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  • How to build an AI-oriented data governance system?
    How to build an AI-oriented data governance system?
    In recent years, with the emergence of new technology models, the polishing of the value of application scenarios in various industries and the improvement of product effects due to the accumulation of massive data, artificial intelligence applications have radiated from fields such as consumption and the Internet to traditional industries such as manufacturing, energy, and electricity. The maturity of artificial intelligence technology and application in enterprises in various industries in the main links of economic production activities such as design, procurement, production, management, and sales is constantly improving, accelerating the implementation and coverage of artificial intelligence in all links, and gradually integrating it with the main business , in order to improve industrial status or optimize operating efficiency, and further expand its own advantages. The large-scale implementation of innovative applications of artificial intelligence technology has promoted the vigorous development of the big data intelligence market, and also injected market vitality into the underlying data governance services. With big data, cloud computing and computing
    AI 1013 2024-04-12 14:31:14
  • How does AI artificial intelligence promote digital transformation?
    How does AI artificial intelligence promote digital transformation?
    It has been decades since artificial intelligence was proposed, but why has this technology experienced explosive growth only in recent years? This phenomenon is no accident. It is precisely thanks to the increasing maturity of digital technologies such as cloud computing, the Internet of Things, and big data that artificial intelligence has made substantial progress: cloud computing provides an open platform for artificial intelligence, and the Internet of Things ensures data security. Real-time sharing, and big data provides unlimited resources and algorithm support for deep learning. The integration of digital transformation of traditional enterprises and technologies in these fields has promoted the continuous upgrading of artificial intelligence technology, laying a solid foundation for its evolution from "intelligent perception" to "intelligent thinking" and "intelligent decision-making". Enterprises with strong digital innovation capabilities have an increasing influence on the market and consumers. Any digital transformation
    AI 650 2024-04-12 14:31:01
  • How to use AI to enhance energy visibility in buildings
    How to use AI to enhance energy visibility in buildings
    In the United States, approximately one-third of the energy used in buildings is wasted, costing as much as $150 billion annually. Today, more and more building facility managers are aware of this and want to identify every available asset to help control this cost. As we all know, artificial intelligence (AI) has become a powerful tool for industry leaders looking to improve energy efficiency. Coupled with zero building planning, advances in artificial intelligence set the stage for a transformative era in facilities management. Statistics from Data International Energy Occupation show that the construction industry accounts for up to 30% of global energy consumption, and optimizing energy consumption can help reduce the impact on the environment. Artificial intelligence helps managers make better, more informed, and more predictive decisions, thereby
    AI 425 2024-04-12 12:16:23
  • How AI and IoT are disrupting key industries
    How AI and IoT are disrupting key industries
    Artificial intelligence (AI) and the Internet of Things (IoT) have already driven significant developments in industries such as manufacturing and banking respectively, but combined, the two technologies offer powerful opportunities across a wide range of industries. The Internet of Things has created a real-time communication network of interconnected devices and has become a multi-billion dollar industry. Statista estimates that its revenue will exceed $1.3 trillion by 2024. At the same time, artificial intelligence has experienced tremendous growth since the release of consumer-facing generative AI programs. Here's how some leading industries are using these technologies, and how industry leaders see this use evolving in the future. Insurance Currently, the role of artificial intelligence in the insurance sector is to improve efficiency and processing
    AI 378 2024-04-12 11:55:26
  • New ideas for LiDAR simulation | LidarDM: Helps generate 4D world, simulation killer~
    New ideas for LiDAR simulation | LidarDM: Helps generate 4D world, simulation killer~
    Original title: LidarDM: GenerativeLiDARSimulationinaGeneratedWorld Paper link: https://arxiv.org/pdf/2404.02903.pdf Code link: https://github.com/vzyrianov/lidardm Author affiliation: University of Illinois, Massachusetts Institute of Technology Paper idea: Introduction to this article LidarDM, a novel lidar generation model capable of producing realistic, layout-aware, physically believable, and temporally coherent lidar videos. LidarDM has two unprecedented capabilities in lidar generative modeling: (1)
    AI 621 2024-04-12 11:46:15
  • Key use cases for industrial connectivity in manufacturing
    Key use cases for industrial connectivity in manufacturing
