自然语言处理(NLP)

Search documents
金融科技“新风口”?多家企业竞逐稳定币赛道
Sou Hu Cai Jing· 2025-07-14 09:19
7月14日消息,近日,金融科技领域风起云涌,稳定币成为众多企业竞相布局的关键领域。天阳科技、 拓尔思、伟仕佳杰等多家企业纷纷在稳定币相关业务上取得新进展或公布新举措,引发市场高度关注。 天阳科技:借势港元稳定币,布局流通基础设施 据人民财讯报道,天阳科技在机构调研中透露了重要合作信息。当前,京东、蚂蚁等科技巨头已积极投 身港元稳定币试点工作,且秉持开放生态策略,广泛寻求合作伙伴共同参与,以推动港元稳定币的多元 化发展。而在稳定币的流通体系中,自有生态外的流通环节至关重要,这离不开第三方机构提供坚实的 基础设施支持,其中U卡在流通环节蕴含着明确的合作机会。 报道称,天阳科技敏锐地捕捉到这一市场契机,目前已与一家港元稳定币发行机构进入合作后期阶段。 凭借此次合作,天阳科技有望在稳定币流通基础设施领域占据重要地位。 拓尔思:AI赋能,筑牢稳定币研究与风控防线 拓尔思今日通过官微发布消息,展示了其在加密货币和稳定币领域强大的技术实力。在稳定币的研究与 风控方面,拓尔思为相关机构提供了全方位的AI赋能解决方案。 伟仕佳杰:前瞻布局,探索东南亚支付结算新路径 伟仕佳杰(00856.HK)在港交所发布公告,宣布公司已开启合 ...
通往 AGI 之路的苦涩教训
AI科技大本营· 2025-06-26 11:10
Core Viewpoint - The article discusses the rapid advancement of AI and the potential for achieving Artificial General Intelligence (AGI) within the next 5 to 10 years, as predicted by Google DeepMind CEO Demis Hassabis, who estimates a 50% probability of this achievement [1] Group 1: AI Development and Challenges - The AI wave is accelerating at an unprecedented pace, but there have been numerous missteps along the way, as highlighted by Richard Sutton's 2019 article "The Bitter Lesson," which emphasizes the pitfalls of relying too heavily on human knowledge and intuition [2][4] - Sutton argues that computational power and data are the fundamental engines driving AI forward, rather than human intelligence [3] - The article suggests that many previously held beliefs about the paths to intelligence are becoming obstacles in this new era [4] Group 2: Paths to AGI - The article introduces a discussion on the "bitter lessons" learned on the road to AGI, featuring a dialogue with Liu Jia, a professor at Tsinghua University, who has explored the intersection of AI, brain science, and cognitive science [5][11] - Liu Jia identifies three paths to AGI: reinforcement learning, brain simulation, and natural language processing (NLP), but warns that each path has its own hidden risks [9] - The article emphasizes that language does not equate to cognition, and models do not represent true thought, indicating that while NLP is progressing rapidly, it is not the ultimate destination [9][14] Group 3: Technical Insights - The article discusses the Scaling Law and the illusion of intelligence associated with large models, questioning whether the success of these models is genuine evolution or merely an illusion [15] - It raises concerns about the limitations of brain simulation due to computational bottlenecks and theoretical blind spots, as well as the boundaries of language in relation to understanding the world [14]
生物学的DeepSeek:阿里云发布LucaOne模型,首次统一DNA/RNA和蛋白质语言,能够理解中心法则
生物世界· 2025-06-19 09:44
Core Viewpoint - The article discusses the development of LucaOne, a generalized biological foundation model that can simultaneously understand and process nucleic acids (DNA and RNA) and protein sequences, marking a significant advancement in the field of life sciences [4][26]. Group 1: Introduction to LucaOne - LucaOne is the world's first foundational model capable of unifying the understanding of nucleic acids and protein sequences, likened to a "DeepSeek" for life sciences [4]. - The model was pre-trained on sequences from 169,861 species, showcasing its ability to comprehend key biological principles such as the translation of DNA into proteins [4][16]. Group 2: Technical Aspects of LucaOne - The model utilizes a vocabulary of 39 "characters" to encode nucleotides and amino acids, allowing it to read both nucleic acids and proteins [13]. - It employs semi-supervised learning, integrating known biological annotations to enhance its understanding [14]. - LucaOne has 1.8 billion parameters and has been trained on 36.95 billion biological sequence "words," enabling it to extract deep, universal patterns from nucleic acid and protein sequences [16]. Group 3: Performance and Capabilities - LucaOne demonstrated an impressive ability to understand the central dogma of molecular biology without explicit instruction, outperforming specialized models in tasks involving DNA and protein sequence matching [18]. - The model excels in generating embeddings that accurately capture the biological significance of sequences, outperforming other models in clustering similar sequences [19]. - It has shown strong performance across seven challenging bioinformatics tasks, including species classification and protein stability prediction, often using simpler downstream networks compared to specialized models [20][24]. Group 4: Significance and Future Outlook - LucaOne provides a unified framework for understanding the two core molecular carriers of life, breaking down barriers between different molecular types [26]. - The model exemplifies the potential of foundational models in bioinformatics, allowing researchers to develop various biological computational tools efficiently [26]. - It paves the way for deeper and more automated analysis of complex biological systems, such as gene regulatory networks and disease mechanisms [26].
