Core Viewpoint - The article discusses the historical context and contributions of Sepp Hochreiter and Jürgen Schmidhuber in the development of residual learning and its impact on deep learning, emphasizing that the concept of residual connections was introduced by Hochreiter in 1991, long before its popularization in ResNet [3][12][26]. Group 1: Historical Contributions - Sepp Hochreiter systematically analyzed the vanishing gradient problem in his 1991 doctoral thesis and proposed the use of recurrent residual connections to address this issue [3][12]. - The core idea of recurrent residual connections involves a self-connecting neuron with a fixed weight of 1.0, allowing the error signal to remain constant during backpropagation [13][14]. - The introduction of LSTM in 1997 by Hochreiter and Schmidhuber built upon this foundational concept, enabling effective long-term dependency learning in tasks such as speech and language processing [18][19]. Group 2: Evolution of Residual Learning - The Highway network, introduced in 2015, successfully trained deep feedforward networks with hundreds of layers by incorporating the gated residual concept from LSTM [23]. - ResNet, which gained significant attention in the same year, utilized residual connections to stabilize error propagation in deep networks, allowing for the training of networks with hundreds of layers [24][26]. - Both Highway networks and ResNet share similarities with the foundational principles established by Hochreiter in 1991, demonstrating the enduring relevance of his contributions to deep learning [26]. Group 3: Ongoing Debates and Recognition - Jürgen Schmidhuber has publicly claimed that various architectures, including AlexNet, VGG Net, GANs, and Transformers, were inspired by his lab's work, although these claims have not been universally accepted [28][31]. - The ongoing debate regarding the attribution of contributions in deep learning highlights the complexities of recognizing foundational work in a rapidly evolving field [10][32].

Core Viewpoint - The article discusses the recruitment process and qualifications for a dynamic target perception algorithm engineer in the autonomous driving industry, highlighting the importance of various technical skills and experience in sensor fusion and deep learning [4][6][8]. Group 1: Job Responsibilities - The role involves processing large amounts of autonomous driving data, building automated ground truth labeling systems, and designing cutting-edge AI and vision technologies [6]. - Responsibilities include detecting static scene elements like lane lines and traffic signs, tracking dynamic targets, and predicting the future trajectories and intentions of moving objects [8]. - The engineer will work on multi-sensor fusion, depth estimation, and developing calibration methods for various sensors [8]. Group 2: Qualifications - Candidates should have a master's degree in computer science, automation, mathematics, or related fields, with experience in perception algorithms for autonomous driving or ADAS systems being a plus [6]. - Proficiency in programming languages such as C++ or Python, along with solid knowledge of algorithms and data structures, is required [8]. - Familiarity with multi-view geometry, computer vision technologies, deep learning, and filtering and optimization algorithms is essential [8]. Group 3: Community and Learning Resources - The article mentions a community of nearly 4,000 members and over 300 autonomous driving companies and research institutions, providing a comprehensive learning path for various autonomous driving technologies [9]. - Topics covered include large models, end-to-end autonomous driving, sensor calibration, and multi-sensor fusion [9].

