首先，我们需要了解一下什么是ChatGPT?
ChatGPT是一种基于人工智能技术的对话机器人，它可以通过学习人类语言来实现自然、流畅的人机对话。ChatGPT的应用领域非常广泛，它可以用于客服、教育、娱乐,金融等领域。例如，它可以作为自动客服助手，为用户提供快速、准确的回答；它也可以作为在线教育助手，为学生提供知识查询和学习辅导；此外，它还可以作为娱乐助手，为用户提供有趣的对话和游戏。
简单举例来说，就是类似于小爱，小度，Siri。ChatGPT可以做人类的文字处理助手，比如可以在一分钟之内写好原创演讲稿，事迹，书信等。还可以做数据整理师，输入工作的数据，比如最近10年的气温最高值，最低值，平均值。ChatGPT可以很快把整理好的数据结果反馈回来。不需要再去网上搜索整理，节省了很多时间。如果再复杂一些，它也可以帮助程序员写程序，如果你是一个程序员，只需要把需求提出来，让ChatGPT按照需求去设置各种变量。这样ChatGPT马上就按照要求写好代码反馈回来。
另外就是在沟通的话术方面，确实很贴合日常人类的语言话术，比如尝试了一下聊天对话：ChatGPT的介绍和推荐的热门留学国家也确实是我们经常给学生的介绍答复。
二、人工智能软件的精进，那么是否会取代人类价值呢？
在我看来，答案是否定的，至少目前是遥不可及的。目前人工智能虽然取得了巨大的进步，但是我个人认为还是处在“弱人工智能”阶段，我们可以通过人工智能去协助人类做一些枯燥的工作，可以避免犹豫人为意识造成的疏忽，但是在面对突发状况或者紧急事件的时候，人工智能还不能做出明智的决定和反应，还是需要人类大脑去判断，处理突发事件。比如chatgpt只是一个生成“合理”文本的AI，甚至不是一个对问题给出“正确”答案的AI。而“合理”也仅是约等于“正确”而已，还达不到“强人工智能”。
三、美国开设人工智能的顶尖高校有哪些，具体的课程设置和要求是什么？
美国USNEWS, 针对AI 专业排名前10名如下：
美国AI 专业排名第一名，卡耐基梅隆大学关于人工智能专业介绍如下：
Founded in 2018 as a successor to the M.S. in Biotechnology, Innovation and Computation (MSBIC) program, the MSAII program trains professional master's students to develop large-scale AI solutions. In the program, students delve deeply into topics such as machine learning, natural language processing. Through core classes, knowledge area courses and electives, students are exposed to a wide variety of AI techniques’, while our internship and capstone project requirements ensure that you'll have the knowledge and experience you need to be successful in your professional career.
Though housed in the Language Technologies Institute, the MSAII program draws from faculty members throughout Carnegie Mellon's top-ranked School of Computer Science, including the Computer Science Department, Machine Learning Department and the Institute for Software Research.
MSAII项目成立于2018年，是生物技术、创新和计算硕士(MSBIC)项目的延伸项目，旨在培养专业硕士学生开发大规模人工智能解决方案。在该课程中，学生将深入研究机器学习、自然语言处理等主题。通过核心课程、知识领域课程和选修课，学生可以接触到各种各样的人工智能技术，而我们的实习和顶点项目要求确保您拥有在职业生涯中取得成功所需的知识和经验。
虽然位于语言技术学院，但MSAII项目的教职人员来自卡内基梅隆大学排名第一的计算机科学学院，包括计算机科学系、机器学习系和软件研究所。
Preparation Prerequisite 先修课程
Historically, students typically need a refresher on basic computer science systems before beginning graduate work at CMU. You must pass the undergraduate course 15-513 Introduction to Computer Systems (6 units), typically in the summer before your program commences. (This course is the distance education version of 15-213 Introduction to Computer Systems.) Failure to pass the course means that you have to take 15-213 during either the fall or spring semester, and the units will not count toward your 192 eligible units of study.
