Advertisements will inevitably become a regular feature in AI-powered chatbots. Smaller startups are already monetizing their chatbots through advertising plugins. The future of ad-driven chatbots will depend on how developers and marketers implement ads.
Tuesday, March 19, 202481% of consumers are willing to wait between 1 to 11 minutes to speak with a human customer service rep instead of chatting with an AI assistant. 22% of respondents are willing to wait 4-5 minutes. Humans are still perceived as better suited for complex problem-solving, one-call issue resolution, and empathetic interactions.
Meta's new AI Studio allows users to create, share, and discover AI chatbots easily, even without technical skills. The feature is rolling out to Instagram Business accounts in the US and will be available to all Meta users soon, accessible through the web, Instagram, Messenger, and WhatsApp.
Meta's new AI Studio tool will soon allow users without technical skills to create personalized AI chatbots for Instagram, Messenger, and WhatsApp. The tool will enable customized interactions with followers and full control over auto-replies.
Recent research has highlighted a concerning trend in the performance of larger artificial intelligence (AI) chatbots, revealing that as these models grow in size and complexity, they are increasingly prone to generating incorrect answers. This phenomenon is particularly troubling because users often fail to recognize when the information provided by these chatbots is inaccurate. The study, conducted by José Hernández-Orallo and his team at the Valencian Research Institute for Artificial Intelligence, examined three prominent AI models: OpenAI's GPT, Meta's LLaMA, and the open-source BLOOM model. The researchers analyzed how the accuracy of these models changed as they were refined and expanded, utilizing more training data and advanced computational resources. They discovered that while larger models generally produced more accurate responses, they also exhibited a greater tendency to answer questions incorrectly rather than admitting a lack of knowledge. This shift means that users are likely to encounter more incorrect answers, as the models are less inclined to say "I don't know" or to avoid answering altogether. The study's findings indicate that the fraction of incorrect responses has risen significantly among the refined models, with some models providing wrong answers over 60% of the time when they should have either declined to answer or provided a correct response. This trend raises concerns about the reliability of AI chatbots, as they often present themselves as knowledgeable even when they are not, leading to a phenomenon described as "bullshitting" by philosopher Mike Hicks. This behavior can mislead users into overestimating the capabilities of these AI systems, which poses risks in various contexts, especially when users rely on them for accurate information. To assess the models' performance, the researchers tested them on a wide range of prompts, including arithmetic, geography, and science questions, while also considering the perceived difficulty of each question. They found that while the accuracy of responses improved with larger models, the tendency to provide incorrect answers did not decrease proportionately, particularly for more challenging questions. This inconsistency suggests that there is no guaranteed "safe zone" where users can trust the answers provided by these chatbots. Moreover, the study revealed that human users struggle to accurately identify incorrect answers, often misclassifying them as correct. This misjudgment occurred between 10% and 40% of the time, regardless of the question's difficulty. Hernández-Orallo emphasized the need for developers to enhance AI performance on easier questions and encourage models to refrain from answering difficult ones, thereby helping users better understand when they can rely on AI for accurate information. While some AI models are designed to acknowledge their limitations and decline to answer when uncertain, this feature is not universally implemented, particularly in all-purpose chatbots. As companies strive to create more capable and versatile AI systems, the challenge remains to balance performance with reliability, ensuring that users can navigate the complexities of AI-generated information without falling prey to misinformation.