Artificial Intelligence Handwritten Newspaper Picture 4k Paper (AI and ChatGPT) AI ChatGPT Quick Vie

 

1. Artificial intelligence chatGPT4

AI Artificial Intelligence and ChatGPT: From Silicon Valley to Pudong, Looking at the Advantages and Disadvantages of AI ChatGPT. In December 2022, OpenAI, an AI laboratory invested by Microsoft, released ChatGPT, which immediately became popular across the internet. In just 5 days, it attracted 1 million users to grade a course paper at the University of North Michigan. Papers written using ChatGPT even received an A+rating, and were considered by professors as the "best paper in the class".

2. Artificial Intelligence ChatGPT Download

Its emergence has had a huge impact on many teaching activities, forcing teachers to make adjustments to the official description of the classroom. This is a dialogue model that "can continuously answer questions, admit mistakes, challenge incorrect premises, and refuse inappropriate requirements". From chatting and answering questions to writing novels, poetry, and helping programmers solve program bugs, ChatGPT's ability has amazed many people.

3. How to download chatgpt

Zhang Lili, an information technology expert, worked as an IT architect at Sun Microsystems in the United States in 1995; In the same year, computer scientist Richard Wallace developed the chat robot ALICE. At this time, artificial intelligence robots could already read music scores, interpret images, and express human reasoning. This change was only 30 years after the birth of the world's first chat robot ELIZA in 1964.

4. Artificial Intelligence ChatGPT Concept

In recent years, AI generated content (AIGC) has made rapid progress, and OpenAI has released the GPT-3 model. ChatGPT is a further innovation based on the introduction of "reinforcement learning" in GPT-3, which greatly improves the accuracy and controllability of AI in human-machine dialogue. However, despite the fact that GPT-3 has 175 billion parameters for "memorizing" and "extracting" the laws of human knowledge, ChatGPT quickly exposes its drawbacks in use.

5.chatGPT

For example, the answers given may not be entirely accurate, and even for some common sense questions with accurate answers, vulnerabilities may arise, leading to the well-known programmer Q&A website Stack Overflow urgently issuing an announcement announcing the temporary ban on ChatGPT

6. How to read chatgpt

From chatting and answering questions to writing novels, poetry, and helping programmers solve program bugs, ChatGPT's ability has amazed many people. In the face of this phenomenon, New Weekly interviewed Zhang Lili, who had worked at a technology company in Silicon Valley for more than 10 years in the United States, and discussed the current application status and challenges of AIGC, as well as the potential new entrepreneurial boom caused by the iteration of new technologies.

7. Microsoft's Artificial Intelligence ChatGPT

In March 2022, the Shanghai Municipal Government released the construction implementation plan of "urban Digital transformation". As the first city in China to implement Digital transformation, facing the huge gap of digital technology talent demand, Zhang Lili took the first batch of digital technology application engineer examination courses to teach "what kind of people need to be trained in the future of artificial intelligence applications to" give orders to machines ", This is a question that Zhang Lili often thinks about.

8. The hottest artificial intelligence ChatGPT

In 2006, Zhang Lili returned from Silicon Valley in the United States and joined the Shanghai Council for the Promotion of the Sea. After that, he worked as a senior executive in Fortune 500 companies and engaged in information technology and enterprise technology management, which made his personal experience of urban digitization very deep. Behind the popularity of ChatGPT, the pros and cons of future social development may be the issue that all sectors should pay attention to.

9. Strongest Artificial Intelligence ChatGPT

Nowadays, many people who have used ChatGPT believe that in the future, such technology can not only undermine Google's artificial intelligence, but may also force some programming Q&A websites to face great threats. Some users even have a conversation with ChatGPT about the history of modern physics and exclaim that universities may no longer exist in the future.

10. Artificial Intelligence ChatGPT Composition

However, in Zhang Lili's opinion, these statements are somewhat "over hyped". In 2018, Zhang Lili started his business in Pudong. Because of his sensitivity to the top-level design of the industrial structure, he found that in the Digital transformation of the city, in addition to scientific and technological innovation itself being a challenge, the greater challenge is how to "define" innovation.

Austrian economist Joseph Schumpeter once proposed the "innovation theory" in his book "Economic Development Theory": "As long as an invention has not been actually applied, it will not work in the economy." Zhang Lili proposed his own interpretation of this: if a new tool or method needs to work in economic development, the most important meaning is that it can create new value, and the so-called innovation, Ultimately, it must be universal and able to bring benefits to humanity.

