Allen Institute’s Semantic Scholar now searches across 175 million academic papers

Lynch spoke with industry analyst Dave Vellante during an interview on SiliconANGLE media’s livestreaming studio theCUBE. They discussed how AtScale delivers its semantic layer, how AI will supplement the product and how data will fit into the future. Genius delivers a structured, integrated and secure approach focused on generating value for the core of clients’ businesses across industries including finance, telecommunications, utilities and logistics.

Starting With Change Management

This allows you to provide data objects as training datasets composed of information from structured data and text. About SPECTRA Semantic StudioSPECTRA Semantic Studio is a cutting-edge platform that allows law enforcement agencies to train and deploy customized AI models tailored to their unique operational language and contextual needs. By focusing on semantic text analysis within secure, on-premises environments, SPECTRA Semantic Studio ensures precision, confidentiality, and enhanced investigative capabilities.

Clean up models now to avoid headaches later

Because machine learning cannot extract causalities, it cannot reflect on why certain rules are extracted. The information they get—specifically accurate, timely and consistent data—determines their effectiveness. AI systems cannot produce precise, reliable results without the kind of single source of truth that a universal semantic layer provides. Adopting a universal semantic layer carries many benefits in data accuracy and consistency—and in getting AI projects off the ground—but it requires change management. At the same time, the Reporting Tab inside Fabric will no longer include options such as ‘New Report,’ ‘Manage default semantic model,’ or ‘Automatically update semantic model,’ which were previously tied to these auto-generated models. Moor Insights and Strategy principal analyst Robert Kramer sees the move as a way for Microsoft to emphasize the importance of accountability in how teams build and manage data models, especially as companies scale up their AI and analytics initiatives.

Drawing from his years working with global businesses, he understands the critical need for solutions that provide visibility and control across supply networks. Semantic Visions addresses this by offering AI-powered tools that monitor risks and identify opportunities, even within often-overlooked lower tiers of the supply chain. The platform features a network of specialised intelligent tools that accelerate migration to modern environments and applications. Key capabilities include automated code analysis to identify obsolete code, business rule extraction to recover critical operational knowledge, automated test generation to ensure operational integrity and code refactoring to simplify complex code without altering its function. At the core of the problem, data scientists spend more than half of their time collecting and processing uncontrolled digital data before it can be explored for useful nuggets.

Between the data repository and the data-consuming endpoints like AI, businesses need to enrich their data with context and meaning, which can be done by using a universal semantic layer that sits between data sources and consumers. The models will be decoupled from their parent data assets and converted into standalone semantic models, which users will need to manage manually, Microsoft said. This change introduces an additional step for enterprise users, requiring manual creation of semantic models that potentially will slow down rapid prototyping workflows, he said. Semantic models are structured representations of data that that add meaning and context to the raw information held in Fabric.

It’s also obvious that data scientists build better models if they can compare their predictions to what actually happened. In other words, historical analysis and predictive analysis are relevant to both teams, but rarely do the two meet. Through a structured semantic analysis process, Legacy Modernization by Genius maps entire legacy ecosystems, identifies areas for improvement and delivers solutions aligned with business needs. This approach enhances accuracy in decision-making, reduces errors and increases the quality of outcomes. Each project generates updated digital information that serves as a foundation for future developments, transforming systemic knowledge into reusable assets while maintaining business logic and keeping data internal, updated, governed and secure. As global supply chains grow increasingly complex, innovative solutions are becoming essential for resilience and efficiency.

A semantic layer solution presents this consumer-friendly interface in the “language” of their tooling (SQL, MDX, DAX, JDBC, ODBC, REST, or Python), translating queries into the dialect of the underlying cloud platform. With a common set of business terms, both teams can interact with the same data, with the same governance rules, with the same results, using the tooling of their choice. Today, enterprises have strong and sometimes regulatory requirements to track “who” saw “which” data and “when.” A modern semantic layer allows users to appear as themselves to the underlying data platforms from any consumer tool. At the same time, a semantic layer ensures that data is consistent regardless of consumption style and makes sure everyone plays by the same (governance) rules. Semantic Visions aims to expand its reach and provide greater visibility into multi-tiered supply chains.

When creating Fabric assets such as warehouses, lakehouses, or SQL databases, users can make their own semantic model — or use an automatically generated Default Semantic Model. This starts with implementing the appropriate processes and tools to democratize data and empower individuals to utilize data through self-service analytics. When most people think of augmented intelligence, they think about specific features that may appear in AI-enhanced business intelligence tools. For example, some BI tools add natural language query (NLQ) or outlier analysis to help their users ask better questions or find the needle in the haystack. These are valuable features, but they are confined to the particular tool and may work differently across different tools. The semantic AI approach thus creates a continuous cycle of which both machine learning and knowledge scientists are an integral part.

• Between the data repository and the data-consuming endpoints like AI, businesses need to enrich their data with context and meaning, which can be done by using a universal semantic layer that sits between data sources and consumers.
• Combining advanced technology with human expertise, Genius provides a collaborative and intelligent environment that enables SQUADRA experts to transform complexity into simplicity.
• About SPECTRA Semantic StudioSPECTRA Semantic Studio is a cutting-edge platform that allows law enforcement agencies to train and deploy customized AI models tailored to their unique operational language and contextual needs.
• AFP is a leading global news agency providing fast, comprehensive and verified coverage of the events shaping our world and of the issues affecting our daily lives.
• Closing the gap between business intelligence and data science teams provides more visibility into the output of data science initiatives throughout the organization and enables organizations to leverage their data for predictive and prescriptive analytics.
• Augmented intelligence (also called augmented analytics or decision intelligence) brings AI-generated insights into traditional business intelligence workflows to improve data-driven decisions.

• But AI disillusionment is brewing, with many projects stuck in pilot versus getting to production.
• But knowledge graphs are also more and more identified as the building blocks of an AI strategy that enables explainable AI through the design principle called human-in-the-loop (HITL).
• Next, they employ defined metadata to develop the semantic or abstraction layer based on the business logic.

One of the biggest complaints from the business is that it takes way too long for IT to build or deliver reports for them. Users want to control their destiny, and subject-matter experts (not IT) are best suited to applying data to improve the business. A well-designed semantic layer hides the complexity of data’s physical form and location while translating data into understandable business constructs. A semantic layer frees business users and data scientists from the dependency on IT and data experts by making data easy to use. In the logistics sector, for example, SQUADRA used Genius to analyse a legacy train circulation management system. Semantic analysis helped identify key concepts, workflows and rules, allowing the team to safely migrate to a modern environment.

The platform can manage multiple projects simultaneously and integrate user profiles to create a conversational digital environment that accelerates digital ecosystem improvements. SQUADRA, a technology consultancy specialising in supporting companies in their Digital Transformation journeys, has announced the launch of Genius, a multipurpose platform driven by AI. Brazilian consultancy SQUADRA has launched Genius – a multipurpose AI-powered platform developed to accelerate legacy system modernisation and digital solution delivery.