If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The field of transportation and logistics has witnessed fundamental transformations in the last decade, due to the convergence of seemingly unrelated technologies. The fast pace of innovations has been particularly striking for an industry that had been relatively stagnant for a long time. Taxi services were born in England where a public coach service for hire was first documented in 1605. The Hackney Carriage Act, which legalized horse-drawn carriages for hire, was passed in Parliament in 1635, and a similar service was started in Paris in 1637. Public transit was invented by Blaise Pascal in 1662 through a service known as the "carriage," which was quite popular and operated for 15 years.
Artificial intelligence (AI) has evolved from hype to reality over the past few years. Algorithmic advances in machine learning and deep learning, significant increases in computing power and storage, and huge amounts of data generated by digital transformation efforts make AI a game-changer across all industries.8 AI has the potential to radically improve business processes with, for instance, real-time quality prediction in manufacturing, and to enable new business models, such as connected car services and self-optimizing machines. Traditional industries, such as manufacturing, machine building, and automotive, are facing a fundamental change: from the production of physical goods to the delivery of AI-enhanced processes and services as part of Industry 4.0.25 This paper focuses on AI for industrial enterprises with a special emphasis on machine learning and data mining. Despite the great potential of AI and the large investments in AI technologies undertaken by industrial enterprises, AI has not yet delivered on the promises in industry practice. The core business of industrial enterprises is not yet AI-enhanced. AI solutions instead constitute islands for isolated cases--such as the optimization of selected machines in the factory--with varying success. According to current industry surveys, data issues constitute the main reasons for the insufficient adoption of AI in industrial enterprises.27,35 In general, it is nothing new that data preparation and data quality are key for AI and data analytics, as there is no AI without data. This has been an issue since the early days of business intelligence (BI) and data warehousing.3 However, the manifold data challenges of AI in industrial enterprises go far beyond detecting and repairing dirty data. This article profoundly investigates these challenges and rests on our practical real-world experiences with the AI enablement of a large industrial enterprise--a globally active manufacturer.
Street maps help to inform a wide range of decisions. Drivers, cyclists, and pedestrians use them for search and navigation. Rescue workers responding to disasters such as hurricanes, tsunamis, and earthquakes rely on street maps to understand where people are and to locate individual buildings.23 Transportation researchers consult street maps to conduct transportation studies, such as analyzing pedestrian accessibility to public transport.25 Indeed, with the need for accurate street maps growing in importance, companies are spending hundreds of millions of dollars to map roads globally.a However, street maps are incomplete or lag behind new construction in many parts of the world. In rural Indonesia, for example, entire groups of villages are missing from OpenStreet-Map, a popular open map dataset.3 In many of these villages, the closest mapped road is miles away. In Qatar, construction of new infrastructure has boomed in preparation for the FIFA World Cup 2022.
Although great progress has been made in automatic speech recognition (ASR), significant performance degradation still exists in very noisy environments. Over the past few years, Chinese startup AISpeech has been developing very deep convolutional neural networks (VDCNN),21 a new architecture the company recently began applying to ASR use cases. Different than traditional deep CNN models for computer vision, VDCNN features novel filter designs, pooling operations, input feature map selection, and padding strategies, all of which lead to more accurate and robust ASR performance. Moreover, VDCNN is further extended with adaptation, which can significantly alleviate the mismatch between training and testing. Factor-aware training and cluster-adaptive training are explored to fully utilize the environmental variety and quickly adapt model parameters.
Mobile crowdsensing (MCS) presents a new sensing paradigm based on the power of user-companioned devices.11,12 It allows "the increasing number of smartphone users to share local knowledge acquired by their sensor-enhanced devices, and the information can be further aggregated in the cloud for large-scale sensing."4 The mobility of large-scale mobile users makes MCS a versatile platform that can often replace static sensing infrastructures. A broad range of applications are thus enabled, including traffic planning, environment monitoring, urban management, and so on. During the past decade, MCS has become a surging research topic in China.
According to the China Disabled Persons' Federation (CDPF), there are now 17 million visually impaired people in China, among which three million are totally blind, while the others are low-visioned. In the past two decades, China has experienced tremendous development of information technology. Traditional industries are incorporating information technology, with services delivered to users through websites and mobile applications. It is positive technical progress that visually impaired people can access various services without leaving home; for example, they can order food delivery online or schedule a taxi from an app-based transportation service. However, the development of technology has also brought challenges to the visually impaired in China.
Neurological diseases, such as cerebrovascular disease, Parkinson's disease (PD), Alzheimer's disease, have become the leading cause of death in China. Neurological function evaluation is crucial for the diagnosis and intervention of neurological diseases. Clinically, neurological function is evaluated by various scales, tests, and questionnaires. However, these methods rely on costly professional equipment and medical personnel. They cannot be used as a means of daily evaluation of neurological diseases.
In the past decades, one line has run through the entire research spectrum of natural language processing (NLP)--knowledge. With various kinds of knowledge, such as linguistic knowledge, world knowledge, and commonsense knowledge, machines can understand complex semantics at different levels. In this article, we introduce a framework named "knowledgeable machine learning" to revisit existing efforts to incorporate knowledge in NLP, especially the recent breakthroughs in the Chinese NLP community. Since knowledge is closely related to human languages, the ability to capture and utilize knowledge is crucial to make machines understand languages. As shown in the accompanying figure, the symbolic knowledge formalized by human beings was widely used by NLP researchers before 1990, such as applying grammar rules for linguistic theories3 and building knowledge bases for expert systems.1
Chinese AI businesses have been growing rapidly since 2010. They have attracted significant investment from Internet giants and a vast number of emerging AI companies have emerged. Over the past decade, Chinese AI start-ups have gradually moved away from noisy bubbles and landed in an investment boom. In 2020, when people were fighting against the pandemic, CloudMinds, an AI start-up based in Beijing, developed a humanoid service robot named Cloud Ginger XR-1. Ginger played an important role in local hospitals, delivering food and medication to patients in a contactless manner when it was needed the most. Moreover, Ginger entertained patients, freeing up doctors and medical teams to focus on more critical health matters.
Artificial intelligence (AI) has the potential to enhance every technology as it resembles enabling technologies like the combustion engine or electricity. Many people in this field believe AI is general purpose, with a multitude of applications across many different disciplines. We believe the nature of AI is interdisciplinary. In other words, the power of AI lies in augmenting its ability to accelerate research exponentially and the possibilities are endless. As a result, demand for professionals who are hard-wired in AI technology knowledge but who also possess interdisciplinary perspectives and transferable skills is becoming increasingly important.