The popularization and application of artificial intelligence often face many difficulties. The upgrading of intelligent products requires people to break through usage habits and old concepts. In Zhang Lili's view, to determine whether ChatGPT is a technological innovation, the first thing to confirm is that ChatGPT is indeed a technological breakthrough.

In 1997, the supercomputer "Deep Blue" defeated the world chess champion Kasparov, and in March 2016, AlphaGo defeated the world Go champion Li Shishi in a "human-machine battle". These cases only focus on the centralization of computing power, algorithms, and models in the segmented vertical fields, but do not have a broader horizontal expansion.

ChatGPT, on the other hand, is more like a 'treasure trove', incorporating a massive data model that can be 'reinforcement learning' into a question and answer system, but how can we ensure that it never goes wrong? Of course, 175 billion parameters are controllable, just like implanting known stem cells into the human body in medicine, but the process of organ regeneration is uncontrollable.

The same goes for the content generated by AIGC, you never know what ivory it will spit out in its mouth, "Zhang Lili said." The reason we find it interesting is that we can write poetry and articles because its results are uncontrollable. ChatGPT has indeed promoted the progress of human intelligence in a narrow sense, but the core issue is that large-scale information output is still in a "black box" state. How to make its results more controllable in the future, It will affect the development of each industry.

Every advancement or even iteration of technology triggers many entrepreneurs and entrepreneurs to think. If a technology can generate economic benefits and benefit society, why not do it? In fact, the popularization and application of artificial intelligence often face many difficulties, after all, the upgrading of intelligent products requires people to break usage habits and old concepts.

Nowadays, artificial intelligence requires the acquisition of more and more personal information from users, which clearly involves the issue of "technological ethics" in privacy protection. In addition, the cost of artificial intelligence solutions is often daunting - humans can already meet all their daily needs through the Internet and software, and this more cost-effective approach inevitably pushes artificial intelligence to the "edge".

But this does not mean that artificial intelligence has developed into ChatGPT, providing entertainment for people. Even in the first August 2022, Zhang Lili was elected as the president of the sixth session of the Shanghai Association for the Promotion of the Sea in the election of the Shanghai Association for the Promotion of the Sea. In addition to frequently attending Shanghai Jiao Tong University as a visiting professor, he also pays special attention to the healthcare and health industry.

In Zhang Lili's view, in the three years since the outbreak of the COVID-19 epidemic, AI has played a significant role in biomedicine. Because biomedicine needs to do a lot of chemical molecule adaptation, AI seems to have found a place to play, and even its application in the future domestic medical industry has a bright future. In the long run, AI is bound to have a long-term impact on human life, And it is not something that can be restricted by any age and generation.

Zhang Lili pointed out that for example, the generation that grew up with mobile payments rarely had a concept of currency. If they were to go to some countries that still use paper currency, On the contrary, they may feel uncomfortable. "Why do many European countries not use online payments? And in the future, will the so-called Internet Indigenous and Metaverse Indigenous create a gap with many people around the world in globalization? From this perspective, they may not necessarily be leaders.

”Zhang Lili stated that he has always maintained an objective and cautious attitude towards the application of AIGC

On September 3, 2022, at the Nanjing Metaverse Industry Development Summit Forum, a company showcased its latest robot product. According to Zhang Lili, in the future, AIGC may take the lead in the application scenarios of technology enterprises because the enterprise itself is an investment entity and has the ability to spend costs.

For example, in a working meeting with dozens of people, AIGC can automatically collate and generate a meeting summary according to the topics and speeches discussed by everyone, and even tabulate the statistical data, which will undoubtedly improve the work efficiency of enterprises. In addition, for example, the commitment of the Chinese government to "3060 carbon peak and carbon neutrality" is imminent. Suppose we want to know the zero carbon standards of different industries in China, Can AIGC quickly retrieve these standards? In the future digital city transformation, when artificial intelligence digitizes the supply chain of the manufacturing industry again, it will not be underestimated for the future national economy and the promotion of globalization development.

Zhang Lili believes that if the knowledge created by humans is divided into social sciences and natural sciences, the above-mentioned applications are basically based on the premise of natural sciences to collect and organize data. However, if it involves the production of content in social sciences such as literature, music, painting, etc., it is still necessary to "point to the point" because if humans lose their imagination and thinking, The ultimate result may be that humans will become increasingly foolish.

