Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.
The year 2020 was profoundly challenging for citizens, companies, and governments around the world. As covid-19 spread, requiring far-reaching health and safety restrictions, artificial intelligence (AI) applications played a crucial role in saving lives and fostering economic resilience. Research and development (R&D) to enhance core AI capabilities, from autonomous driving and natural language processing to quantum computing, continued unabated.
Baidu was at the forefront of many important AI breakthroughs in 2020. This article outlines five significant advances with implications for combating covid-19 as well as transforming the future of our economies and society.
Artificial intelligence (AI) is without question the primary enabling technology for the next generation of system automation. Aside from freeing up valuable resources, improving performance and boosting innovation, AI also opens up a wealth of opportunities for communication service providers (CSPs) to grow their businesses beyond simply providing connectivity.
One of the biggest challenges to the wide-scale adoption of AI, however, is the need to fully address the trust issues related to the technology, particularly with regard to cultural and educational differences. At Ericsson, we believe that the best way to build trust is through knowledge sharing and dialogue with stakeholders about the specific AI techniques that we are exploring, testing and using in our products and solutions. The critical infrastructure that we provide for digital enterprises makes it imperative that our customers are confident that we are deploying AI in a trustworthy way in networks, using techniques that are robust, explainable, traceable and unbiased.
The purpose of this special issue of Ericsson Technology Review is to offer our stakeholders greater insight into our view of the role of AI in future network development. In it, we present our latest findings about the potential for trustworthy AI techniques to help us make breakthroughs in six key areas: business support systems (BSS), operation support systems (OSS), privacy, data ingestion, RAN and overall network performance.
BSS and OSS both have pivotal roles to play in the future development of our industry, particularly when it comes to 5G and the Internet of Things (IoT). Many 5G and IoT use cases require BSS that can handle complex business situations and optimize outcomes with minimal manual intervention. AI-native BSS enables the various applications within the BSS to share business information with each other in an efficient and secure manner. Similarly, our AI-powered OSS concept, which enables the use of industry-defined interfaces and open-source modules, offers big advantages over traditional OSS – especially when it comes to supporting emerging 5G and IoT use cases.
Advanced AI techniques are also proving to be enormously helpful in addressing privacy issues and reducing network footprint. Our research indicates that migration from a conventional machine learning model to a federated learning model – a more advanced AI technique – dramatically reduces the amount of information that is exchanged between different parts of the network with no negative impact on QoE.
AI techniques also open up new possibilities to achieve the level of RAN automation required to solve the complex resource management challenges presented by 5G and future radio systems. For example, our RAN experts have already integrated the key software enablers for AI-based RAN automation into a comprehensive framework that provides a solid and flexible technological foundation for the development of AI-based RAN. To help CSPs ensure superior performance across their operations, we have also used AI techniques to develop a harmonized data ingestion architecture that ensures secure and efficient access to data.