Beyond the gen AI hype: Google Cloud shares key learnings

by | Jul 10, 2024 | Technology

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Is bigger always better when it comes to large language models (LLMs)? 

“Well, the answer is quite simply yes and no,” Yasmeen Ahmad, managing director of strategy and outbound product management for data, analytics and AI at Google Cloud, said onstage at VB Transform this week. 

LLMs do get better with size — but not indefinitely, she pointed out. Huge models with a large number of parameters can be outperformed by smaller models trained on domain and context-specific information. 

“That indicates that data is at the cornerstone, with domain-specific industry information giving models power,” said Ahmad. 

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This allows enterprises to be more creative, efficient and inclusive, she said. They can tap into data that they’ve never been able to access before, “truly reach” all corners of their organization and enable their people to engage in all new ways. 

“Gen AI is pushing the boundaries of what we could even dream machines could create, or humans could imagine,” said Ahmad. “It truly is blurring the lines of technology and magic — perhaps even redefining what magic means.”

Enterprises need a new AI foundation

Successfully training models on a specific enterprise domain comes down to two specific techniques: fine-tuning and retrieval augmented generation (RAG), said Ahmad. Fine-tuning teaches LLMs “the language of your business,” while RAG allows the model to have a real-time connection to data, whether in documents, databases or elsewhere. 

“That means in real-time, it can provide accurate answers which are really important for financial analytics, risk analytics and other applications,” said Ahmad. 

Similarly, the true power of LLMs is in their multimodal capabilities, or their ability to operate on video, image, text documents and all other types of data. This is critical, she no …

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We want to hear from you! Take our quick AI survey and share your insights on the current state of AI, how you’re implementing it, and what you expect to see in the future. Learn More

Is bigger always better when it comes to large language models (LLMs)? 

“Well, the answer is quite simply yes and no,” Yasmeen Ahmad, managing director of strategy and outbound product management for data, analytics and AI at Google Cloud, said onstage at VB Transform this week. 

LLMs do get better with size — but not indefinitely, she pointed out. Huge models with a large number of parameters can be outperformed by smaller models trained on domain and context-specific information. 

“That indicates that data is at the cornerstone, with domain-specific industry information giving models power,” said Ahmad. 

Register to access VB Transform On-Demand

In-person passes for VB Transform 2024 are now sold out! Don’t miss out—register now for exclusive on-demand access available after the conference. Learn More

This allows enterprises to be more creative, efficient and inclusive, she said. They can tap into data that they’ve never been able to access before, “truly reach” all corners of their organization and enable their people to engage in all new ways. 

“Gen AI is pushing the boundaries of what we could even dream machines could create, or humans could imagine,” said Ahmad. “It truly is blurring the lines of technology and magic — perhaps even redefining what magic means.”

Enterprises need a new AI foundation

Successfully training models on a specific enterprise domain comes down to two specific techniques: fine-tuning and retrieval augmented generation (RAG), said Ahmad. Fine-tuning teaches LLMs “the language of your business,” while RAG allows the model to have a real-time connection to data, whether in documents, databases or elsewhere. 

“That means in real-time, it can provide accurate answers which are really important for financial analytics, risk analytics and other applications,” said Ahmad. 

Similarly, the true power of LLMs is in their multimodal capabilities, or their ability to operate on video, image, text documents and all other types of data. This is critical, she no …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]

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