In 1984, the movie "Computer Fantasy" directed by Steve Barron was released, with the plot revolved around a love hate conflict between a man, a woman, and a sentient personal computer named "Edgar". Humans seem to have been fantasizing about chatbots being able to generate self-awareness since the last century, but at the Association for the Promotion of Artificial Intelligence (AAAI), Roger Schank, an American artificial intelligence theorist, and Marvin Minsky, the "father of artificial intelligence" and founder of framework theory, once warned the world of the "artificial intelligence winter".

In 1997, the chess game between "Deep Blue" and Kasparov led to a negative view in Western media - how did human failure come so quickly? In Zhang Lili's view, the reason why people were afraid of technology at that time was not because they were worried that robots' brains were better than humans'.

The question of whether artificial intelligence will replace humans is itself a false proposition, and the real reason for fear is that once this robot that speaks human language begins to express its opinions and values, we will be surrounded by specious "lies" that are hard to distinguish

Nowadays, many people are optimistic about the prospects of AIGC in the field of knowledge education. However, for Zhang Lili, this is actually the behavior that requires both caution and caution. If artificial intelligence is to replace human thinking, do we really need to go through this "winter"? Regarding this, Zhang Lili analyzed that the development of artificial intelligence has gone through the "Moore's Law" and "big data" and entered the third to fourth "winter" cycle.

It may not be easy to say whether or to what extent the current "winter" is cold, but everyone can look back and see that whenever these technologies emerge, artificial intelligence actually stagnates from a macro perspective. Only when humans experience the "barrel effect" in their lives will artificial intelligence come into play and fill in the gaps.

For example, mobile phones have navigation and gyroscopes, and we can use GPS and Beidou for positioning. However, this is not beyond the scope of human "clothing, food, housing, and transportation". Although there is a concept of "weak artificial intelligence" that truly makes human life more convenient, no one dares to say words like 'how to subvert human lifestyles through artificial intelligence'.

”Zhang Lili pointed out that nowadays, many people are optimistic about the prospects of AIGC in the field of knowledge education. However, for Zhang Lili, this is actually the most cautious behavior. "The original intention of artificial intelligence is to liberate people's hands and improve productivity. AIGC can provide accurate knowledge listings for people like search engines, but the ultimate choice is whether to believe or not, or whether to make decisions about information ultimately, it must be people themselves.

”Zhang Lili raised his concerns about AIGC. Artificial intelligence can collect data, classify and label data, but it cannot tell us what is good and what is bad. According to Zhang Lili, due to the 2022 Shanghai epidemic, a considerable number of people started online classes because of this, it is necessary to have some interaction to ensure that students are not dozing off, So he often asks questions like "deduct 1 if you understand, deduct 2 if you don't understand", and the screen is filled with "1, 2, 1, 2...", which has become a lingering haze in Zhang Lili's mind - the connection between people and the temperature of communication between people seem to have disappeared, machines are becoming more and more like humans, but people are becoming more and more like machines.

If a person lacks the will to work hard, the patience to learn independently, and even the ability to judge right and wrong, as well as the responsibility to settle down, then such a scarce life will gradually lose its meaning. "Zhang Lili said that artificial intelligence: progress, challenges, and the future. 1 Introduction: Human society is transitioning from the information society to the intelligent society, and computing has become a key factor in promoting social development.

In the new era of digital civilization where everything is interconnected, traditional data-based computing is far from meeting humanity's pursuit of a higher level of intelligence. In recent years, with the rapid development of computing and information technology, and the unprecedented popularity and success of deep learning, artificial intelligence (AI) has been established as the forefront of human exploration of machine intelligence, resulting in a series of breakthrough research results, This includes the convolutional neural network proposed by Yann LeCun and the achievements of Yoshua Bengio in the field of deep learning causal reasoning.

In March 2016, DeepMind launched the AI Go program AlphaGo against the world's top Go master Li Shishi, which attracted unprecedented attention worldwide. This epoch-making human-computer war ended with AI's landslide victory and became a catalyst to push the AI wave to a new height.

Another important driver of AI is the emergence of large pre training models. These models have begun to be widely used in natural language and image processing to handle a variety of applications with the help of transfer learning. Among them, the most representative is the natural language processing model GPT-3. It has been proved that large models with high structural complexity and a large number of parameters can improve the performance of deep learning.

