Abstract
The study of artificial intelligence (AI) has been a continuous endeavor of scientists and engineers for over 65 years. The simple contention is that human-created machines can do more than just labor-intensive work; they can develop human-like intelligence. Being aware or not, AI has penetrated into our daily lives, playing novel roles in industry, healthcare, transportation, education, and many more areas that are close to the general public. AI is believed to be one of the major drives to change socio-economical lives. In another aspect, AI contributes to the advancement of state-of-the-art technologies in many fields of study, as helpful tools for groundbreaking research. However, the prosperity of AI as we witness today was not established smoothly. During the past decades, AI has struggled through historical stages with several winters. Therefore, at this juncture, to enlighten future development, it is time to discuss the past, present, and have an outlook on AI. In this article, we will discuss from a historical perspective how challenges were faced on the path of revolution of both the AI tools and the AI systems. Especially, in addition to the technical development of AI in the short to mid-term, thoughts and insights are also presented regarding the symbiotic relationship of AI and humans in the long run.
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1 Introduction
Human-created machines are already able to do all-encompassing types of labor-intensive work. Yet, on many occasions, driven by demands for higher productivity and perhaps simply curiosity, humans have been trying to infuse human intelligence into machines, which constitutes the original motivation of Artificial Intelligence (AI). AI research has been going on for over 65 years and has made impressive achievements in terms of theoretical study and real-world applications [1, 2]. AI is being used almost everywhere and is considered a core skill for the future. The AI market is projected to grow to $190 billion by 2025, at a CAGR (compound annual growth rate) of over 36% between 2018 and 2025 [3].
There are many definitions of artificial intelligence. In the Turing test, AI is defined as the ability of machines to communicate with humans (using electronic output devices) without revealing the identity that they are not humans, where the essential judgment criterion is binary. Marvin Minky, one of the pioneers of AI, defined AI as enabling machines to do things that require human intelligence. The symbolic school believes that AI is the operation of symbols, and the most primitive symbols correspond to the physical entities. Although the descriptions of AI are various, the core of AI is widely believed to be the research theories, methods, technologies, and applications for simulating, extending, and expanding human intelligence. Nowadays, the concept of AI has an increasingly profound impact on human life. As the roles of steam engines in the Age of Steam, generators in the Age of Electricity, and computers in the Age of Information, AI is the pillar of technology in the contemporary era and beyond.
“AI” has become a buzzword in almost every aspect of our lives. The semantic network graph (Fig. 1) is built based on the search results in Web of Science (2021-08-26) and plotted using the VOS Viewer software. It shows the impact degree and the connections of the keywords that are most related to AI. According to the color of the links, it can be ascertained that the “application” of AI has received great attention in the literature. The concept is closely related to “system” sciences while “neural network,” “classification,” and “prediction” are the main focuses in terms of algorithms. The research fields of AI include systems and engineering, brain science, psychology, cognitive science, mathematics, computer science, and many other fields. The application fields of AI are extensive, covering (but not limited to) speech recognition [4, 5], image processing [6, 7], natural language processing [64].
