article thumbnail

The key to operational AI: Modern data architecture

CIO Business Intelligence

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

article thumbnail

Thinking Machines At Work: How Generative AI Models Are Redefining Business Intelligence

Smart Data Collective

Ryan Kh 3 Min Read Microsoft Stock Images SHARE Generative AI is no longer confined to research labs or experimental design tools. From automated content creation to synthetic forecasting, the range of applications continues to expand, each powered by large-scale data processing and deep learning frameworks.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Generative AI: A Self-Study Roadmap

KDnuggets

Regular hands-on experimentation helps you understand new capabilities and identify practical applications. Contributing to Open Source : Contributing to generative AI open-source projects provides deep learning opportunities while building professional reputation.

article thumbnail

Synthetic data’s fine line between reward and disaster

CIO Business Intelligence

It can even be used for controlled experimentation, assuming you can make it accurate enough. Early approaches like rule-based generation or SMOTE required minimal computational resources, while modern deep learning approaches like GANs demand substantial GPU capacity, Vawdrey says.

article thumbnail

Your data’s wasted without predictive AI. Here’s how to fix that

CIO Business Intelligence

These capabilities are no longer theoretical or experimental. While the algorithms can vary in complexity, from logistic regression to deep learning, the value lies in what they help us anticipate and prevent. They are live, operational and transforming how companies plan, act and serve their customers.

article thumbnail

Accelerate Neural Network Training Using the Net2Net Method

Analytics Vidhya

Introduction Creating new neural network architectures can be quite time-consuming, especially in real-world workflows where numerous models are trained during the experimentation and design phase. In addition to being wasteful, the traditional method of training every new model from scratch slows down the entire design process.

article thumbnail

AI adoption in the enterprise 2020

O'Reilly on Data

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. It seems as if the experimental AI projects of 2019 have borne fruit. Supervised learning is dominant, deep learning continues to rise.