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The early returns on gen AI for software development

CIO Business Intelligence

Generative AI is already having an impact on multiple areas of IT, most notably in software development. Still, gen AI for software development is in the nascent stages, so technology leaders and software teams can expect to encounter bumps in the road.

Software 125
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IT leaders look beyond LLMs for gen AI needs

CIO Business Intelligence

Along with code-generating copilots and text-to-image generators, which leverage a combination of LLMs and diffusion processing, LLMs are at the core of most generative AI experimentation in business today. And the benefits of MakeShift’s use of AI are beginning to multiply.

IT 119
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What LinkedIn learned leveraging LLMs for its billion users

CIO Business Intelligence

Fits and starts As most CIOs have experienced, embracing emerging technologies comes with its share of experimentation and setbacks. For LinkedIn, this was no different, as its road to LLM insights was anything but smooth, said LinkedIn’s Juan Bottaro, a principal software engineer and tech lead. Not enough dots were being connected.”

IT 131
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Your New Cloud for AI May Be Inside a Colo

CIO Business Intelligence

Enterprises moving their artificial intelligence projects into full scale development are discovering escalating costs based on initial infrastructure choices. Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex.

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Amazing Data-Driven Software Applications Revolutionizing Inventory Optimization

Smart Data Collective

This is the best inventory optimization software for 2021, according to the latest research updated in December 2020 by Business.org. You can find ten of the best software applications that use machine learning, AI and other big data technology to facilitate inventory management processes. Core $59, Pro $199, and Pro-Plus $359.

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Expectations vs. reality: A real-world check on generative AI

CIO Business Intelligence

Pilots can offer value beyond just experimentation, of course. McKinsey reports that industrial design teams using LLM-powered summaries of user research and AI-generated images for ideation and experimentation sometimes see a reduction upward of 70% in product development cycle times.

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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 361