The emergence of generative Artificial Intelligence (AI) has created new opportunities for economic text analysis. This study proposes a Wage Sentiment Index (WSI) constructed with Large Language Models (LLMs) to forecast wage dynamics in Japan. The analysis is based on the Economy Watchers Survey (EWS), a monthly survey conducted by the Cabinet Office of Japan that captures real-time economic assessments from workers in industries highly sensitive to business conditions. The WSI extends the framework of the Price Sentiment Index (PSI) used in prior studies, adapting it specifically to wage related sentiment. To ensure scalability and adaptability, a data architecture is also developed that enables integration of additional sources such as newspapers and social media. Experimental results demonstrate that WSI models based on LLMs significantly outperform both baseline approaches and pretrained models. These findings highlight the potential of LLM-driven sentiment indices to enhance the timeliness and effectiveness of economic policy design by governments and central banks.

AI promises personalized learning and scalable education. As AI agents increasingly permeate education in support of teaching and learning, there is a critical and urgent need for data architectures for collecting and analyzing data on learning, and feeding the results back to teachers, learners, and the AI agents for personalization of learning at scale. At the National AI Institute for Adult Learning and Online Education, we are developing an Architecture for AI-Augmented Learning (A4L) for supporting adult learning through online education. We present the motivations, goals, requirements of the A4L architecture. We describe preliminary applications of A4L and discuss how it advances the goals of making learning more personalized and scalable.

The enterprises today are faced with the tough challenge of processing, storing large amounts of data in a secure, scalable manner and enabling decision makers to make quick, informed data driven decisions. This paper addresses this challenge and develops an effective enterprise data strategy in the cloud. Various components of an effective data strategy are discussed and architectures addressing security, scalability and privacy aspects are provided.

AI systems cannot exist without data. Now that AI models (data science and AI) have matured and are readily available to apply in practice, most organizations struggle with the data infrastructure to do so. There is a growing need for data engineers that know how to prepare data for AI systems or that can setup enterprise-wide data architectures for analytical projects. But until now, the data engineering part of AI engineering has not been getting much attention, in favor of discussing the modeling part. In this paper we aim to change this by perform a mapping study on data engineering for AI systems, i.e., AI data engineering. We found 25 relevant papers between January 2019 and June 2023, explaining AI data engineering activities. We identify which life cycle phases are covered, which technical solutions or architectures are proposed and which lessons learned are presented. We end by an overall discussion of the papers with implications for practitioners and researchers. This paper creates an overview of the body of knowledge on data engineering for AI. This overview is useful for practitioners to identify solutions and best practices as well as for researchers to identify gaps.

Regeneron isn't the only company eager to derive more value from its data. Despite the enormous amounts of data they collect and the amount of capital they invest in data management solutions, business leaders are still not benefitting from their data. According to IDC research, 83% of CEOs want their organizations to be more data driven, but they struggle with the cultural and technological changes needed to execute an effective data strategy. In response, many organizations, including Regeneron, are turning to a new form of data architecture as a modern approach to data management. In fact, by 2024, more than three-quarters of current data lake users will be investing in this type of hybrid "data lakehouse" architecture to enhance the value generated from their accumulated data, according to Matt Aslett, a research director with Ventana Research.

Genomics England partners with the NHS to provide whole genome sequencing diagnostics. We also equip researchers to find the causes of disease and develop new treatments – with patients and participants at the heart of it all. Our mission is to continue refining, scaling, and evolving our ability to enable others to deliver genomic healthcare and conduct genomic research. We are accelerating our impact and working with patients, doctors, scientists, government and industry to improve genomic testing, and help researchers access the health data and technology they need to make new medical discoveries and create more effective, targeted medicines for everybody. The Data Architect will be joining the Enterprise Data Squad and will take responsibility for ensuring that our digital products are underpinned by a robust data architecture.

Although nothing really changes but the date, a new year fills everyone with the hope of starting things afresh. If you add in a bit of planning, some well-envisioned goals, and a learning roadmap, you'll have a great recipe for a year full of growth. This post intends to strengthen your plan by providing you with a learning framework, resources, and project ideas to help you build a solid portfolio of work showcasing expertise in data science. Just a note: I've prepared this roadmap based on my personal experience in data science. This is not the be-all and end-all learning plan.

Role requiring'No experience data provided' months of experience in Richmond Pay if you succeed in getting hired and start work at a high-paying job first. Get Paid to Read Emails, Play Games, Search the Web, $5 Signup Bonus. Role requiring'No experience data provided' months of experience in None Pay if you succeed in getting hired and start work at a high-paying job first. Get Paid to Read Emails, Play Games, Search the Web, $5 Signup Bonus. Piper Enterprise Solutions is searching for a Principal Data Architect for a Healthcare Data and Information company.

Let s immerse on a journey that shapes the future of coffee, transforming one person, one cup and one neighborhood at a time. Primary Responsibilities: • Own the technical relationship and discovery with cross-functional leaders to create product-centric solutions to advance the sustainability cause.

Detailed JD: • Minimum of 15 years of technical experience in Oracle ERP (Oracle Cloud/PeopleSoft) • Experience with preparation of data strategy, migration plan, object dependencies, etc. • Experience in Oracle Financials Cloud Schema and Data model • Experience with Master (customer, supplier, COA, etc.) and transaction (GL, PO, AP, etc.) data in Oracle Financials Cloud • Experience with conducting impact assessment on outbound data payload from Oracle Financials Cloud to data lake • Experience in creating design document for accommodating changes to the payload • Experience with optimizing data transfer (Extraction, Cleansing,Transformation, Loading and Validation) from Oracle Financials Cloud to data lake • Hands on with writing complex SQL • Excellent oral and written communication skills • Good understanding of PeopleSoft financial data model • Experience in data lake architecture Apply Here For Remote Business Architect/ Portland, OR ( Remote),6-12 months contract roles, visit Remote Business Architect/ Portland, OR ( Remote),6-12 months contract Roles