Computing power is one of the important factors supporting intelligent computing. Faced with the vast data sources, heterogeneous hardware configurations, and constantly changing computing needs in the information society, intelligent computing mainly meets the computing power requirements of intelligent tasks through vertical and horizontal architectures. The characteristic of vertical architectures is homogeneous computing infrastructure, which mainly improves resource utilization efficiency through the application of intelligent methods to enhance computing power.

In contrast, horizontal architecture coordinates and schedules heterogeneous and wide area computing resources to maximize the effectiveness of collaborative computing. For example, in April 2020, in response to the computing needs of the global COVID-19 research, Folding@home Within three weeks, we collaborated with 400000 computing volunteers to achieve a computing capacity of 2.5Exaflops, surpassing any supercomputer in the world.

Although we have achieved great success in intelligence and computing, we still face some major challenges in these two fields: challenges in intelligence. AI using deep learning currently faces significant challenges in interpretability, universality, evolutionability, and autonomy. Compared to human intelligence, most AI technologies currently have weak effects and can only play a good role in specific fields or tasks.

Upgrading from data-based intelligence to more diverse intelligence, including perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-machine integration intelligence, also faces significant theoretical and technical challenges in computing. The digital wave has brought unprecedented growth in applications, connections, terminals, users, and data volume, all of which require enormous computing power.

满足如此快速增长的计算能力需求变得越来越具有挑战性智能社会中的巨型任务依赖于各种特定计算资源的高效组合此外，传统的硬件模式不能很好地适应智能算法，制约了软件的发展迄今为止，智能计算还没有一个被普遍接受的定义。

考虑到世界的三个基本空间，即人类社会空间、物理空间和信息空间日益紧密融合，我们从解决复杂的科学和社会问题的角度提出了智能计算的新定义：智能计算是支撑万物互联的数字文明时代新的计算理论方法、架构体系和技术能力的总称。

智能计算根据具体的实际需求，以最小的代价完成计算任务，匹配足够的计算能力，调用最好的算法，获得最优的结果智能计算的新定义是为响应人类社会、物理世界和信息空间三元融合快速增长的计算需求而提出的智能计算以人为本，追求高计算能力、高能效、智能和安全。

其目标是提供通用、高效、安全、自主、可靠、透明的计算服务，以支持大规模、复杂的计算任务图1为智能计算的整体理论框架，它体现了支持人类社会—物理世界—信息空间集成的多种计算范式

图1：基于人类社会空间、物理空间和信息空间融合的智能计算总览2 智能计算基础智能计算是数字文明时代支撑万物互联的新型计算理论方法、架构体系和技术能力的总称利用智能计算可以实现许多经典和前沿研究领域的创新，以解决复杂的科学和社会问题。

智能计算的基本要素包括人的智能、机器的能力以及由万物组成的物理世界在理论框架中，人是智能计算的核心和智慧的源泉，代表着原始的、与生俱来的智能，称为元智能元智能包括理解、表达、抽象、推理、创造和反思等人类高级能力，其中包含人类积累的知识。

元智能以碳基生命为载体，是由个体和生物群体经过百万年的进化产生的，它包括生物具身智能、脑智能（尤其是人脑）和群体智能所有的智能系统都是由人类设计和建造的

因此，在智能计算的理论体系中，人类的智慧是智能的源泉，计算机是人类智能的赋能我们称计算机的智能为通用智能通用智能代表计算机解决具有广泛外延的复杂问题的能力，以硅基设施为载体，由个体和群体计算设备产生生物智能可以在以下四个层次上移植到计算机上：数据智能、感知智能、认知智能和自主智能。

元智能和通用智能如图2所示

图2：元智能和通用智能智能计算面临大场景、大数据、大问题、泛在需求的挑战算法模型变得越来越复杂，需要超级计算能力来支持越来越大的模型训练目前，计算资源已经成为提高计算机智能研究水平的障碍随着智能算法的发展，拥有丰富计算资源的机构可能形成系统的技术垄断。

经典的超级计算机已经难以满足AI对计算能力的需求虽然通过算法优化可以在一定程度上降低算力需求，但并不能从根本上解决这个问题。需要从架构、加速模块、集成模式、软件栈等多个维度进行全面优化，如图3所示。

图3：智能计算的计算能力智能计算在理论技术上具有以下特点（图4）：理论技术上的自学习和可进化性，架构上的高计算能力和高能效，系统方法上的安全性和可靠性，运行机制上的自动化和精确性，以及服务性上的协作和泛在性。

