AI and ML
Enterprise GenAI: The Time to Focus on High-ROI Use Cases is Now
In the relentless pursuit of digital transformation, enterprises are constantly seeking innovative avenues to maintain a competitive edge. Generative Artificial Intelligence (GenAI) stands out as one of the most promising frontiers in this quest. Unlike traditional AI, which primarily focuses on data analysis and interpretation, GenAI has the unique ability to generate new, original content, ideas, and solutions, making it an indispensable tool for businesses across various sectors.
ML - federated learning - Application in life insurance industry
In the healthcare industry where medical insurance providers are competing with each other to acquire more and more customers, evaluating customers' application to assign a risk level is of prime importance. This helps in formulating the policies and the premium that a customer needs to pay. In order to work on this the insurance companies must share their data which is highly susceptible of being stolen and misused against them by their corporate rivals.
Future of Applications with 5G and Cloud
This paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for 5G and B5G networks.
Leveraging the Power of AI/ML in 5G & Beyond 5G (B5G) Networks
This paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for 5G and B5G networks.
Global Practices | Big Data & Analytics Practice | Data Fabric Primer
The intended audience of this document is Architects that are looking to understand practical approaches to notable Big Data & Analytics Architecture-level aspects.
Explainable Voice & Conversational AI: Making Voicebots and Chatbots great again!
Everybody is talking nowadays about chatbots and Conversational AI, especially with the latest ChatGPT3 hype!
Data Quality Solutions for Stream and Batch Data Processing
Learn how data quality can be applied to both stream and batch data processing scenarios.
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