To deal with highly complex problems and discover insights from extensive data collections, deep structures are necessary [65,3.5 Driving forces of AI development An increase in the bulk of subjects facilitates the AI R&D ecosystem and the related ecology. In the short term, the development of AI is to a large extent driven by dominant stakeholders, especially with support from major investments and governmental strategic planning [3, 68]. The key lies in whether the novel technologies can be grounded swiftly, contributing to economic harvest and regional/social stability. On the other hand, learning from the historical experience of “AI winters”, it is still uncertain and indirect that whether, in the mid to long term, AI development will converge to the basic demands of the general public, for improving the quality of life, the efficiency of working, and the degree of happiness. The healthcare industry covers all the attributes from patients to doctors, from platform to end users, from groups to individuals [105]. The industrial chain is constituted of medicinal materials, medicine, medical instruments, medical institutions, medical information, and medical examination. Today, healthcare applications are acting as an active driving force of AI development. Colossal amounts of medical data are collected every day. The complexity of medical information lies in the high dimensionality, nonlinearity, and sometimes strong dynamics. In this context, the AI-powered diagnosis systems need to be well-trained to achieve an acceptable performance, which usually consists of a list of indicators such as those characterizing the prediction accuracy and the confusion matrix that characterizes the classification performance. AI plays a key role in establishing novel norms of modern hospitals towards one-stop medical services and new business models. In the future, the allocation of limited medical resources will still be the major issue. In the cases where the physicians require reliable intelligent preprocessing tools to reduce their workload in dealing with raw data, AI may act as a good assistant. For instance, AI helps to markup suspicious lesion areas on optical images [106], X-ray images [107], or magnetic resonance images [108,109,110], and can analyze patients’ speech to diagnose Alzheimer’s disease [111]. Researchers also put hope on shortening the cycle of new drug discovery with the ability of AI in learning from vast volumes of medical literature and datasets [112,113,114,115]. Furthermore, embedding AI systems in portable devices will help reduce the shortage of medical resources in remote areas and reduce social inequality. Motivated by these practical demands, there are huge driving forces in the AI-related R&D activities, the production, and the applications of AI-embedded devices, as well as the construction of intelligent platforms and infrastructure. Currently, AI has improved the medical automation, but is mainly used to aid diagnosis. Using AI alone as the backbone of healthcare is very dangerous. In 2015, IBM launched Watson Health, a business unit that uses AI to solve critical problems in healthcare. Their algorithms prescribe drugs with severe side effects and even death to a cancer patient. Another driving force behind the advancement of AI is related to intelligent transportation applications. Research on autonomous vehicles has been an extremely popular direction. The tasks cover all aspects from perception to decision making, which are all considered to be in need of intelligence [116]. Delightfully, what were considered as challenging problems have now applicable approaches, such as localization, object recognition, and tracking, movement prediction, and path planning [117]. Although these are ANI tasks, the high demands in dealing with the complexity and uncertainty in the real-world environment and the requirements of real-time and onboard implementation are boosting the AI industry. From the viewpoint of commercialization, it is hard to tell whether the traditional automotive companies or the Internet enterprises dedicated to intelligent solutions will be dominating the innovation and the market of smart automobiles. From another perspective, vehicular cyber-physical systems are being studied to connect the information islands of each individual transportation participants [118]. The models of the real-time regional traffic network can be created with the aid of the cloud computing resources and based on the publicly available information collected from distributed agents (such as vehicle sensors, pedestrians’ smart phones, and roadside infrastructure). On this basis, intelligent algorithms are required to play the role from a macroscopic perspective, for instance, in the prediction and optimal diversion of the traffic. Today, policies are put forward to provide support for promoting intelligent transportation applications and to provide regulations for testing activities and open test roads. In addition to the above, wide categories of simulation applications are relying on and in turn driving the development of AI. The typical application scenarios range from manufacturing to education, from smart city to entertainment, and many more beyond [119,120,121]. The enabling technologies include but are not limited to Internet of things, extended reality (XR), digital twin, robots, smart grids, space technologies, and so forth. In comparison with the AI winters, the historical period of the twenty-first century is full of opportunities, gras** which will lead us to a prosperous future where AI can create values and benefit human lives to the greatest extent.
4 Future
4.1 Understanding and cognition
While AI systems are created by humans, they may evolve in a much faster speed than anticipated and develop high complexity in structure, behavior, and decisions that humans can hardly follow. In the long term, AI systems and human beings need to establish common cognitions in terms of the outlook on the world, life, and values. However, to date, engineered systems cannot be modelled to precisely replicate the real-world entities and the physical environment. That is why a bridge to connect the cyber and the physical worlds is being built in the Industry 4.0 era, ho** to feed the engineered digital replicas with online real-time measurements (for synchronization, correction, and update) and further why Kalman filter is regarded as a cornerstone technique in the domains of sensing/perception/control due to its ability to achieve optimal estimation by compromising measurements (with inevitable error) and model outputs [122, 123].
As discussed in Sect. 3.4, AI will play important roles in symbiotic systems in the future. In the narrow sense, human–machine symbiotes would bring benefits in terms of overcoming disabilities or making humans stronger (also referred to as “human augmentation”) and getting more knowledgeable. Especially, in an age of information explosion, it has become increasingly difficult to search through the available resources. Yet, a type of cognitive digital twin (CDT) may be very helpful to collect and pre-screen the related data, and then to perform tasks on behalf of human. An illustrative example of “Google Duplex” was given in Table 1 where AI made phone calls to book a restaurant. With the grounded applications of natural language processing and computer vision, CDT may execute users’ much complex demands. Customized optimization is feasible by integrating the knowledge structure of an individual, and it is actually an ongoing research which is usually referred to as personal CDT. In turn, the knowledge structure and the practical skills of humans could be improved in a targeted manner by using courses suggested by AI (just like what was envisioned in the movie The Matrix). Similarly, in the future, scientists could manage the explosion of knowledge better with the aid of machine reading to discover new knowledge that they need most from hundreds of thousands of publications each year.