智能计算包括两个本质方面：智能和计算，两者相辅相成智能促进了计算技术的发展，计算是智能的基础提高计算系统性能和效率的高级智能技术范式是「智能驱动的计算」支持计算机智能发展的高效、强大的计算技术范式是「面向智能的计算」。

两种基本范式从五个方面进行创新，提升计算能力、能源效率、数据使用、知识表达和算法能力，实现泛在、透明、可靠、实时、自动化的服务。

图4：智能计算的特征3 智能驱动的计算提高计算的普适性对智能计算至关重要现实场景中的问题，例如模拟、图（gragh）（图5）等，需要进行各种计算智能计算的另一个关键点是如何提高计算的智能化水平从经验上来说，我们常常需要向自然界的智能生物学习，计算也不例外，例如三大经典智能方法：人工神经网络（图6）、模糊系统和进化计算，都是受生物智能启发提出的算法。

智能计算理论包括但不限于以上几种计算，以实现高度的泛在化和智能化

图5：图计算的技术架构

图6：典型神经元的结构和人工神经元的结构智能系统在开始工作之前，首先要进行智能感知因此，感知智能在所有智能系统中都起着至关重要的作用感知智能的重点是多模态感知、数据融合、智能信号提取和处理典型的例子包括智慧城市管理、自动潜水系统、智能防御系统和自主机器人。

感知智能研究中最热门的领域是模拟人类的五种感觉能力，视觉、听觉、嗅觉、味觉和触觉此外，智能传感还包括温度、压力、湿度、高度、速度、重力等，需要大量的计算或数据训练来提高其性能近年来，随着模式识别和深度学习技术的全面应用，机器的感知智能已经超过人类，在语音、视觉和触觉识别方面取得了重大进展。

由于其日益增长的重要性和日益拓宽的应用领域，智能传感器受到了广泛关注如图7所示，智能传感器具有各种形式以满足不同应用的需求，并且更新更好的型号正在被不断的开发出来

图7：工业中需要连接到物联网的的各种传感器类型认知智能是指机器具有像人一样的逻辑理解和认知能力，特别是思考、理解、总结和主动应用知识的能力它描述了智能体在真实环境中处理复杂事实和情况的能力数据识别是感知智能的核心功能，需要对图像、视频、声音等各类数据进行大规模的数据采集和特征提取，完成结构化处理。

相比之下，认知智能需要理解数据元素之间的关系，分析结构化数据中的逻辑，并根据提炼出的知识做出响应认知智能计算主要研究机器的自然语言处理、因果推理和知识推理（图8）等领域通过对人脑的神经生物学过程和认知机制的启发式研究，可以提高机器的认知水平，以使其获得帮助、理解、决策、洞察和发现的能力。

图8：知识推理概述机器从被动输出到主动创造有两个关键要素：强泛化模型和与外部环境的持续交互自主智能的发展路径从学习单一任务开始，举一反三，逐步达到与环境动态交互的主动学习，最终实现自我进化的高级智能当前可以通过迁移学习、元学习和自主学习等技术寻找生成自主智能的可行路径。

尽管在智能的四个层面上（数据智能，感知智能，认知智能，自主智能）取得了重大进展，但目前仅通过计算/统计模型还难以从极其复杂的场景中实现完全的智能在这些场景中，人类应该继续在解决问题和决策中发挥不可或缺的作用，来探索人类认知过程中涉及的要素，并将其与机器智能相结合。

下一步，将聚焦于人机交互、人机融合和脑机接口等技术4 面向智能的计算AI的发现不断涌现，这在很大程度上归功于不断增长的计算能力AI的快速变化是由新思想或革命性理论推动的通常，最新的先进模型仅依赖于更大的神经网络和更强大的处理系统。

Open AI研究人员在2018年进行了一项研究，追踪基于计算能力的最大模型的增长情况利用AI研究史上训练的一些最著名的AI模型所需的计算量，他们发现了计算资源快速增长的两个趋势开发突破性模型所需的计算能力的增长速度与摩尔定律大致相同，即在2012年之前，单个微芯片的计算能力往往每两年翻一番。

但图像识别系统AlexNet在2012年发布时引起了人们的新兴趣AlexNet的引入刺激了顶级模型的计算需求急剧增加，从2012年到2018年，这种需求每3到4个月翻一番，如图9所示

