AI tools including Perplexity and Open AI's GPT-4 often provide one-sided answers to contentious questions, and don't back up their arguments with reliable sources How well-supported are the claims made by AI tools? Generative AI tools, and the deep research agents and search engines powered by them, frequently make unsupported and biased claims that aren't backed up by the sources they cite. That's according to an analysis which found that about one-third of answers provided by the AI tools aren't backed up by reliable sources. For OpenAI's GPT 4.5, the figure was even higher, at 47 per cent. Alongside this, they put five deep research agents through their paces: GPT-5's Deep Research feature, Bing Chat's Think Deeper option and deep research tools offered by You.com, Google Gemini and Perplexity.

AI services like Perplexity or OpenAI's SearchGPT could be search engine options in a future version of Safari, Bloomberg reports. The tentative plans were shared by Eddy Cue, Apple's senior vice president of services, while on the stand for Google's ongoing search antitrust case. Cue was called to testify because of the deal Google and Apple have to keep Google Search as the default search engine on the iPhone. Cue claims Apple has discussed a possible Safari-integration with Perplexity, but didn't share any definitive plans during his testimony. It's clear that he believes AI assistants will inevitably supplant traditional search engines, though.

The advent of Large Language Models (LLMs) has paved the way for AI search engines, e.g., SearchGPT, showcasing a new paradigm in human-internet interaction. However, most current AI search engines are limited to text-only settings, neglecting the multimodal user queries and the text-image interleaved nature of website information. Recently, Large Multimodal Models (LMMs) have made impressive strides. Yet, whether they can function as AI search engines remains under-explored, leaving the potential of LMMs in multimodal search an open question. To this end, we first design a delicate pipeline, MMSearch-Engine, to empower any LMMs with multimodal search capabilities. On top of this, we introduce MMSearch, a comprehensive evaluation benchmark to assess the multimodal search performance of LMMs. The curated dataset contains 300 manually collected instances spanning 14 subfields, which involves no overlap with the current LMMs' training data, ensuring the correct answer can only be obtained within searching. By using MMSearch-Engine, the LMMs are evaluated by performing three individual tasks (requery, rerank, and summarization), and one challenging end-to-end task with a complete searching process. We conduct extensive experiments on closed-source and open-source LMMs. Among all tested models, GPT-4o with MMSearch-Engine achieves the best results, which surpasses the commercial product, Perplexity Pro, in the end-to-end task, demonstrating the effectiveness of our proposed pipeline. We further present error analysis to unveil current LMMs still struggle to fully grasp the multimodal search tasks, and conduct ablation study to indicate the potential of scaling test-time computation for AI search engine. We hope MMSearch may provide unique insights to guide the future development of multimodal AI search engine. Project Page: https://mmsearch.github.io

The discovery of new superconducting materials, particularly those exhibiting high critical temperature ($T_c$), has been a vibrant area of study within the field of condensed matter physics. Conventional approaches primarily rely on physical intuition to search for potential superconductors within the existing databases. However, the known materials only scratch the surface of the extensive array of possibilities within the realm of materials. Here, we develop an AI search engine that integrates deep model pre-training and fine-tuning techniques, diffusion models, and physics-based approaches (e.g., first-principles electronic structure calculation) for discovery of high-$T_c$ superconductors. Utilizing this AI search engine, we have obtained 74 dynamically stable materials with critical temperatures predicted by the AI model to be $T_c \geq$ 15 K based on a very small set of samples. Notably, these materials are not contained in any existing dataset. Furthermore, we analyze trends in our dataset and individual materials including B$_4$CN$_3$ and B$_5$CN$_2$ whose $T_c$s are 24.08 K and 15.93 K, respectively. We demonstrate that AI technique can discover a set of new high-$T_c$ superconductors, outline its potential for accelerating discovery of the materials with targeted properties.

Large language models (LLMs) and retrieval-augmented generation (RAG) techniques have revolutionized traditional information access, enabling AI agent to search and summarize information on behalf of users during dynamic dialogues. Despite their potential, current AI search engines exhibit considerable room for improvement in several critical areas. These areas include the support for multimodal information, the delivery of personalized responses, the capability to logically answer complex questions, and the facilitation of more flexible interactions. This paper proposes a novel AI Search Engine framework called the Agent Collaboration Network (ACN). The ACN framework consists of multiple specialized agents working collaboratively, each with distinct roles such as Account Manager, Solution Strategist, Information Manager, and Content Creator. This framework integrates mechanisms for picture content understanding, user profile tracking, and online evolution, enhancing the AI search engine's response quality, personalization, and interactivity. A highlight of the ACN is the introduction of a Reflective Forward Optimization method (RFO), which supports the online synergistic adjustment among agents. This feature endows the ACN with online learning capabilities, ensuring that the system has strong interactive flexibility and can promptly adapt to user feedback. This learning method may also serve as an optimization approach for agent-based systems, potentially influencing other domains of agent applications.

When people type a question into Perplexity, the two-year-old search engine scours the internet and uses information from multiple sources, including online publishers, to synthesize an answer using AI. Soon, Perplexity will start sharing revenue with some publishers as part of an advertising platform it plans to launch around the end of September, the company announced on Tuesday. The initiative, known as the Perplexity Publishers' Program, comes less than two months after the San Francisco-based startup backed by investors like Jeff Bezos and NVIDIA, and valued at 3 billion, came under fire from Forbes, Wired, and Condé Nast for allegedly scraping content without permission and ignoring robots.txt, Perplexity's initial partners include TIME, Fortune, The Texas Tribune, Der Spiegel and Automattic, the company behind Wordpress.com. It's not clear exactly how much revenue Perplexity will share with publishers.

When Reddit said last month it would block unauthorized data scraping from its site, most of us assumed it was to tackle chatbot training. It turns out the site/service/fandom battleground also appears to be blocking search engines other than Brave and Google, the latter of which reportedly inked a deal earlier this year with Reddit worth 60 million annually. A Reddit spokesperson told Engadget the empty search results are because these engines won't agree to the company's requirements for AI training. The company says it's in discussions with several of them. Bing and DuckDuckGo both appear to be affected.

If you think search engines powered by artificial intelligence, such as Microsoft's Bing Chat, are providing you with useful-sounding answers, it is more likely that they are wrong, researchers have found. "In these current systems, accuracy is inversely correlated with perceived utility," says Nelson Liu at Stanford University. "The things that look better end up being worse."

So far, there doesn't seem to be an awful lot that ChatGPT – the chatbot powered by artificial intelligence (AI) – can't do. It has been used to pass exams, deliver a sermon, write software and give relationship advice -- to name just a handful of its functions. The bot is currently free for anyone to use, meaning that lots of users have been asking it questions to get the information they need in their daily lives. Since the turn of the millennium, this job has been primarily reserved for Google -- the world's most popular search engine and its $149 billion (£120 billion) business. And, if so, which of the warring tech giants will get there first?

Artificial intelligence can now rapidly search through billions of aerial and satellite images to find similar buildings or land features, such as football fields and Arctic ponds. This capability could help researchers classify the amount of land taken up by forests or farms, or could be used by militaries to identify bases or specific weapons used by other countries.