2006 年，多伦多大学 Geoffrey Hinton 教授等人提出逐层预训练方法，突破了深层神经网络训练的 技术瓶颈，为深度学习的复兴奠定了基础。 这个初夏 四位图灵奖得主 强化学习作为智能体与环境交互的学习范式，其核心思想早于深度学习兴起。2013 年 DeepMind 提 出的 DQN 已初步实现深度学习与强化学习的结合，而 2016 年 AlphaGo 的成功则将深度学习与强化 学习的融合推向公众视野，显著提升了这一交叉领域的关注度。 2025 年 6 月 6-7 日 中国，北京 与全球创新力量共赴智源大会 即刻报名，探寻 AI 时代的无尽边域 基础理论 在 AI 发展史上，连接主义（以神经网络为代表）与行为主义（以强化学习为代表）虽源自不同理论脉 络，但二者的技术交叉早有端倪。这两条主线原本独立成长、各自发展，如今交织融合，万宗归一，共 同构成了下一代通用人工智能的基石。 6 月 6 日，关于深度学习和强化学习的探讨，将在 2025 智源大会继续开展，如 「双星交汇 」般的时 空对话，总结过往、共探智能之谜的终极答案。 与此同时，推理大模型的兴起、开源生态的加速、具身智能的百花齐放，成为 2025 ...

Group 1 - The core idea is that coding for artificial intelligence (AI) is becoming as essential as reading and writing, with the potential to enrich lives through data utilization [1][2] - AI and data science can provide significant value across various professions, making AI-oriented coding skills more valuable than traditional coding [2][3] - The rapid rise of AI has led to an increase in job opportunities, emphasizing the importance of foundational skills, project work, and job searching in career development [3][4][6] Group 2 - Learning foundational skills in AI is a continuous process, with a focus on understanding key concepts in machine learning and deep learning [7][8] - Mathematics is crucial for AI roles, with an emphasis on linear algebra, probability, statistics, and exploratory data analysis [8][11] - Building a portfolio of projects that demonstrate skill progression is essential for career advancement in AI [24][26] Group 3 - The job search process in AI involves predictable steps, including researching roles, conducting informational interviews, and applying for positions [27][36] - Networking and building a supportive community are vital for career growth in the AI field [43][48] - The importance of continuous learning and adapting to new technologies is highlighted as a key to success in AI careers [10][41]

Core Insights - The article emphasizes the importance of coding in artificial intelligence as a new literacy skill, akin to reading and writing [7][8] - It outlines three key steps for career development in AI: learning foundational skills, engaging in project work, and finding a job [11][12] - The article discusses the necessity of technical skills in promising AI careers, including machine learning, deep learning, and software development [15][16] Group 1: Importance of Coding and AI Skills - Coding is becoming essential for effective communication between humans and machines, with AI applications becoming increasingly prevalent in various industries [8][9] - Foundational skills in AI include machine learning techniques such as linear regression, neural networks, and understanding the underlying mathematics [17][18] - Continuous learning and adapting to new technologies are crucial in the rapidly evolving field of AI [19][20] Group 2: Project Work and Career Development - Engaging in project work helps deepen skills, build a portfolio, and create impact, which is vital for career advancement in AI [12][13] - Identifying valuable projects involves understanding business problems, brainstorming AI solutions, and evaluating their feasibility [26][30] - A supportive community is essential for navigating the challenges of project work and career transitions in AI [14][33] Group 3: Job Search Strategies - The job search process in AI typically involves researching roles, preparing for interviews, and leveraging networks for opportunities [46][58] - Information interviews can provide valuable insights into specific roles and companies, helping candidates understand the skills required [52][54] - Building a strong portfolio of projects that demonstrate skill progression is beneficial when seeking employment in AI [40][45] Group 4: Overcoming Challenges - Many individuals experience imposter syndrome in the AI field, which can hinder their confidence and growth [10][70] - The article encourages embracing the learning journey and recognizing that mastery comes with time and experience [70]