从历史的角度来看，学生在开始CMU的研究生工作之前通常需要复习基本的计算机科学系统。你必须通过本科课程15-513计算机系统概论(6个学分)，通常是在你的课程开始前的暑期。(本课程是15-213计算机系统导论的远程教育版本。)没有通过这门课程意味着你必须在秋季学期或春季学期选修15-213门课程，这些课程将不计入你的192个合格的学分中。
Curriculum Components 课程模式
Each major has different core curriculum requirements.
Core Curriculum (84 units) 核心课程
This is a five-course sequence based on the four main phases of innovation development, including opportunity identification, opportunity development, business planning and incubation of a business with a viable product. The courses must be taken in the order listed:
1. 11-651, Artificial Intelligence and Future Markets (12 units). First fall semester. In this course, students are divided into teams to survey the fieild of AI applications, make presentations to the faculty and fellow students on areas that are ripe for AI development, and must develop a product proposal, which will be carried through for the next three semesters, leading to 11-699, the Capstone Project.
2. 17-762, Law of Computer Technology (12 units). First fall semester. A review of legal principles applicable to computer developments, including AI law and formation of startups.
3. 11-695, AI Engineering (12 units). First spring semester. This course is devoted to building deep learning applications using TensorFlow and Python. Topics include supervised learning, feed-forward neural networks, flow graphs, dynamic computational graphs, convolutional neural networks and recurrent neural networks. Students will use high-level tools to engineer functioning machine learning models.
4. 11-654, AI Innovation (12 units). Second fall semester. Students learn how to build an enterprise, either intrapreneurial or entrepreneurial, by developing a business model and strategy for their team's product.
5. 11-699, Capstone Project (36 units). Second spring semester. The objective of the Capstone is for your team to develop a working product suitable for intrapreneurial integration into a company or suitable for startup investment.
这是一个基于创新发展的四个主要阶段的五个课程序列，包括机会识别、机会开发、商业规划和具有可行产品的业务孵化。课程必须按照下列顺序学习:
1. 11-651，人工智能和未来市场(12个学分)。第一学期。在这门课程中，学生被分成小组调查人工智能应用领域，就人工智能发展的成熟领域向教师和同学进行演示，并必须制定一份产品提案，该提案将在接下来的三个学期中进行，最终形成11-699，即顶点项目。
2. 17-762，计算机技术法(12个学分)。第一学期。审查适用于计算机发展的法律原则，包括人工智能法律和创业公司的形成。
3. 11-695，人工智能工程(12个学分)。春季第一学期。本课程致力于使用TensorFlow和Python构建深度学习应用程序。主题包括监督学习，前馈神经网络，流图，动态计算图，卷积神经网络和循环神经网络。学生将使用高级工具来设计有效的机器学习模型。
4. 11-654, AI创新(12个学分)。第二学期。学生们学习如何通过为他们团队的产品开发商业模式和战略来建立一个企业，无论是创业型还是创业型。
5. 11-699，顶点项目(36个学分)。第二个春季学期。Capstone的目标是让你的团队开发一款适合内部创业整合到公司或适合创业投资的工作产品。
Internship 实习项目
Every student is required to complete an industry internship during the summer between the first spring and second fall semesters. Every student must register for the internship - 11-934 (MSAII Practicum Internship). No tuition is charged for the internship.
