O'Reilly on Data

JANUARY 29, 2019

In 2017, we published “ How Companies Are Putting AI to Work Through Deep Learning ,” a report based on a survey we ran aiming to help leaders better understand how organizations are applying AI through deep learning. We found companies were planning to use deep learning over the next 12-18 months.

Deep Learning 213

Deep Learning Machine Learning Data Science Metadata 213

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. Data integration and cleaning.

Machine Learning 266

Machine Learning Data Quality Statistics Modeling 266

Ontotext

NOVEMBER 6, 2019

GraphDB Workbench is the interface for Ontotext’s semantic graph database, which provides the core infrastructure including modelling agility, data integration, relationship exploration and cross-enterprise semantic data publishing and consumption. The Plugins.

Experimentation 45

Experimentation Deep Learning Software Modeling 45

O'Reilly on Data

MARCH 20, 2019

Watermarking is a term borrowed from the deep learning security literature that often refers to putting special pixels into an image to trigger a desired outcome from your model. It seems entirely possible to do the same with customer or transactional data. Watermark attacks. Disparate impact analysis: see section 1.

Modeling 228

Modeling Machine Learning Predictive Modeling Consulting 228

Smarten

APRIL 19, 2024

The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. Nothing…and I DO mean NOTHING…is more prominent in technology buzz today than Artificial Intelligence (AI). billion, with the market growing by 31.1%

Analytics 59

Analytics Predictive Modeling KPI Machine Learning 59

Domino Data Lab

APRIL 3, 2019

The longer answer is that in the context of machine learning use cases, strong assumptions about data integrity lead to brittle solutions overall. We keep feeding the monster data. We find ways to improve machine learning so that it requires orders of magnitude more data, e.g., deep learning with neural networks.

Data Governance 84

Data Governance Machine Learning Metadata Data Science 84

Smart Data Collective

OCTOBER 13, 2021

As we have already said, the challenge for companies is to extract value from data, and to do so it is necessary to have the best visualization tools. Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields).

Visualization 131

Visualization Cost-Benefit Big Data Prescriptive Analytics 131