图9：过去十年计算能力需求的增长大大超过宏观趋势当摩尔定律失效时，超大算力主要依赖于海量计算、内存和存储资源的并行叠加例如，「高性能计算」是指将大量计算机快速联网成一个「集群」以进行密集计算的做法，使用户能够比传统计算机更快地处理大量数据，从而获得更深入的洞察力和竞争优势。

此外，得益于云计算（图10），用户现在可以选择增加其高性能计算程序的容量，从而继续提高算力。

图10：云、雾和边缘计算的表示推进智能计算架构创新的目标包括更高效的能源管理、更低的功耗、更便宜的总芯片成本以及更快速的错误检测和纠正当涉及某些无法在CPU上执行的AI操作时，AI加速器可能会大大减少训练和执行时间。

在短期内，所使用加速器的架构专业化将是保持计算能力增长的最佳方式，如图11所示为已公开发布的AI加速器和处理器的峰值性能与功耗另外，内存计算（图12）是一个非常有效的方案，它能够使内存单元执行原始逻辑操作，因此它们可以在不需要与处理器交互的情况下进行计算，这是内存和处理器之间不断扩大速度差距的主要原因。

图11：公开发布的 AI 加速器和处理器的峰值性能与功耗散点图

图12：计算的三种概念方法：（a）传统数字计算，（b）近内存阵列计算（NMAC）和（c）内存阵列计算（IMAC）复杂性是传统计算机进一步突破的瓶颈当今高度复杂的AI模型（例如深度神经网络）在边缘设备中仍然难以实现普遍使用。

这是由于运行这些模型的高级GPU和加速器存在功率和带宽紧缩的缺陷，导致处理时间长并且架构设计繁琐由于这些问题，研究人员开始创造新的计算模式，主要包括：量子计算（图13），因为其具有纠缠或其他非经典相关性带来的量子优势，可以在许多复杂的计算问题中实现指数速度；

神经形态计算（图14）的构造和操作受到大脑中神经元和突触的启发，因其能源效率高而非常适合计算，神经形态计算是事件驱动和高度并行化的，这意味着只有小部分系统同时工作，所以消耗的功率非常小；光子计算（图15）与电神经网络相比具有许多优势，包括超高带宽、快速计算速度和高并行性，所有这些都是通过使用光子硬件加速来计算复杂的矩阵向量乘法来实现的；

生物计算（图16）是利用生物系统固有的信息处理机制发展起来的一种新的计算模型，主要包括蛋白质计算机、RNA计算机和DNA计算机，具有并行和分布式计算能力强、功耗低的优势。

图13：显示复杂性等级之间关系的图表（a）以及用于识别和评估可能的量子优势的流程图（b）

图14：传统计算系统和类脑计算系统的结构

图15：深度神经网络，包括传统网络和电子光子网络

图16：生物计算可能提供优于传统计算机的性能5 智能计算的应用如果要跟上当前科学的快速发展，就必须不断的进行革新现在正在进行的计算机革命的融合将以前所未有的方式极大地推动科学发现的进步几十年来，计算材料（图17）已成为研究材料特性和设计新材料的有力手段。

然而，由于材料和材料行为的复杂性，它们的应用面临许多挑战，包括缺乏许多原子、离子以及原子和离子相互作用的力场和电位，分子动力学模拟中的不同热力学相，以及优化材料成分和工艺参数的巨大搜索空间作为一种新的研究范式，AI集成到计算材料中是对传统计算材料的革命，并且已经在多长度、多时间尺度、多物理场耦合计算方面取得了巨大成功。

图17：材料/分子科学范式的比较作为最古老的观测科学之一，天文学在历史上收集了大量数据由于望远镜技术的突破，收集到的数据爆炸性增长天文学和天体物理学领域的特点是拥有丰富的数据和各种大口径的地面望远镜，例如即将推出的大型巡天望远镜和天基望远镜。

使用高分辨率相机和相关工具，数据收集现在更加高效，并且在很大程度上实现了自动化，必须进行更高效的数据分析因此，需要智能计算技术来解释和评估数据集药物设计同样受益于AI（图18），AI可以帮助科学家建立蛋白质的3D结构、模拟药物和蛋白质之间的化学反应以及预测药物的功效。