Core Viewpoint - The article discusses the integration of return factors into an orthogonal layer within deep learning models to enhance stock selection effectiveness while maintaining low correlation with existing return factors [1][2]. Group 1: Deep Learning Model Enhancements - By incorporating return factors into the orthogonal layer, deep learning factors can maintain good stock selection performance while ensuring low correlation with these return factors [1]. - The deep learning model's black-box nature makes it challenging to manually adjust factor weights during significant market style shifts; thus, the orthogonal layer allows for easier manual adjustments without compromising stock selection effectiveness [1]. Group 2: Performance Metrics - After adding return factors to the orthogonal layer, deep learning factors still exhibit strong stock selection capabilities, achieving an Information Coefficient (IC) above 0.02 and an IC Information Ratio (IR) exceeding 6 [2]. - The combination of deep learning factors with manually constructed return factors leads to significant improvements in overall market long positions compared to using deep learning factors alone, although the enhancement varies across different index-enhanced portfolios [2]. Group 3: Correlation and Performance - The correlation between deep learning factors and multi-granularity factors remains low after integrating return factors into the orthogonal layer, with high-frequency data inputs showing a correlation of no more than 0.01 [2]. - Utilizing deep learning factors alongside multi-granularity factors can significantly enhance the performance of overall market long positions, although the deep learning factors show limited predictive capability for mid to large-cap stock returns, resulting in less noticeable improvements for index-enhanced portfolios [2].

Core Insights - Hangzhou is a leading city in China for AI image recognition technology, showcasing its strength and potential in this field [1] Group 1: Key Technologies - Deep learning and neural networks are the core of Hangzhou's AI image recognition technology, enabling accurate image content recognition through multi-layered neural networks [3] - Convolutional Neural Networks (CNN) are widely applied in Hangzhou's AI image recognition, effectively extracting spatial features and hierarchical information for tasks like facial recognition and object detection [4] - Generative Adversarial Networks (GAN) are utilized in Hangzhou for data augmentation and image restoration, enhancing model generalization and robustness [5] - Transfer learning and weak supervision learning address data scarcity and label shortage in image recognition tasks, improving model performance and scalability in Hangzhou's AI technology [6] Group 2: Conclusion - The continuous innovation and application of deep learning, CNN, GAN, transfer learning, and weak supervision learning have led to significant achievements in Hangzhou's AI image recognition field, laying a solid foundation for future development [7]

据悉，上海交通大学医学院附属精神卫生中心肖世富/岳玲教授团队联合上海科技大学沈定刚/潘永生教 授团队获得的研究成果在知名期刊《阿尔茨海默病预防杂志》(Journal of Prevention of Alzheimer's Disease)上刊登。据悉，轻度认知损害(Mild Cognitive Impairment, MCI)被视为阿尔茨海默病等认知障碍 的前期风险状态。这项研究对认知障碍的早期预测工作显得尤为关键：不仅有助于识别潜在患者，还能 为及时实施有效治疗提供可能，从而延缓疾病进展，改善患者预后。 来源：中国新闻网 中新网上海5月7日电 (记者 陈静) 当下，随着疾病修饰治疗药物(如仑卡奈单抗、多奈单抗)的获批并投 入临床使用，轻度认知损害(MCI)和轻度痴呆阶段成为阿尔茨海默病患者干预的黄金窗口期。记者7日 获悉，中国医学专家获得最新研究成果：成功构建 MCI预测模型，为认知障碍的早期识别提供新方 法。 据介绍，该模型基于结构磁共振图像(MRI)数据，建立了一套深度学习训练框架；通过基于多个感兴趣 区域的网络(MRNet)筛选并整合包括海马体、杏仁核、小脑等10个高区分度脑区特征，并进一步构建了 ...

Core Viewpoint - By 2025, artificial intelligence (AI) has transitioned from a buzzword in tech circles to an integral part of daily life, impacting various industries through applications like image generation, coding, autonomous driving, and medical diagnosis. The evolution of AI is marked by significant breakthroughs and challenges, tracing back to the Dartmouth Conference in 1956, leading to the current technological wave driven by large models [1]. Group 1: Historical Milestones - The Dartmouth Conference in 1956 is recognized as the birth of AI, where pioneers gathered to explore machine intelligence, laying the foundation for AI as a formal discipline [2][3]. - In 1957, Frank Rosenblatt developed the Perceptron, an early artificial neural network that introduced the concept of optimizing models using training data, which became central to machine learning and deep learning [4][6]. - ELIZA, created in 1966 by Joseph Weizenbaum, was the first widely recognized chatbot, demonstrating the potential of AI in natural language processing by simulating human-like conversation [7][8]. - The rise of expert systems in the 1970s, such as Dendral and MYCIN, showcased AI's ability to perform specialized tasks in fields like chemistry and medical diagnosis, establishing its application in professional domains [9][11]. - IBM's Deep Blue defeated world chess champion Garry Kasparov in 1997, marking a significant milestone in AI's capability to outperform humans in strategic decision-making [12][14]. - The 1990s to 2000s saw a shift towards data-driven algorithms in AI, emphasizing the importance of machine learning [15]. - The emergence of deep learning in 2012, particularly through the work of Geoffrey Hinton, revolutionized AI by utilizing multi-layer neural networks and backpropagation techniques, leading to significant advancements in model training [17][18]. - The introduction of Generative Adversarial Networks (GANs) in 2014 by Ian Goodfellow transformed the field of generative models, enabling the creation of realistic synthetic data [20]. - AlphaGo's victory over Lee Sedol in 2016 highlighted AI's potential in complex games requiring intuition and strategic thinking, further pushing the boundaries of AI capabilities [22]. - The development of large language models began with the introduction of the Transformer architecture in 2017, leading to models like GPT-3, which demonstrated emergent abilities and set the stage for the current AI landscape [24][26].

