Companies all over the world are adopting AI rapidly. Is your business? Baidu's AI chatbot, Ernie Bot, has reached a remarkable milestone by attracting over 200 million users, marking its dominance in China's competitive AI sector. Announced by CEO Robin Li, this growth showcases Ernie Bot's widespread adoption among individual users and 85,000 enterprise clients. Despite fierce competition from newer AI services like the Alibaba-backed Kimi, Ernie Bot continues to thrive, utilizing its capabilities to enhance Baidu's revenue through improved advertising services and AI model development. This development highlights China's aggressive expansion and regulation of AI as it vies to compete globally. article

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Gemini 1.5's groundbreaking 1M token context window is a remarkable advancement in LLMs, providing capabilities unlike any other currently available model. With its 1M context window, Gemini 1.5 can ingest the equivalent of 10 Harry Potter books in one go. However, this enormous context window is not without its limitations. In my experience, Gemini 1.5 often struggles to retrieve the most relevant information from the vast amount of contextual data it has access to. The "Needle in a Haystack" benchmark is a well-known challenge for LLMs, which tests their ability to find specific information within a large corpus of text. This benchmark is particularly relevant for models with large context windows, as they must efficiently search through vast amounts of data to locate the most pertinent information. To address this issue, I have developed a novel prompting technique that I call "Multi-Candidate Needle Prompting." This approach aims to improve the model's ability to accurately retrieve key information from within its large context window. The technique involves prompting the LLM to identify 10 relevant sentences from different parts of the input text, and then asking it to consider which of these sentences (i.e. candidate needles) is the most pertinent to the question at hand before providing the final answer. This process bears some resemblance to Retrieval Augmented Generation (RAG), but the key difference is that the entire process is carried out by the LLM itself, without relying on a separate retrieval mechanism. By prompting the model to consider multiple relevant sentences from various parts of the text, "Multi-Candidate Needle Prompting" promotes a more thorough search of the available information and minimizes the chances of overlooking crucial details. Moreover, requiring the model to explicitly write out the relevant sentences serves as a form of intermediate reasoning, providing insights into the model's thought process. The attached screenshot anecdotally demonstrates the effectiveness of my approach.

The rapid advancement of artificial intelligence (AI) is set to redefine the job market, threatening traditional white-collar roles such as management analysts, legal professionals, educators, and financial advisers due to AI's efficiency in data analysis, legal research, and market trend analysis. However, this technological upheaval also paves the way for new opportunities in AI development, ethical oversight, and data science. As the AI landscape evolves, individuals in at-risk professions must adapt through reskilling or upskilling, focusing on areas where human ingenuity remains indispensable. Meanwhile, businesses and policymakers face the dual challenge of leveraging AI's potential for innovation while mitigating its impact on job displacement. article

In a leap towards the future of artificial intelligence, researchers from Loughborough, Yale, and MIT propose developing a collective AI system capable of lifelong learning and instant knowledge sharing across a network. This envisioned AI society could rapidly respond to challenges, share discoveries, and adapt to new situations, from cybersecurity to disaster relief and personalized healthcare. Despite potential risks, the study suggests these AI units could maintain autonomy to prevent the spread of unethical information, marking a significant shift from static AI models to dynamic, sustainable collective intelligence. article here