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20 issues shaping generative AI strategies today

CIO

Organizations are rushing to figure out how to extract business value from generative AI — without falling prey to the myriad pitfalls arising. They note, too, that CIOs — being top technologists within their organizations — will be running point on those concerns as companies establish their gen AI strategies.

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Quality Assurance, Errors, and AI

O'Reilly Media - Ideas

Generative AI will be used to create more and more software; AI makes mistakes and it’s difficult to foresee a future in which it doesn’t; therefore, if we want software that works, Quality Assurance teams will rise in importance. Security is yet another issue: is an AI system able to red-team an application?

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning - AI

Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. This post provides three guided steps to architect risk management strategies while developing generative AI applications using LLMs.

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The early returns on gen AI for software development

CIO

Generative AI is already having an impact on multiple areas of IT, most notably in software development. Early use cases include code generation and documentation, test case generation and test automation, as well as code optimization and refactoring, among others.

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How to manage data integration during an acquisition

CIO

IT teams hold a lot of innovation power, as effective use of emerging technologies is crucial for informed decision-making and is key to staying a beat ahead of the competition. Cloud-based analytics, generative AI, predictive analytics, and more innovative technologies will fall flat if not run on real-time, representative data sets.

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Future AI Trends in Software Development

Invid Group

Current AI Trends in Software Development AI is already pervasive in many facets of software development today. 2. Bug Detection and Debugging: AI-driven systems are improving their ability to find and address bugs and security holes in code, increasing software’s dependability and security.

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AI and Software Development: The Formula for Success

Perficient

Code Llama specialization pipeline (Code Llama: Open Foundation Models for Code) Automated Code Generation One of the most prominent applications of AI in software development is automated code generation.