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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.

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.

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.

While large language models like ChatGPT, Claude, and Gemini are impressive, they all share a significant issue: they often generate hallucinations. This is a serious problem in the field of artificial intelligence, and even Apple has expressed concerns about how future Apple Intelligence will handle hallucinations. Fortunately, a team of researchers has now developed an AI hallucination detector that can determine whether the AI is fabricating content.

Galileo Lab launches new metrics to help improve the accuracy of generative AI. OpenAI GPT-4 has a 23% tendency for hallucination in basic question-answering tasks. The new metrics from Galileo Labs include correctness and contextual consistency. These new tools can help businesses test and mitigate hallucination issues in AI implementations.

["Experts are concerned about the hallucination phenomenon present in popular AI platforms like ChatGPT.", "AI hallucination is a phenomenon where information appears correct but is actually inaccurate.", "ChatGPT has been pointed out for providing false information, raising the attention of scientists."]