Amdocs and NVIDIA to Accelerate Adoption of Generative AI for $1.7 Trillion Telecom Industry
Amdocs and NVIDIA today announced they are collaborating to optimize large language models (LLMs) to speed adoption of generative AI applications and services across the $1.7 trillion telecommunications and media industries.(1)
Amdocs and NVIDIA will customize enterprise-grade LLMs running on NVIDIA accelerated computing as part of the Amdocs amAIz framework. The collaboration will empower communications service providers to efficiently deploy generative AI use cases across their businesses, from customer experiences to network provisioning.
Amdocs will use NVIDIA DGX Cloud AI supercomputing and NVIDIA AI Enterprise software to support flexible adoption strategies and help ensure service providers can simply and safely use generative AI applications.
Aligned with the Amdocs strategy of advancing generative AI use cases across the industry, the collaboration with NVIDIA builds on the previously announced Amdocs-Microsoft partnership. Service providers and media companies can adopt these applications in secure and trusted environments, including on premises and in the cloud.
With these new capabilities — including the NVIDIA NeMo framework for custom LLM development and guardrail features — service providers can benefit from enhanced performance, optimized resource utilization and flexible scalability to support emerging and future needs.
“NVIDIA and Amdocs are partnering to bring a unique platform and unmatched value proposition to customers,” said Shuky Sheffer, Amdocs Management Limited president and CEO. “By combining NVIDIA’s cutting-edge AI infrastructure, software and ecosystem and Amdocs’ industry-first amAlz AI framework, we believe that we have an unmatched offering that is both future-ready and value-additive for our customers.”
“Across a broad range of industries, enterprises are looking for the fastest, safest path to apply generative AI to boost productivity,” said Jensen Huang, founder and CEO of NVIDIA. “Our collaboration with Amdocs will help telco service providers automate personalized assistants, service ticket routing and other use cases for their billions of customers, and help the telcos analyze and optimize their operations.”
Amdocs counts more than 350 of the world’s leading telecom and media companies as customers, including 27 of the world’s top 30 service providers.(2) With more than 1.7 billion daily digital journeys, Amdocs platforms impact more than 3 billion people around the world.
NVIDIA and Amdocs are exploring a number of generative AI use cases to simplify and improve operations by providing secure, cost-effective and high-performance generative AI capabilities.
Initial use cases span customer care, including accelerating customer inquiry resolution by drawing information from across company data. On the network operations side, the companies are exploring how to proactively generate solutions that aid configuration, coverage or performance issues as they arise.
(1) Source: IDC, OMDIA, Factset analyses of Telecom 2022-2023 revenue.
(2) Source: OMDIA 2022 revenue estimates, excludes China.
Editor’s Note:
- Language models: These models, like OpenAI’s GPT-3, generate human-like text. One of the most popular examples of language-based generative models are called large language models (LLMs).
- Large language models are being leveraged for a wide variety of tasks, including essay generation, code development, translation, and even understanding genetic sequences.
- Generative adversarial networks (GANs): These models use two neural networks, a generator, and a discriminator.
- Unimodal models: These models only accept one data input format.
- Multimodal models: These models accept multiple types of inputs and prompts. For example, GPT-4 can accept both text and images as inputs.
- Variational autoencoders (VAEs): These deep learning architectures are frequently used to build generative AI models.
- Foundation models: These models generate output from one or more inputs (prompts) in the form of human language instructions.
https://www.nvidia.com/en-us/glossary/data-science/generative-ai/
https://blogs.nvidia.com/blog/2023/01/26/what-are-large-language-models-used-for/
Cloud Service Providers struggle with Generative AI; Users face vendor lock-in; “The hype is here, the revenue is not”
Global Telco AI Alliance to progress generative AI for telcos
Bain & Co, McKinsey & Co, AWS suggest how telcos can use and adapt Generative AI
Generative AI Unicorns Rule the Startup Roost; OpenAI in the Spotlight
Generative AI in telecom; ChatGPT as a manager? ChatGPT vs Google Search
Generative AI could put telecom jobs in jeopardy; compelling AI in telecom use cases
In a blog post Wednesday, Nvidia said its artificial intelligence platform has raised the bar for AI training and high-performance computing in the latest MLPerf industry benchmarks. The company also put particular focus on Nvidia Eos, an AI supercomputer powered by a whopping 10,752 Nvidia H100 Tensor Core graphics processing chips and Nvidia Quantum-2 InfiniBand networking.
Emphasizing the rapidly improving speed of the testing, the company said the Nvidia Eos completed a training benchmark nearly three times faster than the record Nvidia set when the test was introduced less than six months ago.
Earlier in the week, Nvidia and Amdocs also announced they are collaborating to optimize large language models to speed adoption of generative AI applications and services across the $1.7 trillion telecommunications and media industries.
With roughly 30,000 employees around the world, Amdocs (DOX) helps communications and media clients migrate to the cloud and drive the 5G era. Further, the Nvidia-Amdocs collaboration aims to empower communications service providers to efficiently deploy generative AI use cases across their businesses.
https://www.investors.com/research/ibd-stock-analysis/nvidia-stock-unleashes-barrage-of-ai-enhancements-storms-new-buy-point/