给“开盒”上锁是平台的能力试金石
经济观察报· 2025-05-28 06:36
Core Viewpoint - Platforms must recognize that online violence governance should not merely be a superficial response to regulatory demands, but should be an internalized and proactive governance awareness, as it is crucial for the future ecosystem of the platform [1][6]. Group 1: Regulatory Actions - The Central Cyberspace Administration of China has issued a notice urging local internet departments and platforms to strengthen the rectification of the "opening box" issue, emphasizing a "zero tolerance" approach [2]. - The rise of "opening box" incidents, which involve severe online harassment and privacy violations, has prompted regulatory bodies to take serious action against these new forms of online violence [2][3]. Group 2: Platform Responsibilities - Platforms have failed in three main areas: promoting controversial content through their information push mechanisms, having loopholes in user identity verification, and delayed responses to complaints, which allows harmful information to spread [3]. - The need for platforms to prioritize the prevention of online violence, such as "opening box" incidents, is becoming increasingly urgent as public tolerance for such behavior has reached its limit [3]. Group 3: Proactive Measures - Platforms should not limit their responsibilities to merely notifying and deleting harmful content but should focus on prevention and intervention, establishing a proactive governance model [4][5]. - The implementation of technologies like natural language processing and behavior monitoring is essential for platforms to intercept harmful content before it spreads [5]. Group 4: Victim Support - Many victims face high barriers to asserting their rights on platforms, making it crucial for platforms to create quick reporting channels for "opening box" incidents and prioritize responses to victim requests [5]. - The introduction of features like "anti-violence mode" by platforms indicates a step towards better governance, but further actions are needed to effectively combat online violence [5].
小红书高级副总裁汤维维: 从“文字转换”到“文化解码”的跨越
Shen Zhen Shang Bao· 2025-05-27 20:29
Core Insights - In January 2025, a significant influx of overseas users began to engage with Xiaohongshu, leading to a unique cultural exchange where users shared pet experiences, assisted with English homework, and learned Chinese cooking from Chinese users [1][2] - The primary challenge faced by Xiaohongshu was the language barrier, prompting the need for effective communication tools to facilitate user interactions [1] Group 1: Technological Developments - Xiaohongshu quickly developed a "one-click translation" feature in response to user demands, allowing automatic translation of English comments into Chinese, thus streamlining the user experience [1] - The translation functionality is built on a multi-modal AI model that integrates Natural Language Processing (NLP), Optical Character Recognition (OCR), and Computer Vision (CV), enabling the system to understand not just text but also cultural nuances such as memes [1] - A dynamic learning mechanism is in place where user edits to translations contribute to ongoing model training, particularly enhancing the understanding of culturally sensitive content [1] Group 2: Cultural Integration - The company emphasizes that its translation capabilities extend beyond mere word-for-word translation to encompass cultural adaptation, reflecting the diversity of human civilization [1] - Xiaohongshu's approach illustrates the importance of embedding technology within a humanistic framework, transforming barriers into bridges for communication [2]
揭秘财报会议中的选举密码:如何用AI工具预测美国总统大选结果
Refinitiv路孚特· 2025-05-22 08:21
