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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. Chief Marketing Officer, recently engaged in an extensive discussion on exactly how photonics technology could help meet the power demands of AI.

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Artificial Intelligence in practice

CIO

The world has known the term artificial intelligence for decades. Until recently, discussion of this technology was prospective; experts merely developed theories about what AI might be able to do in the future. Today, integrating AI into your workflow isn’t hypothetical, it’s MANDATORY.

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An example of LLM prompting for programming

Martin Fowler

A couple of weeks ago I watched a fascinating Zoom call hosted by Xu Hao , Thoughtworks's Head of Technology in China. His initial prompt primes the LLM with an implementation strategy (chain of thought prompting). He showed an example of how he uses ChatGPT to help him code in a self-testing style.

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Amazon turns to AI to help customers find clothes that fit when shopping online

TechCrunch

After recently turning to generative AI to enhance its product reviews, e-commerce giant Amazon today shared how it’s now using AI technology to help customers shop for apparel online.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations.

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Social services provider uses artificial intelligence to provide genuine help

CIO

However, IT users depended on difficult-to-support legacy systems, with member data spread over different technologies and each specialty unit often partial to a separate solution. When Colsubsidio evaluated the situation, managers realized that recent technological innovations could quickly solve the dilemma.

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How AI orchestration has become more important than the models themselves

CIO

Large language models (LLMs) just keep getting better. In just about two years since OpenAI jolted the news cycle with the introduction of ChatGPT, weve already seen the launch and subsequent upgrades of dozens of competing models. From Llama3.1 to Gemini to Claude3.5 In fact, business spending on AI rose to $13.8

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MLOps 101: The Foundation for Your AI Strategy

Many organizations are dipping their toes into machine learning and artificial intelligence (AI). Download this comprehensive guide to learn: What is MLOps? How can MLOps tools deliver trusted, scalable, and secure infrastructure for machine learning projects? Why do AI-driven organizations need it?

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5 Things a Data Scientist Can Do to Stay Current

And more is being asked of data scientists as companies look to implement artificial intelligence (AI) and machine learning technologies into key operations. Fostering collaboration between DevOps and machine learning operations (MLOps) teams. Collecting and accessing data from outside sources.

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Trusted AI 102: A Guide to Building Fair and Unbiased AI Systems

The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. Numerous high-profile examples demonstrate the reality that AI is not a default “neutral” technology and can come to reflect or exacerbate bias encoded in human data.

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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.

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Data Science Fails: Building AI You Can Trust

The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology.

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A Tale of Two Case Studies: Using LLMs in Production

Speaker: Tony Karrer, Ryan Barker, Grant Wiles, Zach Asman, & Mark Pace

Join our exclusive webinar with top industry visionaries, where we'll explore the latest innovations in Artificial Intelligence and the incredible potential of LLMs. We'll walk through two compelling case studies that showcase how AI is reimagining industries and revolutionizing the way we interact with technology.

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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.

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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.