shutdown drags on. Increasingly, investors are seeking safety. We told you about the big gains for gold.Many are buying Bitcoin. Where should bonds fit into the equation. Where should fixed income in general fit into the equation.Well, joining us now with his fixed income outlook for the fourth quarter is Jeff Rosenberg, portfolio manager for Black Rockck Systematic Multistrategy Fund. It's great to have you here on set. >> Great to be here.Thanks. >> Let's start right there. You just put out your Q4 outloo ...

Investors are turning to so-called alternative data and other resources to get a grip on the U.S. labor market and economy during the government shutdown. ...

Core Viewpoint - The article discusses the erosion of trust in U.S. economic data due to actions taken by the Trump administration, including the dismissal of key officials and the undermining of independent statistical agencies [1][2][3]. Group 1: Impact on Economic Data - The U.S. Department of Labor's employment data, crucial for assessing the economy's health, was reported to be significantly weaker than expected, leading to President Trump's dismissal of the Labor Statistics Bureau chief [1]. - Trump's appointment of a loyalist to the Labor Statistics Bureau raises concerns about the independence and quality of economic data, as the new appointee has previously suggested halting employment data releases [2]. - The government's budget cuts have led to the disappearance of hundreds of data sets and over 8,000 government web pages, which are essential for public policy and economic analysis [3]. Group 2: Alternative Data Sources - Some institutional investors have begun using alternative data, such as satellite imagery, to gain insights into economic performance, indicating a shift in how market participants assess economic conditions [4]. - The reliance on alternative data raises concerns about market fairness, as access to such data is often limited to wealthier investors, creating disparities in information availability [4]. - While advancements in technology are making alternative data more accessible, it is still years away from fully replacing traditional economic data collection methods [4].

Core Viewpoint - Over-reliance on alternative data sources can severely limit the ultimate capabilities of models, and true breakthroughs must be built on real data [1][10] Group 1: The Dilemma of Alternative Data - Researchers in robotics often seek cheaper alternatives to real data due to high collection costs, leading to a compromise in model performance [2][3] - Common alternative methods include simulation training, learning from human videos, and using handheld devices to mimic robotic actions, but each method ultimately weakens the model's true potential [3][4] Group 2: Intersection Dilemma - The collection of data inevitably involves human judgment, which can limit the problem-solving approach when avoiding real data [4][6] - As models grow stronger, they can better distinguish between alternative and real data, leading to a smaller intersection of effective behaviors [6][7] Group 3: The Importance of Real Data - Attempting to bypass real data results in a "spork" scenario, where neither alternative data nor real data is effectively utilized [10][11] - To build robust robotic models that generalize well, real data is essential, but it can be complemented with diverse data sources [11][12] Group 4: The "Spork" Phenomenon - The concept of "spork" applies to various AI research areas, where attempts to combine manual design with learning systems ultimately create performance bottlenecks [13]

Core Viewpoint - The article discusses the challenges and limitations of using alternative data for training large models in the context of artificial intelligence, particularly in robotics, emphasizing that while alternative data can reduce costs, it often compromises the model's generalization capabilities [6][30][40]. Group 1: Challenges in Training Large Models - Training large models, especially in robotics, requires vast amounts of real-world interaction data, which is costly to obtain [2][4]. - Researchers are exploring alternative data sources to balance cost and training effectiveness, but achieving this balance is complex [5][8]. Group 2: Alternative Data Strategies - Various methods for obtaining alternative data include simulation, human videos, and handheld gripper devices, each with its own strengths and weaknesses [10][12][13]. - While these methods have produced significant research outcomes, they represent compromises that may weaken the inherent capabilities of large-scale learning models [14]. Group 3: Limitations of Alternative Data - The reliance on alternative data can lead to a disconnect between the training environment and real-world applications, limiting the model's ability to generalize effectively [26][28]. - The design decisions made when creating alternative data can significantly impact the overlap between successful strategies in real-world scenarios and those learned from alternative data [23][24]. Group 4: Importance of Real-World Data - Real-world data is essential for developing models with broad generalization capabilities, as it allows models to learn the true mechanisms of the world [36]. - Alternative data should be viewed as a supplementary source of knowledge rather than a replacement for real-world experience [37][38]. Group 5: The Concept of "Sporks" - The term "sporks" is used to describe alternative data approaches that attempt to combine the benefits of large-scale training with the cost-effectiveness of alternative data [39][40]. - Other "spork" methods include hybrid systems that combine manual design with learning components, aiming to mitigate the high data demands of machine learning [41][42].