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What Do We Mean by Machine Learning?

The goal of artificial intelligence is to create a machine that can mimic a human mind and to do that, of course, it needs learning capabilities. However, it is more than just about learning. It’s about reasoning, knowledge representation, and even things like abstract thinking. 

Machine learning, on the other hand, is solely focused on writing software that can learn from past experiences. One thing you might find astounding is that machine learning is more closely related to data mining and statistics than it is to AI. Why is that? First, we need to know what we mean by machine learning.

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Predicting Wine Quality With Several Classification Techniques

Introduction

As the quarantine continues, I’ve picked up a number of hobbies and interests…including WINE. Recently, I’ve acquired a taste for wines, although I don’t really know what makes a good wine. Therefore, I decided to apply some machine learning models to figure out what makes a good quality wine!

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Why Time Dimension Is Vital in IoT and How a Rules Engine Can Reduce Complexity

The time dimension increases the complexity of application development for software developers that are building logic with conditional statements (rules) that need to change over time.

“Time is an observed phenomenon, by means of which human beings sense and record changes in the environment and in the universe. Time has been called an illusion, a dimension, a smooth-flowing continuum, and an expression of separation among events that occur in the same physical location.” — whatis.techtarget.com

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The Future of Containers

To understand the current and future state of containers, we gathered insights from 33 IT executives who are actively using containers. We asked, "What’s the future for containers from your point of view? Where do the greatest opportunities lie?"

Here's what they told us:

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Five Predictions for The Next Decade of Software Delivery

Throughout this series of articles, I have been exploring the state of the practice in DevOps, summarizing recent trends in scaling software delivery. In this post – originally written for a special issue of IEEE Software to celebrate software engineering’s 50th anniversary – I look further ahead to consider how software engineering will evolve over the coming decades. My five predictions stretch far enough into the future that they aren’t intended to be precise; they aim to provide discussion topics for the shape of software engineering trends to come.

These predictions resulted from a celebration of the 50th anniversary of the University of British Columbia (UBC) Computer Science Department in May 2018. I participated in a panel of UBC alumni that discussed topics ranging from AI’s future impact to where computer science students should focus today’s studies to have the best job prospects.