Core Insights - The article discusses the journey of Naveen Rao and his team from founding Nervana Systems to their new venture, Unconventional, highlighting the evolution of the AI hardware market and the challenges faced by startups in this space [1][30]. Group 1: Founding of Nervana Systems - In 2014, the founders of Nervana, including Naveen Rao, Amir Khosrowshahi, and Arjun Bansal, recognized the potential of deep learning and aimed to address the hardware limitations in AI processing [2][3]. - The team, all with backgrounds in neuroscience, was motivated by a fascination with intelligent machines and aimed to design specialized chips for machine learning [4][7]. Group 2: Acquisition by Intel - In 2016, Intel acquired Nervana for approximately $350 million to strengthen its position in the deep learning chip market, which was being dominated by NVIDIA [10][11]. - Following the acquisition, Rao led Intel's AI platform division, where they developed the Nervana NNP series of chips aimed at competing with NVIDIA's offerings [13][15]. Group 3: Challenges and Setbacks - Despite initial success, Intel announced in 2020 that it would cease development of the Nervana chips in favor of the technology acquired from Habana Labs, which posed a direct competition to Nervana's products [21][22]. - The performance of Habana's chips significantly outperformed Nervana's, leading to doubts about the future of Nervana within Intel's product lineup [19][21]. Group 4: Launch of Unconventional - After leaving Intel, Rao founded Unconventional, aiming to raise $1 billion with a target valuation of $5 billion, significantly higher than Nervana's previous valuation [26][30]. - Unconventional seeks to rethink the foundations of computing, potentially leveraging neuromorphic computing principles to create more efficient AI hardware [27][28]. Group 5: Market Dynamics - The AI hardware market has dramatically changed since 2014, with NVIDIA's market cap soaring to over $4 trillion and a surge in competition from both established companies and new startups [30][31]. - The current landscape presents both opportunities and challenges for new entrants like Unconventional, including the need to compete against NVIDIA's established ecosystem and address customer inertia [31][32].

Core Viewpoint - The company has over 20 years of experience in the bulk commodity information service sector, accumulating a vast amount of price and fundamental data [1] Group 1 - The data and information content is collected and written by a professional analyst team, ensuring authority, timeliness, and accuracy [1] - The company has established a data center within its software industrial park, capable of managing the entire data lifecycle from collection to application [1] - The company employs machine learning and deep learning algorithms to train, evaluate, optimize, and persist data sets, supporting business user modeling and forecasting needs [1]

Group 1 - MediaGo has officially become a member of IAB UK, a key authority in the UK digital advertising industry, furthering its commitment to compliance and transparency in the local market [1] - IAB UK is part of the global IAB network and includes over 300 leading media, brands, platforms, agencies, and technology companies in the UK, aiming to shape a sustainable future for the industry [1] - Membership in IAB UK provides MediaGo with opportunities to engage with industry leaders, participate in standard-setting, and gain insights into market trends, enhancing its business collaborations in the UK [1] Group 2 - MediaGo focuses on creating visible value for advertisers through technological innovation, exemplified by its upgraded SmartBid 3.0 product, which aids in budget management and conversion optimization [2] - The platform adheres to international compliance standards such as GDPR and is set to receive TrustArc's GDPR compliance certification again in 2025, emphasizing its commitment to user data privacy [2] - MediaGo employs advanced brand safety mechanisms and transparent data practices to ensure advertisers' confidence and provide secure services to European clients [2]

Core Viewpoint - The article discusses Geoffrey Hinton's perspective on artificial intelligence (AI), suggesting that AI may already possess a form of "subjective experience" or consciousness, albeit unrecognized by itself [1][56]. Group 1: AI Consciousness and Understanding - Hinton posits that AI might have a nascent form of consciousness, which is misunderstood by humans [2][3]. - He emphasizes that AI has evolved from keyword-based search systems to tools that can understand human intentions [10][14]. - Modern large language models (LLMs) exhibit capabilities that are close to human expertise in various subjects [15]. Group 2: Neural Networks and Learning Mechanisms - Hinton explains the distinction between machine learning and neural networks, with the latter inspired by the human brain's functioning [17][21]. - He describes how neural networks learn by adjusting the strength of connections between neurons, similar to how the brain operates [21][20]. - The breakthrough of backpropagation in 1986 allowed for efficient training of neural networks, significantly enhancing their capabilities [38][40]. Group 3: Language Models and Cognitive Processes - Hinton elaborates on how LLMs process language, drawing parallels to human cognitive processes [46][47]. - He asserts that LLMs do not merely memorize but engage in a predictive process that resembles human thought [48][49]. - The training of LLMs involves a cycle of prediction and correction, enabling them to learn semantic understanding [49][55]. Group 4: AI Risks and Ethical Considerations - Hinton highlights potential risks associated with AI, including misuse for generating false information and societal instability [68][70]. - He stresses the importance of regulatory measures to mitigate these risks and ensure AI aligns with human interests [72][75]. - Hinton warns that the most significant threat from advanced AI may not be rebellion but rather its ability to persuade humans [66]. Group 5: Global AI Landscape and Competition - Hinton comments on the AI competition between the U.S. and China, noting that while the U.S. currently leads, its advantage is diminishing due to reduced funding for foundational research [78][80]. - He acknowledges China's proactive approach in fostering AI startups, which may lead to significant advancements in the field [82].

