CIO Business Intelligence

SEPTEMBER 19, 2023

AI personalization utilizes data, customer engagement, deep learning, natural language processing, machine learning, and more to curate highly tailored experiences to end-users and customers. AI can also be integrated into products to better ensure their safety and the safety of the people who use them.

Enterprise 141

Enterprise Cost-Benefit Optimization Deep Learning 141

Smart Data Collective

MAY 8, 2022

Let’s not forget that big data and AI can also automate about 80% of the physical work required from human beings, 70% of the data processing, and more than 60% of the data collection tasks. From the statistics shown, this means that both AI and big data have the potential to affect how we work in the workplace.

Big Data 122

Big Data Data Analytics Analytics Cost-Benefit 122

Smart Data Collective

APRIL 25, 2022

Instead, machine learning algorithms can be much more effective when tracking online reviews. Artificial intelligence can use deep learning as a means to create automatically generate content for their social media page. How Artificial Intelligence Can Help with Managing Online Profiles. The Creation of Content on Social Media.

Management 144

Management Machine Learning Marketing Deep Learning 144

IBM Big Data Hub

NOVEMBER 14, 2023

A value exchange system built on data products can drive business growth for your organization and gain competitive advantage. This growth could be internal cost effectiveness, stronger risk compliance, increasing the economic value of a partner ecosystem, or through new revenue streams.

Strategy 103

Strategy Data-driven Cost-Benefit Measurement 103

IBM Big Data Hub

MAY 17, 2023

They use drones for tasks as simple as aerial photography or as complex as sophisticated data collection and processing. It can offer data on demand to different business units within an organization, with the help of various sensors and payloads. The global commercial drone market is projected to grow from USD 8.15

Data Collection 76

Data Collection Visualization Data-driven Unstructured Data 76

DataRobot

OCTOBER 20, 2021

Because of the multidisciplinary nature of AI products, stakeholders across an entire organization must share a common understanding of each project’s scope, deployment, governance , impact, and projected risk. Achieving that level of governance at scale requires a common understanding of AI and data concepts. WHITE PAPER.

Machine Learning 52

Machine Learning Metrics Deep Learning Risk 52

IBM Big Data Hub

SEPTEMBER 6, 2023

From customized content creation to task automation and data analysis, AI has seemingly endless applications when it comes to marketing, but also some potential risks. They can also reduce the likelihood of human error, deliver more personalized customer messages and identify at-risk customers. What is AI marketing?

Marketing 78

Marketing Technology Advertising Machine Learning 78

IBM Big Data Hub

APRIL 22, 2024

The US City of Atlanta , for example, uses IBM data collection, machine learning and AI to monitor public transit tunnel ventilation systems and predict potential failures that could put passengers at risk. This will help advance progress by optimizing resources used.

IoT 77

IoT Internet of Things Cost-Benefit Reporting 77

IBM Big Data Hub

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning.

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

IBM Big Data Hub

AUGUST 28, 2023

Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g., The data collection process should be tailored to the specific objectives of the analysis.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

Domino Data Lab

MARCH 3, 2019

Then, when we received 11,400 responses, the next step became obvious to a duo of data scientists on the receiving end of that data collection. Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. One-fifth use reinforcement learning.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

JUNE 23, 2019

It used deep learning to build an automated question answering system and a knowledge base based on that information. It is like the Google knowledge graph with all those smart, intelligent cards and the ability to create your own cards out of your own data.

Management 110

Management Machine Learning Experimentation Metrics 110

O'Reilly on Data

JULY 28, 2020

This isn’t always simple, since it doesn’t just take into account technical risk; it also has to account for social risk and reputational damage. A product needs to balance the investment of resources against the risks of moving forward without a full understanding of the data landscape.

Marketing 364

Marketing Experimentation Metrics Testing 364

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Machine learning model interpretability. . – back to the structure of the dataset.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

CIO Business Intelligence

MARCH 30, 2022

But that takes a deep understanding of the decision-making process, the risks and rewards of each decision, the acceptable margin of error, and the ability to figure how confident you should be in any decision offered by your automated decision processes. A certain amount of learning is always business as usual.

Machine Learning 144

Machine Learning Testing Risk Data Collection 144

Domino Data Lab

OCTOBER 9, 2019

We’ll examine National Oceanic and Atmospheric Administration (NOAA) data management practices which I learned about at their workshop, as a case study in how to handle data collection, dataset stewardship, quality control, analytics, and accountability when the stakes are especially high. How cool is that?!

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

Domino Data Lab

JULY 22, 2019

We’ve got this complex landscape, tons of data sharing, an economy of data, external data, tons of mobile devices. and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries. You can take TensorFlow.js

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

IBM Big Data Hub

FEBRUARY 14, 2024

They can streamline workflows to increase efficiency and reduce time-consuming tasks and the risk of error in production, support, procurement and other areas. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

Cost-Benefit 82

Cost-Benefit Insurance Machine Learning Unstructured Data 82