The new generation of artificial intelligence (AI) research initiated by Chinese scholars conforms to the needs of a new information environment changes, and strives to advance traditional artificial intelligence (AI 1.0) to a new stage of AI 2.0. As one of the important components of AI, collective intelligence (CI 1.0), i.e., swarm intelligence, is developing to the stage of CI 2.0 (crowd intelligence). Through in-depth analysis and informative argumentation, it is found that an incompatibility exists between CI 1.0 and CI 2.0. Therefore, CI 1.5 is introduced to build a bridge between the above two stages, which is based on bio-collaborative behavioral mimicry. CI 1.5 is the transition from CI 1.0 to CI 2.0, which contributes to the compatibility of the two stages. Then, a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is given. The meta-synthesis of wisdom, as an improvement of crowd intelligence, is an advanced stage of bionic intelligence, i.e., CI 3.0. It is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0, and some elaboration is made. As a result, we propose four development stages (CI 1.0, CI 1.5, CI 2.0, and CI 3.0), which form a complete framework for the development of CI. These different stages are progressively improved and have good compatibility. Due to the dominant role of cooperation in the development stages of CI, three types of cooperation in CI are discussed: indirect regulatory cooperation in lower organisms, direct communicative cooperation in higher organisms, and shared intention based collaboration in humans. Labor division is the main form of achieving cooperation and, for this reason, this paper investigates the relationship between the complexity of behavior and types of labor division. Finally, based on the overall understanding of the four development stages of CI, the future development direction and research issues of CI are explored.
The game of Tibetan Go faces the scarcity of expert knowledge and research literature. Therefore, we study the zero learning model of Tibetan Go under limited computing power resources and propose a novel scale-invariant U-Net style two-headed output lightweight network TibetanGoTinyNet. The lightweight convolutional neural networks and capsule structure are applied to the encoder and decoder of TibetanGoTinyNet to reduce computational burden and achieve better feature extraction results. Several autonomous self-attention mechanisms are integrated into TibetanGoTinyNet to capture the Tibetan Go board’s spatial and global information and select important channels. The training data are generated entirely from self-play games. TibetanGoTinyNet achieves 62%–78% winning rate against other four U-Net style models including Res-UNet, Res-UNet Attention, Ghost-UNet, and Ghost Capsule-UNet. It also achieves 75% winning rate in the ablation experiments on the attention mechanism with embedded positional information. The model saves about 33% of the training time with 45%–50% winning rate for different Monte–Carlo tree search (MCTS) simulation counts when migrated from 9 × 9 to 11 × 11 boards. Code for our model is available at
Ground elevation estimation is vital for numerous applications in autonomous vehicles and intelligent robotics including three-dimensional object detection, navigable space detection, point cloud matching for localization, and registration for mapping. However, most works regard the ground as a plane without height information, which causes inaccurate manipulation in these applications. In this work, we propose GeeNet, a novel end-to-end, lightweight method that completes the ground in nearly real time and simultaneously estimates the ground elevation in a grid-based representation. GeeNet leverages the mixing of two- and three-dimensional convolutions to preserve a lightweight architecture to regress ground elevation information for each cell of the grid. For the first time, GeeNet has fulfilled ground elevation estimation from semantic scene completion. We use the SemanticKITTI and SemanticPOSS datasets to validate the proposed GeeNet, demonstrating the qualitative and quantitative performances of GeeNet on ground elevation estimation and semantic scene completion of the point cloud. Moreover, the crossdataset generalization capability of GeeNet is experimentally proven. GeeNet achieves state-of-the-art performance in terms of point cloud completion and ground elevation estimation, with a runtime of 0.88 ms.
The integration of industrial Internet, cloud computing, and big data technology is changing the business and management mode of the industry chain. However, the industry chain is characterized by a wide range of fields, complex environment, and many factors, which creates a challenge for efficient integration and leveraging of industrial big data. Aiming at the integration of physical space and virtual space of the current industry chain, we propose an industry chain digital twin (DT) system framework for the industrial Internet. In addition, an industry chain information model based on a knowledge graph (KG) is proposed to integrate complex and heterogeneous industry chain data and extract industrial knowledge. First, the ontology of the industry chain is established, and an entity alignment method based on scientific and technological achievements is proposed. Second, the bidirectional encoder representations from Transformers (BERT) based multi-head selection model is proposed for joint entity–relation extraction of industry chain information. Third, a relation completion model based on a relational graph convolutional network (R-GCN) and a graph sample and aggregate network (GraphSAGE) is proposed which considers both semantic information and graph structure information of KG. Experimental results show that the performances of the proposed joint entity–relation extraction model and relation completion model are significantly better than those of the baselines. Finally, an industry chain information model is established based on the data of 18 industry chains in the field of basic machinery, which proves the feasibility of the proposed method.
