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Generative AI: Eight fundamental security risks and considerations

CIO

As new technologies emerge, security measures often trail behind, requiring time to catch up. This is particularly true for Generative AI, which presents several inherent security challenges. Here are some of the key risks related to AI that organizations need to bear in mind. However, this shift introduces challenges.

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Reimagine application modernisation with the power of generative AI

CIO

As a consequence, these businesses experience increased operational costs and find it difficult to scale or integrate modern technologies. 1] Retaining outdated technology may seem like a cautious approach but there are mounting inherent dangers. The foundation of the solution is also important.

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Generative AI in healthcare: benefits and challenges according to CIOs

CIO

The implementation of these techniques in medical care will allow the transformation of the way we diagnose, in addition to personalizing treatments, helping to identify risk factors and, in general, improving the results and productivity of the health sector.

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System of intelligence — generative AI at the app layer

TechCrunch

As generative AI shapes the next generation of application products, we can expect even more sweeping evolution. © 2023 TechCrunch. All rights reserved. For personal use only.

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Maximizing Profit and Productivity: The New Era of AI-Powered Accounting

Speaker: Yohan Lobo and Dennis Street

Outdated processes and disconnected systems can hold your organization back, but the right technologies can help you streamline operations, boost productivity, and improve client delivery. From automation to generative AI, learn how to optimize workflows, reclaim valuable time, and attract top-tier talent with cutting-edge technology.

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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. Solution overview For this solution, you deploy a demo application that provides a clean and intuitive UI for interacting with a generative AI model, as illustrated in the following screenshot.

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Track, allocate, and manage your generative AI cost and usage with Amazon Bedrock

AWS Machine Learning - AI

As enterprises increasingly embrace generative AI , they face challenges in managing the associated costs. With demand for generative AI applications surging across projects and multiple lines of business, accurately allocating and tracking spend becomes more complex.

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Addressing Top Enterprise Challenges in Generative AI with DataRobot

The buzz around generative AI shows no sign of abating in the foreseeable future. Enterprise interest in the technology is high, and the market is expected to gain momentum as organizations move from prototypes to actual project deployments.

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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.

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Improving the Accuracy of Generative AI Systems: A Structured Approach

Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

When developing a Gen AI application, one of the most significant challenges is improving accuracy. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.

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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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A Tale of Two Case Studies: Using LLMs in Production

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

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

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Launching LLM-Based Products: From Concept to Cash in 90 Days

Speaker: Christophe Louvion, Chief Product & Technology Officer of NRC Health and Tony Karrer, CTO at Aggregage

Christophe Louvion, Chief Product & Technology Officer of NRC Health, is here to take us through how he guided his company's recent experience of getting from concept to launch and sales of products within 90 days.

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How to Achieve High-Accuracy Results When Using LLMs

Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage

When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale.

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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

Putting the right LLMOps process in place today will pay dividends tomorrow, enabling you to leverage the part of AI that constitutes your IP – your data – to build a defensible AI strategy for the future. Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications.