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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 129
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

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Google Introduces NotebookLM: Your Personalized Virtual Research Assistant

Analytics Vidhya

Google is unveiling its latest experimental offering from Google Labs: NotebookLM, previously known as Project Tailwind. This innovative notetaking software aims to revolutionize how we synthesize information by leveraging the power of language models.

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Lessons from the field: How Generative AI is shaping software development in 2023

CIO Business Intelligence

Specifically, organizations are contemplating Generative AI’s impact on software development. While the potential of Generative AI in software development is exciting, there are still risks and guardrails that need to be considered. Generative AI has forced organizations to rethink how they work and what can and should be adjusted.

Software 117
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AI Technology Leads to Innovative Photo Editing Software

Smart Data Collective

The market for AI software is booming. Last summer, we wrote an article about the ways that artificial intelligence is changing video editing software. However, AI technology is arguably even more important for photo editing software. However, AI technology is arguably even more important for photo editing software.

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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 362
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Your New Cloud for AI May Be Inside a Colo

CIO Business Intelligence

The cloud is great for experimentation when data sets are smaller and model complexity is light. Often the burden of platform development can fall on data science and developer teams who know what they need for their projects, but whose skills are better served focusing on experimentation with algorithms instead of systems development.