中国信通院人工智能研究所所长魏凯在主题演讲时表示，人工智能正以前所未有的速度重塑世界，而数据作为AI发展的核心驱动力，其产业发展态势备受 关注。高质量数据集是用于训练、验证和优化大模型，覆盖多行业公域和私域数据，具备高技术含量、高知识密度、高价值应用的"三高"特征。然而，高质 量数据集建设也面临着目标定位模糊、实施路径碎片化、技术底座薄弱三大难题。对此，我国在高质量数据集建设方面有着强有力的政策驱动。国家部委和 地方政府注重"人工智能+数据要素"政策协同布局，在破局路径上，新一代数据标注和合成数据也为突破"数据墙"提供了解决思路。 中国电子党组成员、副总经理王桂荣王桂荣在致辞时表示，当前人工智能非常火热，代表着科技革命和产业革命的最新方向。中国电子正抢抓人工智能发展 机遇，建立了完整的集成电路产业链，构建了以飞腾、麒麟、达梦、中国电子云、中国长城为代表的全栈信创底座，并积极落实"数据要素×"行动，在数据 资源利用与价值激活上进行了诸多探索。中国电子云作为中国电子自主计算体系核心组成，自主研发了集通算、智算、超算一体的CECSTACK专属云平 台，为人工智能应用发展提供高效算力支撑，打造了全链路AI解决方案，将 ...

New chapter It's finally happening. More than a year after agreeing to a merger, Paramount Global (PARA) has finally gotten the necessary clearance to combine with David Ellison's Skydance Media. The hope here is to resurrect a Hollywood icon that has shed much of its market value, which had also been sought after by Sony Pictures (SONY), Apollo (APO) and IAC Chair Barry Diller. PARA +2.5% premarket.Snapshot: It's been a tough environment out there for legacy media. Streaming giants like Netflix (NFLX ...

(Dollar amounts are in thousands, in Canadian currency unless otherwise specified) GUELPH, Ontario, July 24, 2025 (GLOBE NEWSWIRE) -- Hammond Power Solutions Inc. (“HPS”) (TSX: HPS.A) a leading manufacturer of dry-type transformers, power quality products and related magnetics, today announced its financial results for the second quarter 2025. HIGHLIGHTS Record sales of $224 million in the quarter, a 13.8% increase vs quarter 2, 2024.Adjusted EBITDA of $33 million in the quarter, or 14.9% of sales.Gross ma ...

Core Viewpoint - GE Vernova Inc. (GEV) is expected to report strong second-quarter 2025 results, with significant growth in both revenue and earnings per share (EPS) anticipated [1][2]. Revenue and Earnings Estimates - The Zacks Consensus Estimate for revenues is $8.79 billion, reflecting a 7.1% increase from the previous year [2]. - The consensus estimate for EPS is $1.64, indicating a substantial growth of 131% year-over-year [2]. - The EPS estimate has risen by 5.8% over the past 60 days [2]. Performance History - GEV has exceeded the Zacks Consensus Estimate in three of the last four quarters, with an average surprise of 34.70% [2]. Earnings Prediction - The model predicts an earnings beat for GEV, supported by a positive Earnings ESP and a Zacks Rank of 1 (Strong Buy), 2 (Buy), or 3 (Hold) [3][5]. Segment Performance Expectations - The Power segment is expected to see revenue growth of 3.8%, with estimates at $4,623.9 million, driven by strong sales of gas power equipment [7]. - The Electrification segment is projected to grow by 18.1%, with revenues estimated at $2,113.5 million, boosted by increased demand for transmission-related equipment [9]. - The Wind segment's revenue is estimated at $2,182.1 million, reflecting a 5.8% increase, although offshore production may face challenges [10]. Factors Influencing Earnings - Positive factors for GEV's earnings include favorable pricing, higher productivity, and cost reduction initiatives [11]. - However, increased expenses for research and development and capacity expansions may negatively impact earnings [12]. Stock Performance and Valuation - GEV shares have increased by 42% over the past six months, outperforming the Zacks Alternative-Energy industry, which rose by 5% [13]. - GEV's forward 12-month price-to-earnings (P/E) ratio is 58.38X, significantly higher than the industry average of 17.24X, indicating a premium valuation [16]. Industry Context - The growing global demand for electricity, driven by data center expansion and energy consumption, is boosting the adoption of renewable energy sources, benefiting companies like GEV [19]. - GEV's established expertise in the electric power sector, with a technology base of around 55,000 wind turbines and 7,000 gas turbines, positions it well for continued growth [20].