在药理学中，AI可以用于创建靶向化合物和多靶点药物利用AI还可以设计合成路线、预测反应产率并了解化学合成背后的机制AI让重新利用现有药物来治疗新的治疗目标变得更加容易此外，AI对于识别不良反应、测定生物活性和获得药物筛选结果至关重要。

图18：不同的基于深度学习的药物-靶点相互作用预测算法对应不同的输入特征（a）基于配体的方法，（b）基于结构的方法，和（c）基于关系的方法随着大数据和AI技术使用的增长，作物育种开始进行融合与突破（图19）。

AI技术可以支持服务的创建、模型的识别以及农业食品应用和供应链阶段的决策过程AI在农业中的主要目标是准确预测结果并提高产量，同时最大限度地减少资源使用因此，AI工具提供的算法可以评估产量，预测难以预见的问题或事件以及发生趋势。

从种植到收获再到销售，AI促进了整个农业价值链

图19：大数据与AI在植物育种中的结合智能计算加速转型变革，导致经济和社会秩序的转变由于技术进步，商品和劳动力市场正在发生巨大变化，数字社会正在逐渐形成（图20）AI应该成为数字经济中每一个数据驱动战略的核心，包括工业4.0。

例如，人工智能可以应用于预测性维护预测性维护包括涉及通用设备或生产机械的维护，并使用来自生产线或运营线的传感器数据帮助降低运营费用或停机时间另外AI可以应用于城市治理，通过开发新的策略和方法，使城市更智能。

智慧城市治理旨在利用最先进的信息技术同步数据、程序、权限等，造福城市居民，主要包含四个方面：智慧决策、智慧城市治理、智慧行政和智慧城市合作。

图20：数字社会的组成部分6 展望从新兴产业生态的角度来看，智能计算产业仍面临着一系列挑战，决定着其未来发展第一，与传统计算理论相比，智能计算是语言和生物学驱动的计算范式的应用和发展这意味着机器可以根据不同的场景模仿人脑解决问题和决策的能力。

然而，硅基和碳基运算的底层逻辑存在根本差异，大脑智能的机制仍有待进一步探索智能计算的下一步是通过深入探索类人智能的基本要素，其在宏观层面的相互作用机制以及在微观层面上支持不确定性生成的计算理论，进行彻底的改革。

第二，探索人类如何学习并将其应用到AI的研究中具有重要意义知识驱动的机器智能可以从人类活动中学习，模仿人脑的决策能力，使机器能够像人一样感知、识别、思考、学习和协作需要探索多知识驱动的知识推理和持续学习的理论和关键技术，使智能系统具有类人的学习、感知、表示和决策能力，促进智能计算从数据驱动向知识驱动演进。

第三，软硬件适配面临着巨大的挑战，如精度损失、调用困难、协作效率低下等 未来，计算机必须突破冯·诺依曼体系结构下固定的输入和处理范式，大力发展交叉学科的智能计算和仿生学在算法层面进行设计，突破现有架构的局限，以更低的计算和硬件设计成本尝试更灵活、更人性化的数据处理方式。

此外，开发高性能、低能耗的新型组件设计方案，提高软件和硬件的计算能力和效率，以满足快速增长的需求和智能计算应用也很重要第四，智能计算的理论技术架构是一个复杂的系统，具有多个与其他学科相互作用的子系统系统中的各种硬件需要更复杂的系统设计，更好的优化技术，以及系统调优的更大成本。

高维计算理论复杂性的缺乏是大规模计算系统面临的主要挑战7 结论当前，我们正迎来人类发展的第四次浪潮，正处于从信息社会向人类社会-物理世界-信息空间融合的智能社会的关键转型期在这种转变中，计算技术正在经历变革，甚至是颠覆性的变化。

智能计算被认为是未来计算的发展方向，不仅是面向智能的计算，而且是智能赋能的计算它将提供通用、高效、安全、自主、可靠和透明的计算服务，以支持当今智能社会中大规模和复杂的计算任务本文全面回顾了智能计算的理论基础、智能与计算的技术融合、重要应用、挑战和未来方向。

我们希望这篇综述能为研究人员和从业者提供一个很好的参考，并促进未来智能计算领域的理论和技术创新参考文献链接https://mp.weixin.qq.com/s/u5E9YldiaE8_SPMjzws5mg。

https://spj.science.org/doi/10.34133/icomputing.0006https://mp.weixin.qq.com/s/d7PpTEHjfH33fVRTsOaucg