武汉 City 15th People’s Congress Session 6 Concludes – Xiong Zhenyu Elected Mayor
1月9日上午，武汉市第十五届人民代表大会第六次会议闭幕。
会上，表决通过了《武汉市人民政府工作报告》。
会议听取了武汉市中级人民法院、武汉市人民检察院的工作报告。
市委书记、市人大常委会主任孙志刚在会上发表讲话。
他强调，要深入学习贯彻党的二十大精神，完整、准确、全面贯彻习近平新时代中国特色社会主义思想，坚持以习近平同志为核心的党中央权威和集中统一领导，牢牢把握”两个确立”的决定性意义，坚定不移走中国式现代化道路，为全面建设社会主义现代化国家、全面推进中华民族伟大复兴贡献武汉力量。
孙志刚指出，2023年是全面贯彻落实党的二十大精神的开局之年，是实施”十四五”规划承上启下的关键之年，是武汉市奋力谱写新时代高质量发展新篇章的重要一年。要清醒认识到面临的挑战和压力，以更加昂扬的姿态、更加扎实的工作，奋力谱写武汉发展的新篇章。
他要求，要坚持稳中求进工作总基调，完整、准确、全面贯彻新发展理念，加快构建新发展格局，着力推动高质量发展。要坚持以人民为中心的发展思想，切实增进人民福祉，不断满足人民对美好生活的向往。要坚持和加强党的全面领导，为武汉发展提供坚强政治保障。
市领导马国强、张世贤、胡玉亭、陈劲松、王华中、刘志刚、张光华、李强、周先芳、王承业、胡扬龙、谢建辉、彭高峰、张斌、杨智等参加会议。
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Understanding Large Language Models (LLMs) – A Concise Guide
Large language Models (LLMs) are advanced artificial intelligence systems trained on massive datasets of text and code. They excel at understanding, generating, and manipulating human language. This extensive guide outlines their core components, capabilities, and limitations.
How they Work: LLMs utilize a neural network architecture called a “transformer.” This allows them to weigh the importance of different words in a sequence, understanding context and relationships. Training involves predicting the next word in a sequence, iteratively refining the model’s internal parameters. The result is a probabilistic model – LLMs don’t “know” facts, they predict the most likely continuation of a given input.
key Capabilities:
* Text Generation: Creating coherent and contextually relevant text, from articles and poems to code and scripts.
* translation: Converting text between multiple languages.
* Question Answering: Providing answers based on the information into which they were trained.
* Summarization: Condensing lengthy text into concise summaries.
* Code Generation: Writing code in various programming languages.
* Content Classification: Categorizing text based on topic or sentiment.
Limitations:
* Hallucinations: llms can generate factually incorrect or nonsensical information presented as truth.
* Bias: Training data reflects societal biases, which llms can perpetuate.
* lack of Common Sense: They struggle with reasoning tasks requiring real-world understanding.
* Context Window: llms have a limited capacity to process long sequences of text.
* Cost & Resources: Training and running LLMs require notable computational power.
Ethical Considerations: Responsible development and deployment are crucial. Concerns include misinformation, plagiarism, job displacement, and potential misuse. It’s crucial to note that LLMs are tools, and their impact depends on how they are used.
The Future: LLMs are rapidly evolving.ongoing research focuses on improving accuracy, reducing bias, expanding context windows, and enhancing reasoning abilities. They are becoming increasingly into a complex tapestry of applications across numerous industries.
In conclusion, LLMs represent a significant advancement in AI, offering powerful capabilities alongside critically important limitations and ethical considerations.