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Responsible AI can revolutionize tax agencies to improve citizen services

IBM Big Data Hub

The new era of generative AI has spurred the exploration of AI use cases to enhance productivity, improve customer service, increase efficiency and scale IT modernization. Generative AI can revolutionize tax administration and drive toward a more personalized and ethical future.

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AI transforms the IT support experience

IBM Big Data Hub

Enterprises demand prompt and accurate solutions to their technical issues, requiring support teams to possess deep technical knowledge and communicate action plans clearly. About 85% of execs say generative AI will be interacting directly with customers in the next two years.

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Redefining clinical trials: Adopting AI for speed, volume and diversity

IBM Big Data Hub

Learning from historical protocol data and using synthetically generated scenario events to optimize inclusion and exclusion criteria can be powerful for achieving efficient trial design. Additionally, generative adversarial networks (GANs) can be used to simulate real recruitment scenarios, further optimizing protocol design.

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Application performance optimization: Elevate performance and reduce costs

IBM Big Data Hub

This approach paves the way for substantial savings, aligning resource allocation precisely with the demands of the application. Continuous application performance Traditional monitoring tools fall short in the face of dynamic workloads, unable to keep pace with the evolving demands of applications.

Cloud 65
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Securing AI Deployments: Striking the Balance

OpenText Information Management

The launch of OpenText™ Aviator brings the power of large language models (LLMs) and generative AI (GenAI) to information management users. If PII is part of the model’s training dataset, it may generate responses to users, potentially leading to data breaches, privacy compromises, and violations of compliance regulations.

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Conversational AI use cases for enterprises

IBM Big Data Hub

Natural language generation (NLG) complements this by enabling AI to generate human-like responses. ML algorithms understand language in the NLU subprocesses and generate human language within the NLG subprocesses. DL, a subset of ML, excels at understanding context and generating human-like responses.

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Event-driven architecture (EDA) enables a business to become more aware of everything that’s happening, as it’s happening 

IBM Big Data Hub

The importance of events is elevated to be on par with application programming interface (API), with facilities to describe, advertise and discover events. Event triggers are used to automate workflows or decisions, allowing businesses to generate notifications so appropriate actions can be taken as swiftly as situations are detected.

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