Remove Artificial Inteligence Remove Data Remove Generative AI
article thumbnail

How the world can tackle the power demands of artificial intelligence

CIO

The world must reshape its technology infrastructure to ensure artificial intelligence makes good on its potential as a transformative moment in digital innovation. New technologies, such as generative AI, need huge amounts of processing power that will put electricity grids under tremendous stress and raise sustainability questions.

article thumbnail

LLM benchmarking: How to find the right AI model

CIO

But how do companies decide which large language model (LLM) is right for them? But beneath the glossy surface of advertising promises lurks the crucial question: Which of these technologies really delivers what it promises and which ones are more likely to cause AI projects to falter?

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

The key to operational AI: Modern data architecture

CIO

From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. However, there’s a significant difference between those experimenting with AI and those fully integrating it into their operations.

article thumbnail

Dulling the impact of AI-fueled cyber threats with AI

CIO

IT leaders are placing faith in AI. Consider 76 percent of IT leaders believe that generative AI (GenAI) will significantly impact their organizations, with 76 percent increasing their budgets to pursue AI. But when it comes to cybersecurity, AI has become a double-edged sword.

article thumbnail

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage

Executive leaders and board members are pushing their teams to adopt Generative AI to gain a competitive edge, save money, and otherwise take advantage of the promise of this new era of artificial intelligence.

article thumbnail

Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

The emergence of generative AI has ushered in a new era of possibilities, enabling the creation of human-like text, images, code, and more. However, as exciting as these advancements are, data scientists often face challenges when it comes to developing UIs and to prototyping and interacting with their business users.

article thumbnail

Reimagine application modernisation with the power of generative AI

CIO

According to research from NTT DATA , 90% of organisations acknowledge that outdated infrastructure severely curtails their capacity to integrate cutting-edge technologies, including GenAI, negatively impacts their business agility, and limits their ability to innovate. [1] The foundation of the solution is also important.

article thumbnail

LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.

article thumbnail

LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.