We see that there are plenty of AI algorithms inspired by the nature, such as the grey wolf algorithm, ant colony optimization, particle swarm, genetic algorithm, and so forth. Neural networks are inspired by biological nerve systems. Nevertheless, the models have been much simplified, and as a result, the mechanisms of learning and acting become different and divergent that the AI systems can hardly understand the nature and the socio-economic worlds like we do. In the future, with super large-scale neural networks such as Wu Dao 2.0 and GPT-3 becoming common, it may be expected that multimodality across the major branches of AI such as computer vision and natural language processing will end up unified [124].
On the other hand, can humans understand the behavior of AI systems and trust their decisions? As aforementioned, XAI (or interpretable AI) is the key towards extensive and generic application of AI systems for reliability-critical tasks. Theoretical guarantee is needed in terms of why it works and what the target is [125,126,127,128].
The research on XAI can take two routes. First, AI systems are designed as equivalent or approximate solutions to the conventional non-AI solutions with clear physical meanings. In this way, it is clear what the design target is and what exactly is to be learned, and the heavy dependency on trial-and-error during the design phase can be reduced by learning from data. For instance, the “kernel trick” is usually used to simplify the computation of the inner product of nonlinear functions which map low-dimensional inseparable variables to a high-dimensional separable feature space. The problem is that there has been no consensus reached on how to optimally select the type and how to set the parameters of the kernel function, and the online computational load is quite high. As a result, although the performance is satisfactory and sometimes superior, kernel-based methods are facing great challenges in practical real-time applications. Promisingly, Deep Neural Network (DNN) training enables automating the process of manual tuning of critical parameters in these conventional solutions. The theoretical support is provided by Mercer’s Theorem, which proves that any semi-positive function is a valid candidate. Therefore, by using DNN, it can achieve a learnable and faster realization of the kernel function where the explicit form and the coefficients of the nonlinear map** function can be obtained [ Profound technological changes have taken place around us during the last 2 decades, supported by disruptive advances both on the software and the hardware sides. The dominant feature of the changes is the integration of the virtual world with the physical world through the Internet of Things (IoT). The most recent development is the radical paradigm shift from “connected things” to “connected intelligence”. Table 1 tries to emphasize the accelerating nature of technological developments by listing the important milestones in AI during the last 25 years. Throughout the history of scientific and technological development, the emergence of any scientific and technological revolution is not only reflected in technology, but also changes in human social structure, moral constraints, laws, and education. In this article, at the critical point of AI technological revolution, a brief history of AI development is presented, the advancement and the state-of-the-art of the AI discipline are reviewed, and a perspective on the future of AI is offered. AI integrates with a variety of disciplines, plays an important role in the history and might impact the future of humanity. Today, AI is more available now than ever. Products and services driven by AI technologies have widely emerged around us and have led to a profound impact on daily life. Predictably, AI will act as the one of the backbones of new technological revolutions. Nevertheless, several troughs may once again take place if the technologies/applications cannot be grounded swiftly according to the expectations from the dominant stakeholders (inventors rather than the general public). The article is intended to help the readers to better understand AI, accept AI, and think more deeply about AI. With the long-lasting curiosity and wisdom, we are likely to witness the beauty of a world with the novel AI ecology. As Ophelia stated in Shakespeare’s play Hamlet, “we know what we are, but we know not what we may become.”5 Concluding remarks
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Conceptualization, OK and YJ; resources: OK, SY, HL; investigation: YJ, XL, OK; writing—original draft preparation: YJ and XL; writing—review and editing: OK, YJ, XL, HL, SY; supervision: SY, HL, OK. All authors read and approved the final manuscript.
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Jiang, Y., Li, X., Luo, H. et al. Quo vadis artificial intelligence?. Discov Artif Intell 2, 4 (2022). https://doi.org/10.1007/s44163-022-00022-8
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DOI: https://doi.org/10.1007/s44163-022-00022-8