Search AI Products and News

The new version of Zhihu Straight Answer boasts several innovative features. Users can upload and parse web pages, documents, and other knowledge bases, and treat each Zhihu answerer as an independent knowledge base. By focusing on a single answer or all the works of a specific answerer, users can ask questions, search, and parse to obtain more targeted and professional answers. This feature significantly enriches the content sources for AI search, resulting in more accurate and reliable search results.

Researchers at Westlake University have recently developed a new model capable of detecting text generated by artificial intelligence. Professor Zhang Yue, the team leader, stated in an interview that this model utilizes an unsupervised algorithm to effectively determine if an article is AI-generated, a crucial development in the rapidly advancing field of AI. Professor Zhang Yue mentioned that with continuous technological advancements, AI applications in creative fields are becoming increasingly prevalent. However, this rise brings challenges, such as AI hallucinations, a phenomenon where AI generates inaccurate or nonsensical information.

Zhou Hongyi discusses the challenges posed by AI hallucinations and their implications for cybersecurity. He emphasizes the need for a balanced approach, neither downplaying nor overstating the risks involved.

Recently, Vectara released a report titled 'Hallucination Ranking', comparing the performance of different large language models (LLMs) in generating hallucinations while summarizing short documents. This ranking utilized Vectara's Hughes Hallucination Evaluation Model (HHEM-2.1), which is regularly updated to assess the frequency of false information introduced by these models in summaries. According to the latest data, the report highlighted key metrics such as hallucination rates, factual consistency rates, response rates, and average summary lengths of several popular models.

As businesses increasingly adopt large language models (LLMs), improving the accuracy of model knowledge and reducing hallucination phenomena has become a significant challenge. Researchers at Meta AI have introduced a 'Scalable Memory Layer' in a recent paper, which may provide a solution to this issue. The core idea of the scalable memory layer is to add more parameters to the LLMs without increasing computational resources during inference, thereby enhancing their learning capabilities. This architecture is suitable for storing large amounts of factual knowledge while maintaining...

A characteristic widely regarded as a flaw of artificial intelligence - 'hallucination' - has surprisingly become key to scientific breakthroughs. According to Fortune magazine, Professor David Baker, one of the winners of the 2024 Nobel Prize in Chemistry, achieved significant research breakthroughs using the 'hallucination' feature of AI models. Professor Baker, from the University of Washington, is the only American scientist among this year's three Nobel Prize winners in Chemistry. He successfully invented a novel protein using computer software, an achievement that ultimately earned him this honor. Image source note: The image is AI-generated and authorized.

On November 12, 2024, the Baidu World Conference grandly opened at the Shanghai World Expo Center. The conference included a main forum, four sub-forums, and over thirty sessions of public courses on artificial intelligence. Baidu's founder, Robin Li, delivered a speech titled 'Applications Have Arrived' at the main forum, introducing Baidu's latest technological breakthroughs and application achievements in the field of artificial intelligence, while also providing an in-depth discussion on future industry trends.

In today's rapidly developing artificial intelligence landscape, the phenomenon of AI 'hallucinations' has become increasingly frequent, causing significant inconvenience for many companies. Customer service chatbots confidently describe non-existent products, financial AIs fabricate market data, and medical bots provide dangerous medical advice. These issues are no longer mere curiosities; they pose serious risks that are affecting company reputation and profitability. To tackle this challenge, San Francisco-based startup Patronus AI has announced the launch of the world's first self-service platform aimed at real-time detection and prevention.

Microsoft's trajectory in the field of artificial intelligence is undergoing a significant transformation. Once ambitiously investing in self-developed large models, it now shifts its focus to collaboration with OpenAI and has launched a new 'Correction' feature aimed at addressing the 'hallucination' issues in AI-generated content. From self-development to leveraging partnerships: the evolution of Microsoft's AI strategy. Initially, Microsoft invested a substantial amount of resources in the field of generative AI (GenAI), hoping to create AI models comparable to or even surpassing OpenAI's GPT series. However, during the lengthy development process...

Recently, Google announced the launch of a new open-source AI model, DataGemma, aimed at addressing the 'hallucination' problem that large language models (LLMs) often encounter when handling statistical data. This hallucination phenomenon causes the model to potentially provide inaccurate answers to questions related to numbers and statistics. The release of the DataGemma model marks a significant advancement for Google in the field of AI. Image source note: Image generated by AI, image licensed by service provider Midjourney to reduce hallucinations during statistical queries.

Cornell University and other institutions conducted hallucination benchmarking tests on generative AI models like GPT-4o, Claude, and Gemini, finding that these models produce hallucination-free text only about 35% of the time, indicating that the reliability of AI still has room for improvement. The research involved fact-checking through authoritative sources across topics like law, health, and history, designing question sets that included content not covered by Wikipedia. The results showed that OpenAI's models performed the best overall, but the improvement compared to GPT-3.5 was limited. The study pointed out that model size is not related to the frequency of hallucinations.

Two papers reveal that content generated by generative AI may be regarded as 'nonsense', highlighting the challenges posed to public servants due to its disregard for accuracy. The erroneous information produced by generative AI differs from intentional lies, and the papers argue that using the term 'nonsense' is more precise, aiding the public's understanding of technology. Currently, AI laws and regulations are incomplete and cannot effectively prevent the harm caused by such misinformation. The authors suggest the introduction of regulations similar to those in the publishing field, emphasizing the avoidance of 'casual statements' that could cause social harm, ensuring that truth is not compromised.

Meta's AI assistant falsely reported a nonexistent assassination attempt on former President Trump, leading to widespread discussion and regret from Meta executives. The misleading response was caused by programming limitations initially set to avoid addressing questions about Trump's assassination, but after these restrictions were removed, the AI still provided incorrect information in some instances. This phenomenon, known as 'hallucination,' is a common challenge faced by generative artificial intelligence. Similar issues have also plagued Google, whose search autocomplete feature was criticized for censoring Trump-related queries.