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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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Understanding the concept of AI, ML, and DL

By CIO Applications| Friday, May 24, 2019
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FREMONT, CA: Based on multiple artificial intelligence (AI) surveys, 43 percent of respondents in the U.S., and 47 percent in the U.K. had no clue about AI. The ignorance is because the buyers or the decision-makers are unable to match the myriads of AI solutions that are already developed by the vendors. The clarity among the technologies and understanding their potential to foster growth in an organization is vital.

AI analyzes data and provides analytical results to the users. Machine learning (ML) examines and seeks a pattern in the data to gain further insights. Deep Learning goes further apart from analyzing data and data patterns; it also uses algorithms designed by data scientists that ask specific data related questions, thereby providing critical insights. Thus, for understanding, these technologies can also be considered as successive stages or layers of automation and analytics working on a common platform.

Here’s a business example of traffic planning to demonstrate the complexities of these layers.

AI Layer

An organization develops an AI application that informs the planners and traffic engineers over the congestion points in the locality. The information helps them to plan repairs and to build other infrastructure as per the need.

Machine Learning

The organization further develops its analytics that enables them to understand the data patterns. For example, the traffic at a certain point gets congested at a particular time interval or the traffic queues up at a place before an event. Such patterns allow traffic engineers to manage traffic at specific locations.

Deep Learning

Deep learning goes beyond analyzing data and data analytics. Data Scientists design complex algorithms that can address deeper insights such as—which areas will have the highest population growth in the next two years? or, which roads will need repairs in the next two years? By leveraging deep learning, better and long-term insights can be gained.

Aligning -t All Together

It’s essential to understand the capabilities of these technologies to understand the extent and possibilities underlying them. Comparing these technologies with business requirements enables better decision making. Thus, it is imperative for a company to have a clear and firm understanding of AI technologies.

Check out: Top Artificial Intelligence Companies in UK

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