    In recent years, there has been increasing discussion of possibilities and potential such as smart factories and Industry 4.0, but the many benefits of these ambitious visions and strategies can now be realized by leveraging industrial connectivity. Industrial connectivity in manufacturing enables a variety of applications to increase efficiency, enhance production quality, enable real-time monitoring and control, and facilitate intelligent decision-making processes. Technologies such as smart manufacturing factories and Industry 4.0 have been widely discussed in recent years, but the many benefits of these ambitious visions and strategies that leverage industrial connectivity to break down the silos common in manufacturing are now achievable. In practice, several common use cases for providing standardized data access through industrial connectivity have had a significant impact on global manufacturing. Some of these possible key use cases include: Real-time data monitoring and analytics in manufacturing
    AI 770 2024-04-12 09:16:32
  • How IoT sensors and AI are revolutionizing smart buildings
    How IoT sensors and AI are revolutionizing smart buildings
    With the continuous development of smart technology, smart buildings have become a powerful force in today's construction industry. In the rise of smart buildings, Internet of Things (IoT) sensors and artificial intelligence (AI) have played a crucial role. Their combination is not just a simple technical application, but also a complete subversion of traditional building concepts, bringing us a more intelligent, efficient and comfortable building environment. Over the past few years, and especially in the wake of the COVID-19 pandemic, the challenges facing building management have increased and evolved as expectations for facilities managers have changed and viability needs have expanded. The shift to more integrated and flexible work environments within offices is also changing the way commercial buildings are used, requiring real-time visibility into building usage, occupant trends
    AI 967 2024-04-12 09:10:15
  • BAIC Jihu releases Darwin 2.0 technology system, leading new energy vehicle technology to new heights
    BAIC Jihu releases Darwin 2.0 technology system, leading new energy vehicle technology to new heights
    On April 11, Tesla 2.0 technology was grandly released in Beijing. Jihu Motors once again leads the future with innovations in the field of smart travel. The Darwin 2.0 technology system is the result of Jihu Auto’s in-depth exploration and practice in the field of intelligent travel. It integrates seven major technology sectors, including intelligent driving, intelligent interconnection, intelligent safety and other aspects, to provide users with a comprehensive and intelligent travel experience. Through advanced sensors and algorithms, this technology system can achieve precise perception and intelligent decision-making of vehicle driving conditions, ensuring that users can enjoy a safe and comfortable driving experience in various traffic environments. At the same time, Darwin 2.0 technology also focuses on upgrading user experience. Through seamless connection with smart devices, it realizes the integration of in-vehicle systems with mobile phones and smart homes.
    AI 976 2024-04-12 09:04:13
  • Recommender systems based on causal inference: review and prospects
    Recommender systems based on causal inference: review and prospects
    The theme of this sharing is recommendation systems based on causal inference. We review past related work and propose future prospects in this direction. Why do we need to use causal inference techniques in recommender systems? Existing research work uses causal inference to solve three types of problems (see Gaoe et al.'s TOIS2023 paper Causal Inference in Recommender Systems: ASurvey and Future Directions): First, there are various biases (BIAS) in recommendation systems, and causal inference is an effective way to remove these Tools for bias. Recommender systems may face challenges in addressing data scarcity and the inability to accurately estimate causal effects. in order to solve
    AI 635 2024-04-12 09:01:07
  • Long text cannot kill RAG: SQL+ vector drives large models and the new paradigm of big data, MyScale AI database is officially open source
    Long text cannot kill RAG: SQL+ vector drives large models and the new paradigm of big data, MyScale AI database is officially open source
    The combination of large models and AI databases has become a magic weapon for reducing costs and increasing efficiency for large models and making big data truly intelligent. The wave of large models (LLM) has been surging for more than a year, especially models represented by GPT-4, Gemini-1.5, Claude-3, etc., which have become a well-deserved hot spot. On the LLM track, some research focuses on increasing model parameters, and some are crazy about multi-modality... Among them, LLM's ability to process context length has become an important indicator for evaluating models. A stronger context means that the model Have stronger retrieval performance. For example, the ability of some models to process up to 1 million tokens in one go has led many researchers to think about RAG (R
    AI 1163 2024-04-12 08:04:24
  • Face Wall Intelligence completed a new round of financing of several hundred million yuan and continued its journey towards efficient large models for AGI
    Face Wall Intelligence completed a new round of financing of several hundred million yuan and continued its journey towards efficient large models for AGI