Core Viewpoint - The article discusses the challenges of predicting the outcome of the U.S. presidential elections, emphasizing the limitations of traditional polling methods and introducing an innovative approach using corporate executives' sentiments expressed during earnings calls to forecast election results [1][10]. Group 1: Political Polarization and Election Dynamics - The U.S. has experienced significant political polarization, with a solidification of party bases and a decrease in independent voters, making election outcomes heavily reliant on a few swing states [2]. - The "winner-takes-all" electoral system has led to controversial outcomes, as seen in the 2024 election where Trump won 312 electoral votes but only led the popular vote by 1.5% [2]. Group 2: Complexity of the Electoral College System - Variations in state election rules, such as mail-in ballot verification standards and counting timelines, complicate the prediction of election results [3]. - Historical anomalies, like the "Biden curve" in Pennsylvania during the 2020 election, highlight the unpredictability of the counting process [3]. Group 3: Impact of Unexpected Events and Media Influence - Political violence, scandals, and misinformation on social media can rapidly shift voter sentiment and influence election outcomes [4]. Group 4: Predictive Models and Their Limitations - Various models, such as the "White House Keys" model and Bayesian statistical models, have been developed to predict election outcomes, but they often lack accuracy and require extensive data [5][6][8]. - Historical trends, like the "Nevada bellwether," indicate that winning Nevada has often correlated with winning the presidency, as seen in Trump's 2.1% victory in the state [7]. Group 5: Issues with Traditional Polling - Polling suffers from sample bias and design flaws, leading to skewed results that may favor certain political parties [9]. - Manipulation and incentives in polling can distort data, affecting both local and national surveys [10]. Group 6: Alternative Predictive Methodology - The LSEG and MarketPsych's AI sentiment analysis tool, MarketPsych Transcript Analytics (MTA), offers a novel approach to predicting election outcomes by analyzing executives' sentiments during earnings calls [10][11]. - The tool captures subtle changes in tone and underlying messages, providing insights that may be more reliable than traditional polling data [10][22]. Group 7: Correlation Between Corporate Discussions and Election Outcomes - Analysis of earnings call transcripts reveals that the frequency of candidate mentions correlates with election results, with specific terms indicating support for either party [11][22]. - Industries such as energy and technology show distinct political leanings based on the discussions during earnings calls, reflecting their expectations of election outcomes [11].
彭博数据洞察 | 透过AI看新闻,投资信号抓得准
彭博Bloomberg· 2025-03-14 03:08
Group 1 - The article emphasizes the importance of AI-driven news summarization to extract insights and signals from real-time news, which has become a critical intelligence source for quantitative investors [3][4] - Bloomberg's flagship product provides comprehensive support for news headlines and content, covering thousands of themes and regions, with a rich tagging system to label topics, securities, and individuals [3][4] - The article illustrates the impact of news events on market prices, using the example of the Keystone pipeline shutdown, which led to a significant increase in crude oil prices shortly after the news broke [3][4] Group 2 - The article discusses the release of a framework by the Taskforce on Nature-related Financial Disclosures (TNFD) aimed at helping companies and financial institutions assess and disclose their reliance on natural resources and environmental impacts [7][8] - It highlights the importance of understanding ecological interconnections for investors and companies, as these factors can significantly affect market performance, brand reputation, and compliance status [7][8] - The example of Meiji Holdings illustrates how integrating supply chain data with biodiversity databases can help identify risks associated with suppliers located in high water stress or biodiversity integrity areas [8][9] Group 3 - The article analyzes the European automotive industry, indicating that sales momentum has been declining, with signs of demand weakness among suppliers emerging before the broader market recognized the trend [11][12] - The analysis is based on Bloomberg's global supply chain database, covering over 1,500 suppliers in the European automotive sector across 53 countries, combined with timely standardized financial data [12] - This integration of financial data and supply chain information is crucial for predicting industry trends and optimizing decision-making [12]