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REPLY: MLFRAME REPLY Integrates a New Approach to Analysis and Modelling of Knowledge Bases to Create and Specialise Conversational Models

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During the annual Reply Xchange event, dedicated to innovation and new technologies, Reply introduced the latest release of MLFRAME Reply, a generative artificial intelligence framework for managing heterogeneous knowledge bases. The new version incorporates a novel approach to analysing and modelling the knowledge bases used to create and specialise generative AI-based conversational models.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20240517546613/en/

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MLFRAME Reply Integrates a New Approach to Analysis and Modelling of Knowledge Bases to Create and Specialise Conversational Models (Photo: Reply)

This innovative knowledge management approach allows the development of more advanced conversational models, capable of sustaining intricate conversations and recognising relationships between similar concepts in the knowledge base, without the need for specific training on these connections.

Furthermore, applying MLFRAME Reply to knowledge base modelling enables the rapid conceptual representation of a specific knowledge domain, significantly improving the organisation and analysis of large volumes of heterogeneous and often unintelligible data. The use of graph models not only allows for the definition of information structure by highlighting main nodes and relationships, making the analysis more effective, but also automates the mapping of key topics, reducing the need for manual interventions in data cleaning and review for the training of algorithms underpinning the conversational models.

MLFRAME Reply, conceptualised and developed by Machine Learning Reply—specialised in artificial intelligence services and solutions—employs a proprietary methodology on leading AI technologies for database analysis, algorithm training, and result validation, to quickly create generative conversational models applicable to specific business knowledge domains. Thanks to MLFRAME Reply, it is therefore possible to activate the “artificial intelligence” component foundational to the new generation of "human-like" interaction systems, such as digital assistants or digital humans.

With its latest features, MLFRAME Reply provides even more comprehensive support throughout all phases of the development and training of conversational systems: from creating a robust knowledge base within a knowledge domain, through the introduction of models, to the training and subsequent optimisation of algorithms using the most suitable techniques for each case's complexity.

For more information about MLFRAME Reply and Reply's generative AI-based solutions, please visit:

www.ai.reply.com

Reply
Reply [EXM, STAR: REY] specialises in the design and implementation of solutions based on new communication channels and digital media. Reply is a network of highly specialised companies supporting key European industrial groups operating in the telecom and media, industry and services, banking, insurance and public administration sectors in the definition and development of business models enabled for the new paradigms of big data, cloud computing, digital media and the Internet of Things. Reply services include: Consulting, System Integration and Digital Services. www.reply.com

Machine Learning Reply
Machine Learning Reply is the Reply Group company specialised in providing artificial intelligence services and solutions to guide its customers towards digitisation, helping them to become more competitive and data-driven through Smart Analytics, Machine Learning and Artificial Intelligence. With expertise in deep learning, machine vision, NLP and predictive modelling, the company helps its customers to empower their business by providing them with highly experienced dedicated development teams. www.machine-learning.reply.it

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Contact information

Media Contact:

Reply
Fabio Zappelli
f.zappelli@reply.com
Tel. +390117711594

Irene Caia
i.caia@reply.com
Tel. +39 02 535761

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