Core Viewpoint - The article emphasizes the opportunities in the field of embodied intelligence, highlighting the need for skilled researchers and the benefits of joining a collaborative academic environment focused on artificial intelligence and robotics. Research Content - The research focuses on interdisciplinary areas such as artificial intelligence control theory, embodied intelligence control, and reinforcement learning control [11]. - Candidates are expected to have a deep understanding and interest in core research directions, with the ability to conduct theoretical innovation and experimental validation independently [2]. Candidate Requirements - **Postdoctoral Researchers**: Must hold a PhD in relevant fields from prestigious institutions, with a strong publication record in top-tier journals or conferences [2]. - **PhD Candidates**: Should possess a master's degree or an outstanding bachelor's degree in related disciplines [3]. - **Master's Candidates**: Expected to have a bachelor's degree in relevant fields from recognized universities [5]. - Candidates should demonstrate a solid foundation in mathematics and programming, with a keen interest in control theory, AI, and robotics [4]. Skills and Experience - Familiarity with deep learning and AI models such as CLIP, BLIP, and LLaVA is essential [6]. - Experience with classic models like VAE, Transformer, and BERT, along with strong algorithm design and programming skills, particularly in high-performance languages like C++ or Rust, is preferred [7][8]. - Practical experience in training, tuning, and deploying deep learning models is highly valued [12]. Mentor Introduction - Professor Ji Xiaoqiang, with a PhD from Columbia University, leads the AI Control and Decision Laboratory at The Chinese University of Hong Kong (Shenzhen) [13]. - His research focuses on intelligent control systems, and he has published over 50 papers in top international journals and conferences [13]. Benefits and Compensation - **Postdoctoral Researchers**: Eligible for annual pre-tax living allowances of 210,000 CNY, with additional subsidies and potential for significant research funding [14]. - **PhD Candidates**: Full or half scholarships available, with top candidates eligible for a principal's scholarship of 180,000 CNY per year [15]. - **Master's Candidates**: Opportunities for transitioning to PhD programs and additional living stipends for outstanding candidates [16]. Application Materials - Applicants must submit a complete CV in both Chinese and English, along with any published papers and evidence of research capabilities [19].

Core Viewpoint - The discussion emphasizes the emerging risks associated with AI, particularly the potential for AI to develop its own motivations and the challenges of understanding its decision-making processes [3][5][30]. Group 1: AI's Evolution - AI is transitioning from being a tool that responds to commands to a system that can set its own goals and motivations [7][8]. - The next generation of AI will not only be smarter but will also have the capability to create sub-goals, leading to a fundamental shift in its operational logic [9][10]. - Hinton warns that as AI begins to "want" to achieve certain tasks, it raises questions about whether it is assisting humans or making decisions on their behalf [11] Group 2: Understanding AI's Decision-Making - A significant risk highlighted is that AI operates in a "black box," meaning its decision-making processes are not transparent or easily understood by humans [11][17]. - Unlike traditional software, modern AI learns from vast amounts of data without clear traceability, making it difficult to ascertain how it arrives at specific conclusions [13][14]. - This lack of understanding poses serious risks, especially in high-stakes environments like healthcare and finance, where decisions can have significant consequences [17][28]. Group 3: Rapid Knowledge Sharing - Hinton points out that AI can share knowledge at an unprecedented speed, exponentially increasing its learning capabilities compared to human learning [19][21]. - The ability for multiple AI copies to learn simultaneously and share insights instantaneously creates a knowledge-sharing efficiency that is billions of times faster than human communication [25][27]. - This rapid evolution of AI capabilities outpaces human regulatory and safety measures, leading to a growing concern about the implications of such advancements [28][29]. Group 4: Urgency for Action - Hinton suggests that humanity may only have 5 to 20 years to address these challenges before AI surpasses human intelligence [28][30]. - The current pace of AI development is exponential, and the time available for humans to establish effective regulations and safeguards is diminishing [28][31]. - The urgency is underscored by the observation that while AI evolves rapidly, human responses in terms of regulation and understanding lag significantly behind [29][34].