    Recently, Wall-facing Intelligence completed a new round of financing of several hundred million yuan, led by Primavera Ventures and Huawei Hubble, and followed by the Beijing Artificial Intelligence Industry Investment Fund, with Zhihu continuing to invest as a strategic shareholder. After the completion of this round of financing, Face Wall Intelligence will further promote the introduction of outstanding talents, strengthen the basic computing power and data foundation for large model deployment, continue to lead the "efficient large model" route, promote efficient training of large models, and quickly implement applications. Light Source Capital served as the exclusive financial advisor for this round. Based on a solid foundation of original AI technology, Wall-Facing Intelligence is one of the largest large model teams in the world that is at the forefront of exploring "efficient large models". It has now completed large model full-stack technology that implements efficient training, efficient implementation and efficient reasoning. Production line layout. The core R&D team was born out of Tsinghua NL
    AI 990 2024-04-11 21:22:01
  • Exploring the boundaries of agents: AgentQuest, a modular benchmark framework for comprehensively measuring and improving the performance of large language model agents
    Exploring the boundaries of agents: AgentQuest, a modular benchmark framework for comprehensively measuring and improving the performance of large language model agents
    Based on the continuous optimization of large models, LLM agents - these powerful algorithmic entities have shown the potential to solve complex multi-step reasoning tasks. From natural language processing to deep learning, LLM agents are gradually becoming the focus of research and industry. They can not only understand and generate human language, but also formulate strategies, perform tasks in diverse environments, and even use API calls and coding to Build solutions. In this context, the introduction of the AgentQuest framework is a milestone. It not only provides a modular benchmarking platform for the evaluation and advancement of LLM agents, but also provides researchers with a Powerful tools to track and improve the performance of these agents at a more granular level
    AI 1038 2024-04-11 20:52:21
  • Multiple SOTAs! OV-Uni3DETR: Improving the generalizability of 3D detection across categories, scenes and modalities (Tsinghua & HKU)
    Multiple SOTAs! OV-Uni3DETR: Improving the generalizability of 3D detection across categories, scenes and modalities (Tsinghua & HKU)
    This paper discusses the field of 3D object detection, especially 3D object detection for Open-Vocabulary. In traditional 3D object detection tasks, systems need to predict the localization of 3D bounding boxes and semantic category labels for objects in real scenes, which usually relies on point clouds or RGB images. Although 2D object detection technology performs well due to its ubiquity and speed, relevant research shows that the development of 3D universal detection lags behind in comparison. Currently, most 3D object detection methods still rely on fully supervised learning and are limited by fully annotated data under specific input modes, and can only recognize categories that emerge during training, whether in indoor or outdoor scenes. This paper points out that the challenges facing 3D universal target detection are mainly
    AI 298 2024-04-11 19:46:18
  • AI security company TrojAI receives additional seed funding
    AI security company TrojAI receives additional seed funding
    TrojAI, a Canadian AI security solutions provider, announced this week that it has received $5.75 million in additional seed funding. The enterprise AI security platform provided by TrojAI helps customers protect AI models and applications from risks and attacks. Its platform can test AI models before deployment and protect applications from issues such as sensitive data leakage, helping enterprises comply with benchmarks such as the OWASPAI framework and privacy regulations. Its main business modules are as follows: AI model risk detection: The TrojAI platform can be integrated with AI and MLOps workflows to automatically penetrate and scan AI models before production to identify potential risks and vulnerabilities, such as backdoors, data leaks and bias. AI application protection: TrojAI platform can protect A
    AI 738 2024-04-11 19:43:17
  • By 2028, the smart building AI market is expected to reach US$6.48 billion
    By 2028, the smart building AI market is expected to reach US$6.48 billion
    This new research, based on analysis of the artificial intelligence (AI) market, looks at the progress being made in AI’s broad capabilities and its specialized applications that are making the built environment smarter, more sustainable, and more responsive. This is the first in a two-part series, with the second on the AI ​​market landscape to be released later this year. This report explores where we are on the journey towards “truly cognitive architecture”. Today’s commercial construction technology is moving from rules-based analytics to artificial intelligence predictive machine learning models, but adoption rates are still at middling levels. The scope of real-world deployments remains narrow, driven primarily by more well-understood use cases in energy optimization, space utilization, and security. Challenges and obstacles hindering the application of artificial intelligence
    AI 913 2024-04-11 19:31:16

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