Generative AI Webinar Series

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January 24
Improving LLMs with Prompt Economization and In-Context
Wednesday, January 24
2024, 10:00am – 11:00am PST

On-Demand
Small and Nimble – the Fast Path to Enterprise GenAI

On-Demand
The Next Wave of GenAI: Domain-Specific LLMs

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Join our new GenAI webinar series to learn about the latest trends and best practices in generative AI from industry leaders and practitioners.

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On-Demand

The Next Wave of GenAI: Domain-Specific LLMs
The Next Wave of GenAI: Domain-Specific LLMs

To gain competitive advantage, innovative companies are starting to embed large language models into proprietary workflows that support domain-specific use cases. Many of them choose open-source LLMs to reduce data and compute requirements as well as privacy risks. The results have the potential to accelerate and enrich all sorts of business functions, from customer service to document processing and more. Join the discussion with AI leaders to understand how careful design, implementation, and governance will help you achieve success with generative AI.

Topics include:
- The requirements and architectural approaches to domain-specific LLMs
- Common challenges, benefits, and use cases
- Must-know guiding principles for successful generative AI initiatives
Hear from AI industry leaders:
- Kevin Petrie, VP of Research at Eckerson Group
- Ro Shah, AI Product Director at Intel's Data Center and AI Group
- Sancha Huang Norris (moderator), Generative AI Marketing Lead at Intel's Data Center and AI Business Unit
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Kevin Petrie

VP of Research at Eckerson Group

Ro Shah

AI Product Director at Intel's Data Center and AI Group

Sancha Huang Norris

Generative AI Marketing Lead at Intel's Data Center and AI Business Unit
On-Demand

Small and Nimble – the Fast Path to Enterprise GenAI
Small and Nimble – the Fast Path to Enterprise GenAI

The fast path to integrate the power of generative AI for your business is not necessarily general purpose, third-party giant models! Smaller LLM models, like those less than 20B parameters, can be a good or better match for your needs. Recent commercially available compact models, such as Llama 2, can address the key attributes that you need– performance, domain adaptation, private data integration, verifiability of results, security, flexibility, accuracy, and cost effectiveness. Join us as we evaluate the effectiveness of open source LLM models, discuss pros and cons, and share methods to build nimble models.

What you’ll learn about nimble models:
- Advantages and challenges
- The ecosystem-driven technology advancement of small, open models
- Performance compared with top-tier giant models
- Methods to build one and ways to assess its benefits and value 
- The full path from a nimble model to a fully adapted model in business
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Gadi Singer

Vice President and Director of Emergent AI Research at Intel Labs, leading the development of third-wave AI capabilities.

Moshe Berchansky

NLP Deep Learning Researcher at EAI Intel Labs, specializing in Retrieval-Augmented Generation techniques.

Sancha Huang Norris (moderator)

Generative AI Marketing Lead at Intel's Data Center and AI Business Unit
Jan 24

Improving LLMs with Prompt Economization and In-Context

Improving LLMs with Prompt Economization and In-Context

There are multiple ways to create a domain specific LLM. Prompt engineering is a method to guide the model to a better output, while RAG augments the model with more data. Neither methods will change the models but both will improve the output. Come learn about the pros and cons of each method to achieve your business project objectives.

Speaker: Eduardo Alvare

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