Data Quality Concerns - The Bureau of Labor Statistics (BLS) data quality and reliability has deteriorated over the decades [1] - Declining response rates and a refusal to correct for them are the root causes of the data quality deterioration [2] - BLS leadership did nothing to arrest the deterioration in data quality [3] Political Influence Allegations - There are concerns about potential political interference affecting BLS data [1] - The question of whether the president should fire BLS leadership for disliking the results is raised [4] - The loss of professional staff is unlikely to improve the situation [5] Confidence in Leadership - The nominee's answers do not inspire confidence regarding the protection of BLS independence [5]

I think the independence of the Fed is absolutely paramount. But we cannot ignore the elephant in the room, and that is that President Trump has launched an all out assault on the independence of the Federal Reserve. Dr.. Myron has said that, quote, To pretend that one can easily shift between highly political and allegedly nonpolitical roles without letting political biases inform policy is at best naive and at worst, sinister, sinister. So that's what you're about to do, go from a highly political role to ...

Data Reliability Concerns - Bureau of Labor Statistics (BLS) 数据质量和可靠性下降，修订增加 [2] - 本届政府正在解决数据质量问题 [3] Leadership Changes and Future Plans - 总统已任命新领导接管劳工统计局 (BLS) [3] - 目标是为美国人民提供诚实可靠的数据，以供重要的经济决策 [4] - 劳工统计局 (BLS) 计划继续发布月度就业报告 [1] - 需要审查美国获取重要数据的方式和方法 [3]

Well, now let's get back uh to the drama surrounding uh the Bureau of Labor Statistics, which will certainly come up with with the president at 8 a. m. the uh as well as the accuracy of the data.Senior economics reporter Steve Leeman is taking a look at the shrinking budget. Maybe that's one of the reasons, but uh Jeremy Seagull just teed off on the on it yesterday. Steve, it is amazing that you send stuff out the the response rate is in the 60s.used to be in the '9s. >> It's it's at 43% >> even lower. And ...

For example, I went to MIT, but yeah, I think half of the people who graduate with a CS degree, they they can't even code. And second, if you actually take the folks from MIT who can code, they're actually just going to try to cheat you. They're going to sell their accounts to somebody in a third world country.They're going to try to use LMS to generate the data for you. They're going to come up with all these crazy methods to cheat a system. So, it's also this really really challenging problem to detect lo ...

Core Insights - The article emphasizes the importance of aligning AI pricing with the value delivered to customers, moving beyond traditional pricing models based on usage or seats [2][3][4] - It highlights the necessity for robust infrastructure to support enterprise-level AI applications, focusing on reliability, scalability, and security [7][8][12] - The integration of AI into existing workflows is crucial for adoption, aiming for seamless automation that enhances productivity without disrupting established practices [14][21] - Continuous evolution and scalability of architecture are essential, with a recommendation to reassess systems every 6-12 months to adapt to changing technologies and user needs [19][20] - Data quality, transparency, and trust are foundational for reliable AI, necessitating investment in data governance and interpretability [26][29][30] - A customer-centric approach is vital, focusing on understanding and solving real user problems rather than merely showcasing technological capabilities [33][34][36] - The article discusses the potential of reasoning, planning, and agent capabilities as significant differentiators in AI systems [38][40] - Specialization in specific domains is encouraged, as it allows companies to leverage unique data and expertise to create competitive advantages [42][43][44] - Balancing human-machine collaboration is essential, ensuring that AI enhances human capabilities rather than replacing them [46][49][51] - The ability to iterate quickly and embrace experimentation is crucial for AI founders, promoting a culture of rapid prototyping and user feedback [53][55][56] Summary by Sections Pricing and Value Delivery - AI pricing should be based on the value delivered rather than traditional metrics like seat usage [2][3][4] Infrastructure Development - A strong infrastructure is necessary for enterprise AI, focusing on reliability, observability, and security [7][8][12] Workflow Integration - AI products should integrate seamlessly into existing workflows to minimize friction and enhance productivity [14][21] Architecture Evolution - Companies should prepare to reassess and evolve their AI architecture every 6-12 months [19][20] Data Quality and Trust - High-quality data and transparency are critical for reliable AI systems [26][29][30] Customer-Centric Approach - Understanding user needs and providing value should be prioritized over showcasing technology [33][34][36] Reasoning and Planning - Developing systems capable of reasoning and planning is a key opportunity for differentiation [38][40] Specialization - Focusing on specific domains can create significant competitive advantages [42][43][44] Human-Machine Collaboration - AI should enhance human capabilities, ensuring effective collaboration [46][49][51] Iteration and Experimentation - Embracing rapid iteration and user feedback is essential for AI development [53][55][56]

iMerit believes better quality data, not more data, is the future of AI | TechCrunch https://t.co/Q2Y5csiECy ...

Problem Statement - Data readiness is a myth, and achieving perfect data for AI is an unattainable pipe dream [1][2][3] - Fortune 500 companies lose an average of $250 million due to poor data quality [7] - Traditional semantic layers and knowledge graphs are insufficient for capturing the nuances of business language and tribal knowledge [8][9][10][11][12][13][14] Solution: Agentic Semantic Layer (PromQL) - PromQL is presented as a "day zero smart analyst" AI system that learns and improves over time through course correction and steering [17][18][19][20] - It uses a domain-specific language (DSL) for data retrieval, computation, aggregation, and semantics, decoupling LLM plan generation from execution [21][22] - The system allows for editing the AI's "brain" to correct its understanding and guide its learning [28] - It incorporates a prompt learning layer to improve the semantic graph and create a company-specific business language [31] - The semantic layer is version controlled, allowing for fallback to previous builds [33] Key Features and Benefits - Correctable, explainable, and steerable AI that improves with use [19] - Ability to handle messy data and understand business context [24][25] - Reduces months of work into immediate start, enabling faster AI deployments [37] - Self-improving and achieves 100% accuracy on complex tasks [37] Demonstrated Capabilities - The system can understand what revenue means and perform calculations [23] - It can identify and correct errors in data, such as incorrect status values [24] - It can integrate data from multiple databases and SAS applications [25][27] - It can summarize support tickets and extract sentiment [26][29] - It can learn the meaning of custom terms and relationships between tables [35][36] Customer Validation - A Fortune 500 food chain company and a high-growth fintech company achieved 100% accurate AI using PromQL [38]