TAIPEI, July 18, 2025 /PRNewswire/ -- TECO Electric & Machinery Co., Ltd. (TWSE: 1504) has announced a major milestone following its March acquisition of Malaysian MEP engineering company NCL Energy. The company has secured significant data center projects in Selangor and Johor Bahru, marking its entry into the hyperscale data center sector. With a combined capacity of 178 MW, these two contracts have a total value exceeding MYR 170 million (approximately TWD 1.17 billion).Chairman Morris Li emphasized that ...

Core Insights - The article presents a comprehensive overview of latent space reasoning, highlighting its potential to achieve over 2700 times the bandwidth of traditional explicit reasoning chains (CoT) [1][15]. Group 1: Overview of Latent Space Reasoning - Latent space reasoning is an emerging field that traces its origins to the 2019 ICLR paper "Universal Transformers" by researchers from the University of Amsterdam and Google Brain [7]. - The article introduces a unified framework for latent space reasoning, which is based on mechanical interpretability and connects with the internal operations of models [3][4]. - The framework aims to facilitate future explorations, such as investigating infinite-depth reasoning through diffusion models [4]. Group 2: Mechanisms of Latent Space Reasoning - Latent space reasoning employs latent chains of thought, which represent reasoning in a continuous internal form rather than discrete natural language tokens [13][14]. - This method significantly enhances bandwidth, with each token in explicit CoT being approximately 15 bits, while latent CoT operations in a 2560-dimensional FP16 space yield around 40960 bits per step [15]. - The reasoning process is not constrained by a limited vocabulary, allowing for richer expressive capabilities [16]. Group 3: Modes of Latent Space Reasoning - There are two primary modes of latent space reasoning: vertical cycles and horizontal cycles [19]. - Vertical cycles utilize activation-based methods to extend computational depth, allowing models to repeatedly process the same set of layers to enhance reasoning [20][21]. - Horizontal cycles focus on expanding the model's memory and reasoning capabilities over time, maintaining a compressed hidden state that aggregates information from multiple time steps [28][29]. Group 4: Depth and Reasoning Capacity - The relationship between layer depth and reasoning capability is critical, with studies indicating that the implicit reasoning chain ability of models is strictly limited by the number of layers [34][40]. - Sufficient layer depth is necessary to execute multi-hop reasoning tasks effectively, as insufficient layers can hinder the emergence of final reasoning results [36][41]. - Research has established that the achievable length of reasoning chains is linearly related to the number of layers, positioning layer depth as a primary bottleneck for latent reasoning capacity [45]. Group 5: Advanced Reasoning Paradigms - The concept of "infinite depth reasoning" is proposed, allowing AI to allocate unlimited "thinking time" to refine solutions without output length constraints [53]. - This can be achieved through spatial infinite reasoning, which utilizes text diffusion models, and temporal infinite reasoning, which equates longer sequences with more optimization iterations [54][57]. - The article discusses specific methods for implementing these advanced paradigms, emphasizing their potential to enhance latent space reasoning [58].

Company Announcement - Hammond Power Solutions Inc. will release its financial results for the Second Quarter ended June 28, 2025, on July 24, 2025, after markets close [1] - A conference call and webcast will be held on July 25, 2025, at 9:00 a.m. Eastern Time to discuss the financial results [1] Company Overview - Hammond Power Solutions Inc. specializes in electrification through a wide range of dry-type transformers, power quality products, and related magnetics [3] - The company has manufacturing plants in Canada, the United States, Mexico, and India, and sells its products globally [3] - HPS shares are listed on the Toronto Stock Exchange under the symbol HPS.A [3]