In graphic design, layout is a result of the interaction between the design elements in the foreground and background images. However, prevalent research focuses on enhancing the quality of layout generation algorithms, overlooking the interaction and controllability that are essential for designers when applying these methods in real-world situations. This paper proposes a user-centered layout design system, Iris, which provides designers with an interactive environment to expedite the workflow, and this environment encompasses the features of user-constraint specification, layout generation, custom editing, and final rendering. To satisfy the multiple constraints specified by designers, we introduce a novel generation model, multi-constraint LayoutVQ-VAE, for advancing layout generation under intra- and inter-domain constraints. Qualitative and quantitative experiments on our proposed model indicate that it outperforms or is comparable to prevalent state-of-the-art models in multiple aspects. User studies on Iris further demonstrate that the system significantly enhances design efficiency while achieving human-like layout designs.
As one of the essential tools for spatio‒temporal traffic data mining, vehicle trajectory clustering is widely used to mine the behavior patterns of vehicles. However, uploading original vehicle trajectory data to the server and clustering carry the risk of privacy leakage. Therefore, one of the current challenges is determining how to perform vehicle trajectory clustering while protecting user privacy. We propose a privacy-preserving vehicle trajectory clustering framework and construct a vehicle trajectory clustering model (IKV) based on the variational autoencoder (VAE) and an improved K-means algorithm. In the framework, the client calculates the hidden variables of the vehicle trajectory and uploads the variables to the server; the server uses the hidden variables for clustering analysis and delivers the analysis results to the client. The IKV’ workflow is as follows: first, we train the VAE with historical vehicle trajectory data (when VAE’s decoder can approximate the original data, the encoder is deployed to the edge computing device); second, the edge device transmits the hidden variables to the server; finally, clustering is performed using improved K-means, which prevents the leakage of the vehicle trajectory. IKV is compared to numerous clustering methods on three datasets. In the nine performance comparison experiments, IKV achieves optimal or sub-optimal performance in six of the experiments. Furthermore, in the nine sensitivity analysis experiments, IKV not only demonstrates significant stability in seven experiments but also shows good robustness to hyperparameter variations. These results validate that the framework proposed in this paper is not only suitable for privacy-conscious production environments, such as carpooling tasks, but also adapts to clustering tasks of different magnitudes due to the low sensitivity to the number of cluster centers.
Currently, decarbonization has become an emerging trend in the power system arena. However, the increasing number of photovoltaic units distributed into a distribution network may result in voltage issues, providing challenges for voltage regulation across a large-scale power grid network. Reinforcement learning based intelligent control of smart inverters and other smart building energy management (EM) systems can be leveraged to alleviate these issues. To achieve the best EM strategy for building microgrids in a power system, this paper presents two large-scale multi-agent strategy evaluation methods to preserve building occupants’ comfort while pursuing system-level objectives. The EM problem is formulated as a general-sum game to optimize the benefits at both the system and building levels. The α-rank algorithm can solve the general-sum game and guarantee the ranking theoretically, but it is limited by the interaction complexity and hardly applies to the practical power system. A new evaluation algorithm (TcEval) is proposed by practically scaling the α-rank algorithm through a tensor complement to reduce the interaction complexity. Then, considering the noise prevalent in practice, a noise processing model with domain knowledge is built to calculate the strategy payoffs, and thus the TcEval-AS algorithm is proposed when noise exists. Both evaluation algorithms developed in this paper greatly reduce the interaction complexity compared with existing approaches, including ResponseGraphUCB (RG-UCB) and αInformationGain (α-IG). Finally, the effectiveness of the proposed algorithms is verified in the EM case with realistic data.
Dy3+-doped fluoride fiber lasers have important applications in environment monitoring, real-time sensing, and polymer processing. At present, achieving a high-efficiency and high-power Dy3+-doped fluoride fiber laser in the mid-infrared (mid-IR) region over 3 μm is a scientific and technological frontier. Typically, Dy3+-doped fluoride fiber lasers use a unidirectional pumping method, which suffers from the drawback of high thermal loading density on the fiber tips, thus limiting power scalability. In this study, a bi-directional in-band pumping scheme, to address the limitations of output power scaling and to enhance the efficiency of the Dy3+-doped fluoride fiber laser at 3.2 μm, is investigated numerically based on rate equations and propagation equations. Detailed simulation results reveal that the optical‒optical efficiency of the bi-directional in-band pumped Dy3+-doped fluoride fiber laser can reach 75.1%, approaching the Stokes limit of 87.3%. The potential for further improvement of the efficiency of the Dy3+-doped fluoride fiber laser is also discussed. The bi-directional pumping scheme offers the intrinsic advantage of mitigating the thermal load on the fiber tips, unlike unidirectional pumping, in addition to its high efficiency. As a result, it is expected to significantly scale the power output of Dy3+-doped fluoride fiber lasers in the mid-IR regime.