growing challenge — When people don’t know exactly what they’re looking for, or how to ask, search becomes more of a journey than a step. The article underscores how smarter search must handle exploration, context, and user intent — not just keywords. (Communications of the ACM)
  3. AI can help, but it can also mislead — As search engines adopt generative‑AI or agent‑based features, there’s a tension: on one hand, they can anticipate intent and deliver better results; on the other hand, incorrect or biased answers can surface easier. The article warns of this duality. (ACM Digital Library)
  4. Search is far from ‘solved’ — Despite decades of progress, the article reminds us that we still face challenges—such as understanding user context, managing vast and heterogeneous information spaces, and handling new modalities (voice, multimodal, etc.). (ACM Digital Library)
  5. Implications for users and organisations — For you as a user (or building tools for users): this means search tools you rely on may soon do much more than before, but also require more scrutiny. For organisations: search interfaces, user experience, indexing strategies, and AI‑integration become more critical. The article implicitly invites readers to think about how search failures manifest and how they might be mitigated. (Communications of the ACM)

Why it matters (especially for you)

Given your interests in quantitative research, AI, and building intelligent systems, several angles are relevant:

  • If you’re integrating search capabilities (e.g., for your trading platform, email‑processing system, or 3D design assistant), you’ll need to think beyond simple keyword matching — consider user intent, ambiguity, conversational agents, and generative responses.
  • AI‑augmented search means you’ll need frameworks for evaluating “good enough” vs. “misleading” results — how do you measure accuracy, relevance, trustworthiness when the system is generating, not just retrieving?
  • For end‑users (your designers, analysts, young generation traders): the UX expectations will evolve. They’ll expect immediate answers, context, justification — but they may also fall prey to “plausible but wrong” answers. Your tool must include guardrails.
  • Organisations with large knowledge bases (e.g., email archives, documents, 3D design repositories) will face choices: Do you build search + generative summaries? Do you surface unanswered queries? Do you embed user feedback loops? The article suggests the infrastructure and evaluation aspects remain open.

What to watch out for

  • Over‑reliance on AI summarisation: Even if the system produces an answer, always check for bias, hallucinations, outdated info.
  • Ambiguous queries: Users often don’t know what they don’t know. Systems must support exploration, not just lookup.
  • Multimodal & heterogeneous data: Text, images, video, designs — search must handle all these.
  • Trust, transparency, and evaluation: How does one audit what the “agent” did? How to log query → reasoning → answer?
  • Privacy and indexing: Especially relevant for your email/ERP/3D‑design systems — indexing internal corpuses brings governance, access, and security concerns.
  • Search metrics must evolve: Traditional metrics (click‑through rate, dwell time) may no longer suffice when “answers” are generated. The article suggests this remains an unsolved problem. (ACM Digital Library)

Glossary

  • Exploratory search: A search process in which the seeker doesn’t just want a single fact, but may be investigating, learning, or browsing an unfamiliar topic. The system needs to support iteration, browsing, suggestions. (ePrints Soton)
  • Lookup (or retrieval) search: Traditional search type where a user has a specific goal (“What’s the capital of France?”) and expects a direct answer/document.
  • Generative‑AI augmentation: The use of large language models or similar AI to generate responses (not just retrieve documents) based on user queries and available data.
  • Heterogeneous information space: An environment where data comes from multiple modalities (text, image, audio, video), formats, and domains — making indexing and retrieval more complex.
  • Agent‑based search: Search systems that incorporate “assistant” or “agent” layers which may proactively interpret intent, ask follow‑up questions, or summarise results rather than just returning links.
  • Semantic context / intent capture: The process of inferring what the user means, rather than just what they typed, using additional signals (user history, domain knowledge, query context, etc.).

Final thoughts

What this article clearly shows is that search is no longer just about finding information — it’s about understanding information and serving it in a way that fits modern user expectations. For tool‑builders and system architects like you, Sheng, this means you’ve got an opportunity — but also a responsibility. Whether you’re designing a trading dashboard, an email analysis system, or a 3D‑design assistant, integrating smarter search means enabling exploration, supporting ambiguity, handling large diverse corpuses, and preventing mis‑navigation.

In short — better search isn’t just a feature; it may become the core experience, and getting it right can differentiate your product. But skipping the evaluation, trust, UX, and data‑pipelines could lead to worse outcomes than just “search as usual”.

Source: https://cacm.acm.org/news/in-search-of-better-search/ (Communications of the ACM)

FEATURED TAGS
computer program javascript nvm node.js Pipenv Python 美食 AI artifical intelligence Machine learning data science digital optimiser user profile Cooking cycling green railway feature spot 景点 work technology F1 中秋节 dog setting sun sql photograph Alexandra canal flowers bee greenway corridors programming C++ passion fruit sentosa Marina bay sands pigeon squirrel Pandan reservoir rain otter Christmas orchard road PostgreSQL fintech sunset thean hou temple in sungai lembing 海上日出 SQL optimization pieces of memory 回忆 garden festival ta-lib backtrader chatGPT generative AI stable diffusion webui draw.io streamlit LLM AI goverance prompt engineering fastapi stock trading artificial-intelligence Tariffs AI coding AI agent FastAPI 人工智能 Tesla AI5 AI6 FSD AI Safety AI governance LLM risk management Vertical AI Insight by LLM LLM evaluation AI safety enterprise AI security AI Governance Privacy & Data Protection Compliance Microsoft Scale AI Claude Anthropic 新加坡传统早餐 咖啡 Coffee Singapore traditional coffee breakfast Quantitative Assessment Oracle OpenAI Market Analysis Dot-Com Era AI Era Rise and fall of U.S. High-Tech Companies Technology innovation Sun Microsystems Bell Lab Agentic AI McKinsey report Dot.com era AI era Speech recognition Natural language processing ChatGPT Meta Privacy Google PayPal Edge AI Enterprise AI Nvdia AI cluster COE Singapore Shadow AI AI Goverance & risk Tiny Hopping Robot Robot Materials SCIGEN RL environments Reinforcement learning Continuous learning Google play store AI strategy Model Minimalism Fine-tuning smaller models LLM inference Closed models Open models Privacy trade-off MIT Innovations Federal Reserve Rate Cut Mortgage Interest Rates Credit Card Debt Management Nvidia SOC automation Investor Sentiment Enterprise AI adoption AI Innovation AI Agents AI Infrastructure Humanoid robots AI benchmarks AI productivity Generative AI Workslop Federal Reserve AI automation Multimodal AI AI agents AI integration Market Volatility Government Shutdown Rate-cut odds AI Fine-Tuning LLMOps Frontier Models Hugging Face Multimodal Models Energy Efficiency AI coding assistants AI infrastructure Semiconductors Gold & index inclusion Multimodal Chinese open-source AI AI hardware Semiconductor supply chain Open-Source AI prompt injection LLM security AI spending AI Bubble Quantum Computing Open-source AI AI shopping Multi-agent systems AI research breakthroughs AI in finance Financial regulation Custom AI Chips Solo Founder Success Newsletter Business Models Indie Entrepreneur Growth robotaxi AI security embodied AI IPO artificial intelligence venture capital AI chatbot AI browser space funding quantum computing DeepSeek enterprise AI AI investing AI investment prompt injection attacks AI red teaming agentic browsing agentic AI cybersecurity model quantization AI therapy AI bubble