Core Viewpoint - Hasbro warns that toy prices in the U.S. may rise later this year due to potential higher tariffs, with CEO Chris Cocks indicating that price increases could be seen by consumers between August and October [1][2] Group 1: Tariff Impact - The company has not yet raised prices due to tariffs but anticipates overall price increases in the toy industry [1] - Cocks emphasizes the need for flexibility in response to tariffs, stating that the company is focused on long-term strategies [1] - Approximately half of Hasbro's products are produced in the U.S., with the remainder manufactured abroad [1] Group 2: Domestic Manufacturing Challenges - Cocks acknowledges the challenges of increasing domestic production, citing high labor costs in the U.S. [2] - If toys are produced in the U.S., labor costs could account for 80% to 90% of total costs, potentially raising the price of a $10 toy to $18 to maintain profit margins [2] - The company is exploring opportunities for domestic production, such as Play-Doh, which could be made in the U.S. [2] Group 3: Financial Performance and Strategy - Hasbro's gaming division is performing well, reducing reliance on overseas manufacturing [3] - The company's licensing business has grown by 60% over the past three years, contributing significantly to its profitability [3] - Cocks points out that foreign suppliers have low profit margins of about 2% to 3%, making it difficult for them to absorb the 10% tariff costs [3]

Core Insights - The article discusses the development of Energy-Based Transformers (EBTs) that can learn to think independently through unsupervised learning, enhancing the model's reasoning capabilities akin to human System 2 thinking [9][10]. Group 1: System 2 Thinking and Model Development - Human thinking is categorized into System 1 (fast thinking) and System 2 (slow thinking), with the latter being crucial for complex tasks [3][4]. - Current large language models excel in System 1 tasks but struggle with System 2 tasks, prompting researchers to explore methods to enhance System 2 reasoning [4][5]. - EBTs are designed to assign energy values to input and candidate predictions, optimizing through gradient descent to simulate a thinking process [9][10]. Group 2: Performance and Scalability - EBTs demonstrate a 35% faster scalability in training compared to mainstream Transformer++ methods across various metrics such as data volume and model depth [11]. - In reasoning tasks, EBTs outperform Transformer++ by 29% in language tasks, indicating superior performance with increased computational effort [12]. - EBTs also excel in image denoising tasks, requiring fewer forward passes than diffusion Transformers while achieving better results [13]. Group 3: Generalization and Robustness - EBTs show enhanced generalization capabilities, particularly when handling out-of-distribution data, outperforming existing models even with similar or worse pre-training performance [14]. - The model's ability to learn and express uncertainty in predictions is highlighted, with EBTs effectively capturing the difficulty of token predictions [62][65]. - EBTs exhibit a linear trend in performance improvement as the distribution shift increases, emphasizing their critical role in cross-distribution generalization tasks [68][69]. Group 4: Experimental Results and Comparisons - EBTs outperform Transformer++ in various scalability metrics, including data efficiency and computational efficiency, suggesting they will excel in large-scale training scenarios [46][72]. - Despite slightly higher pre-training perplexity, EBTs achieve lower perplexity in downstream tasks, indicating stronger generalization capabilities [74]. - In image denoising tasks, EBTs significantly outperform DiT models, achieving better peak signal-to-noise ratios (PSNR) with 99% fewer forward passes [81][92].

Core Insights - GE Vernova Inc.'s Electrification segment is a key growth driver, focusing on modernizing grids and enabling smarter power distribution in the clean energy transition [1][3] - The demand for large-scale transmission equipment has surged due to rising electricity needs, particularly from electric vehicles and data centers, prompting significant investments in grid infrastructure [2] Company Performance - GE Vernova's Electrification segment has shown strong revenue growth, with year-over-year increases of 14% in Q1 2025, 11% in Q4 2024, 22% in Q3 2024, and 19% in Q2 2024, driven by demand for transformers and switchgears [4][10] - The company is well-positioned for continued growth, particularly in North America and Asia, as the global energy transition accelerates [5] Industry Context - Other companies like Siemens Energy and Eaton Corp. are also experiencing growth due to the surge in electricity generation and grid modernization, highlighting the clean energy industry's potential [6] - Siemens Energy's Grid Technologies segment reported a 33.7% year-over-year revenue increase and 41.6% order growth in Q2 fiscal 2025, while Eaton's Electrical Americas segment saw a 12% sales improvement in Q1 2025 [7][8] Valuation and Market Performance - GE Vernova's shares have increased by 211.6% over the past year, outperforming the industry's 53.6% gain [9] - The company is trading at a forward 12-month price-to-earnings (P/E) ratio of 56.25X, which is approximately 165.1% higher than the industry average of 21.21X [11] - The Zacks Consensus Estimate predicts a 6.4% sales improvement for 2025 and a 10% increase for 2026, with mixed earnings outlooks [12]