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CIO Applications Weekly Brief

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Editor's Pick (1 - 4 of 8)
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Democratizing Machine Learning Algorithms for Integrated Data-Sharing

Democratizing Machine Learning Algorithms for Integrated Data-Sharing
Andre Mutovic, CIO, ION Media Networks

The Tango of AI and Big Data

The Tango of AI and Big Data
Monica Khurana, CTO/CIO in Financial Services, Guardian Life Insurance

Transforming the Art Museum in the 21st Century.

Transforming the Art Museum in the 21st Century.
Jane Alexander, CIO, Cleveland Museum of Art

Machine Learning: The Future from the Perspective of Model Building

Machine Learning: The Future from the Perspective of Model Building
Markus Levy, Director of Enabling Technology, NXP Semiconductors

The State-Of Machine Learning Adoption in the Enterprise

The State-Of Machine Learning Adoption in the Enterprise
Sangeeta Edwin, Vice President, Data, Analytics & Insights, Rockwell Automation

Inside the Latest in Machine Learning

Inside the Latest in Machine Learning
Troy Lau, Group Leader in Machine Intelligence, Draper

Recent Trends in Machine Learning

Recent Trends in Machine Learning
Sears Merritt, Head of Data, Strategy & Architecture, MassMutual

Balancing Accuracy With Interpretability in Machine Learning Models

Balancing Accuracy With Interpretability in Machine Learning Models
Joshua Mabry Expert in Advanced Analytics practice

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Use Cases of Machine Learning

By CIO Applications| Friday, January 08, 2021
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Chatbots can utilize a combination of natural language processing, pattern recognition, and deep neural networks to interpret input text and offer suitable responses.

Fremont, CA: Machine learning is a subdivision of artificial intelligence (AI) focused on building applications that are smart enough to learn from data and also improve their accuracy over time without being programmed to do so. 

In the realm of data science, an algorithm is nothing but a sequence of statistical processing steps. In machine learning, algorithms are purposed to find features and patterns in massive amounts of data to make decisions and predictions on the basis of new data. When the algorithm is better, the predictions and decisions will become more accurate as it processes more data.

Here are some of the use cases of machine learning:

• Digital assistants: Google Assistant, Apple Siri, Amazon Alexa, and other digital assistants are empowered by natural language processing (NLP), a machine learning application that allows computers to process text as well as voice data and 'understand' human language. Natural language processing also powers voice-driven applications like speech recognition (speech-to-text) software and GPS.

• Recommendations: Deep learning models enable 'people also liked' and 'just for you' recommendations offered by Netflix, Spotify, Amazon, and other entertainment, retail, job search, travel, and news services.

• Chatbots: Chatbots can utilize a combination of natural language processing, pattern recognition, and deep neural networks to interpret input text and offer suitable responses.

• Online Advertising: Deep learning and machine learning and models are capable of evaluating the content of a web page—the topic and nuances such as the author's attitude or opinion—and serve up advertisements customized to the visitor's interests.

• Detecting Fraud: Machine learning regression and classification models have overthrown rules-based fraud detection systems that have a high number of false positives when flagging stolen credit card utilization and are rarely successful at detecting criminal utilization of stolen or compromised financial data.

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