Core Viewpoint - The rapid urbanization in China is driving the demand for special space robots, which are becoming standardized tools for routine operations due to their efficiency and low-risk non-excavation capabilities. The market for special space robots is projected to reach $700 million in 2024, growing by 16.67% year-on-year and accounting for 23% of the global market [1][4][6]. Market Policy - The Chinese government has issued several policies to support the development of the robotics industry, including special space robots, creating a favorable environment for industry growth [4][5]. Industry Chain - The industry chain for special space robots includes upstream components such as camera modules, servo motors, reducers, controllers, chips, sensors, and laser radars. The midstream involves research and manufacturing, while the downstream encompasses various applications in water supply, drainage, gas, electricity, and heating sectors [6][7]. Current Development - The expansion of urban infrastructure has led to an increased need for inspection, assessment, repair, and renovation of aging pipelines. Special space robots are transitioning from pilot projects to standard operational tools, with a notable increase in market penetration [1][6]. Competitive Landscape - The special space robot market, previously dominated by foreign companies, is seeing a rise in domestic firms such as Shenzhen Bomiv Technology Co., Ltd. and Wuhan Zhongyi IoT Technology Co., Ltd., which are increasing their market share through technological advancements [7][8]. Future Trends - The future of special space robots is expected to be shaped by advancements in artificial intelligence and deep learning, enhancing their environmental perception and autonomous decision-making capabilities. Collaboration among upstream, midstream, and downstream companies will be crucial for overcoming technical challenges and improving product quality [9][10].

Core Insights - The BEHAVIOR Challenge aims to advance embodied intelligence by uniting academic and industrial forces to tackle household robotics [3][4] - Inspired by the success of ImageNet, the challenge seeks to establish a standardized framework for evaluating robotic performance in household tasks [11][14] Challenge Overview - The first BEHAVIOR household challenge, sponsored by NVIDIA, requires participants to use the Xinghai R1 Pro robot to complete 50 household tasks in a realistic virtual environment [5][6] - Participants can choose their algorithms and have access to 10,000 expert demonstration trajectories for imitation learning [6] - The challenge includes two tracks: Standard Track (limited to visible information) and Privileged Track (access to detailed environmental data) [9] Objectives and Rationale - The initiative is driven by the need to address existing challenges in robotic learning, such as lack of standardization and fragmented task selection [25] - The goal is to create a "North Star" task for the robotics field, promoting community collaboration to advance embodied intelligence [16] Design Philosophy - BEHAVIOR emphasizes a human-centered approach, ensuring that AI enhances and empowers human capabilities rather than replacing them [18] - The challenge focuses on household tasks, defining clear standards for a true household robot, including navigation, fine manipulation, long-term planning, and dynamic adaptation [19] Scale and Potential - The challenge encompasses 1,000 household activities and 50 long-term tasks, with an average task duration of 6.6 minutes [21] - BEHAVIOR is positioned to potentially become the "next ImageNet" in the field of embodied intelligence, although its success will depend on future developments [21][22]