每个学生都必须在春季第一学期和秋季第二学期之间的夏季完成一个行业实习。每个学生都必须注册实习- 11-934 (MSAII实习)。实习不收学费。
选修课
Electives (36 units)36学分
You must take at least three 12-unit elective courses or equivalent. The approved electives are listed below. If you want to take any other course for elective credit, you must have the permission of the MSAII Director. It is recommended to take one elective in the first fall semester, one or two in the first spring semester, one or two in the second fall semester and zero or one in the second spring semester.必须修至少三门12学分的选修课程或同等学历。批准的选修课如下所示。如果你想选修任何其他课程，你必须得到MSAII主任的许可。建议秋季第一学期选一门选修课，春季第一学期选一门或两门选修课，秋季第二学期选一门或两门选修课，春季第二学期选零门或一门选修课。
11-641 Machine Learning for Text Mining
11-642 Search Engines
11-747 Neural Networks for NLP
11-755 Machine Learning for Signal Processing
11-777 Advanced Multimodal Machine Learning
10-605 Machine Learning with Large Datasets
10-608 Conversational Machine Learning
10-716 Advanced Machine Learning: Theory & Methods (was 10702)
15-624 Foundations of Cyber-Physical Systems
15-645 Database Systems
15-688 Practical Data Science
15-719 Advanced Cloud Computing
15-780 Graduate Artificial Intelligence
16-720 Computer Vision
16-725 Medical Image Analysis
16-772 Sensing and Sensors
16-824 Visual Learning and Recognition
17-637 Web Application Development
17-639 Management of Software Development
17-653 Managing Software Development
17-766 Software Engineering for Startups
02-604 Fundamentals of Bioinformatics
02-718 Computational Medicine
申请要求：Requirements
A GPA of 3.0 or higher. (Students should report raw university GPA scores and NOT converted scores. Please DO NOT convert your international score to a US GPA or weighted GPA or other system). GRE scores: GRE is required. Our Institution Code is 2074; Department Code is 0402. TOEFL/IELTS/Duolingo scores: If you are an international applicant and your native language (language spoken from birth) is not Enlgish, an official copy of English proficiency score report is required. The English proficiency requirement cannot be waived for any reason. We strongly encourage applicants to take either the TOEFL or IELTS. In cases where these are not available it is acceptable to take the Duolingo test. We discourage the use of the TOEFL IPT Plus for China since speaking is not scored. Successful applicants will have a minimum TOEFL score of 100. Our Institution Code is 4256; the Department Code is 78. Unofficial transcripts from each university you have attended, regardless of whether you received a degree. Current resume. Statement of Purpose. A Statement of Purpose is not a resume. It should discuss your reasons for choosing the MSAII program and indicate your intended career path. Three letters of recommendation. A short (1-3 minutes) video of yourself. Tell us about you and why you are interested in the MSAII program. This is not a required part of the application process, but it is STRONGLY suggested.
常见问题：
I have an undergraduate degree in a field outside of computer science. Am I eligible to apply to the MSAII program with this degree? Will a computer science undergraduate have an advantage over me? 本科不是计算机专业相关领域的背景，可以申请吗？
Most of the MSAII courses require an undergraduate-level background in statistics or computer science. Thus, the applicant must show evidence of mastery of this material. This evidence is automatically available for students with an undergraduate degree in CS from top universities. Most students with non-CS degrees haven't taken a sufficient number of courses in computer science. But if you have, you should make this evidence explicitly clear in your application.
MSAII的大部分课程都要求有统计学或计算机科学的本科背景。因此，申请人必须证明掌握了这些材料。这一证明对于顶尖大学的计算机科学本科学位的学生来说是可用的。大多数拥有非计算机科学学位的学生没有学习足够数量的计算机科学课程。但如果你有，你应该在你的申请中明确地说明。
The application process asks for publications. Are these required?
申请过程中，必须提供发表过的刊物或者文章吗？
No, publications are not required, but they good evidence of your ability to innovate.We do not require publications because the MSAII is not intended as a research degree. Some of our admitted students have already published articles. Publications in lesser-known conferences are still valuable, although all publications should be easily verifiable if not directly linked in your application.
不，你不需要发表作品，但如果有发表的作品，那它们是你创新能力很好的证明。我们不需要出版物，因为MSAII不是一个研究学位。我们录取的一些学生已经发表了文章。在不太知名的会议上发表的文章仍然是有价值的，尽管所有的出版物如果没有直接链接到您的应用程序中，应该很容易验证。
The application asks for work experience. Is this required?
申请过程，必须提供工作经验吗？
No, work experience is not required. However, we value some types of work experience highly, particularly if it is similar to the type of work our graduates perform, or highly relevant to your interest in the program. Work experience can demonstrate to us and prospective employers that you have the ability to function in a job setting instead of just an academic one.
不需要，不需要工作经验。但是，我们非常重视某些类型的工作经验，特别是如果它与我们毕业生的工作类型相似，或者与您对该项目的兴趣高度相关。工作经验可以向我们和未来的雇主证明，你有能力胜任工作环境，而不仅仅是学术环境。