"恭喜！""当之无愧！" AISTATS官宣其获奖的推文下面，业界大佬齐聚，一片祝贺之声。 当初，这篇论文被AISTATS接收。 然而在谢赛宁本人的转发推文中，我们知道另一重内幕—— 衡宇 发自 凹非寺 量子位 | 公众号 QbitAI 谢赛宁十年前被NeurIPS （当时还叫NIPS） 拒收的论文，刚在今年获得了AISTATS 2025年度时间检验奖。 这篇论文就是《Deeply-Supervised Nets》 （DSN，深度监督网络） ，2014年9月挂上arXiv。 时间匆匆，十一年过去，属于是真·时间检验了。 它提出的中间层监督思想被谢赛宁后续作品REPA （Representation Alignment） 和U-REPA （U-Net Representation Alignment） 等继 承并发展，展示出从单一模型优化到跨模型知识迁移的演进。 而后两者在深度学习、扩散模型深化发展的这两年间，影响颇深。 这篇论文最初投稿给NeurIPS。虽然拿下8/8/7高分，但仍然被该顶会拒绝了。 他表示： 那次挫折一直萦绕在我心头，困扰着我…… 十一年前，拿到8/8/7高分却被拒 补充下背景信息—— 《D ...

金融界2025年5月5日消息，国家知识产权局信息显示，中国石油化工股份有限公司、中石化石油物探技 术研究院有限公司申请一项名为"一种基于深度学习的微地震事件强度评价方法及系统"的专利，公开号 CN119916443A，申请日期为2023年10月。 中石化石油物探技术研究院有限公司，成立于2022年，位于南京市，是一家以从事开采专业及辅助性活 动为主的企业。企业注册资本133611.989369万人民币。通过天眼查大数据分析，中石化石油物探技术 研究院有限公司共对外投资了1家企业，参与招投标项目179次，专利信息524条，此外企业还拥有行政 许可13个。 专利摘要显示，本发明提供了一种基于深度学习的微地震事件强度评价方法及系统，属于水力压裂微地 震监测资料解释领域。所述方法包括以下步骤：步骤1，建立正演模型；步骤2，构建训练数据集；步骤 3，构建微地震事件强度评价网络模型；步骤4，以训练数据集作为输入，对微地震事件强度评价网络模 型进行训练，得到训练好的微地震事件强度评价网络模型；步骤5，微地震事件强度评价。本发明利用 深度学习自动提取多道微地震事件的特征，并利用这些特征来进行微地震事件强度的分类；在构建数据 ...

河北大学科学与技术创新研究院院长杨晓晖认为，早期的人工智能研究主要集中在理论探索和基础算法 研究上，随着计算机技术的飞速发展，人工智能正逐渐从实验室走向实际应用。如今，人工智能已广泛 应用于图像识别、语音识别、自然语言处理等多个领域。 对于人工智能发展趋势，吉林大学计算机学院院长杨博表示，当前人工智能已迈入深度学习时代，以神 经网络为核心技术，通过构建更深更复杂的网络解决现实难题，未来发展趋势将围绕算力、数据、算法 三要素的深度融合展开，以开发更智能的程序，更加注重与人类的协作和交互，实现人机共生。 "人工智能大模型确实对生产生活产生了深远影响。然而，大模型也存在'高阶幻觉'问题，即模型生成 的内容逻辑严密但可能包含虚假信息，需引起警惕。"杨晓晖说，人工智能是提升效率的工具，而非人 类的替代品。人类应敬畏技术，了解其局限，避免过度依赖。同时，需加强数据治理，确保数据质量与 安全。在产业应用中，应聚焦数据整合与价值挖掘，推动人工智能与实体经济深度融合，促进经济社会 可持续发展。 多位人工智能领域专家参加当天活动，并围绕人工智能行业发展、趋势等方面进行了探讨。与会专家认 为，如今全球人工智能发展进入新阶段，人工智 ...