Rootstrap Blog

The Explainability Dilemma

Healthcare AI explainability might be the most important dilemma of this century. This article explains why it will define medical outcomes for future generations.

Today’s AI algorithms provide medical recommendations by analyzing big data, but they can’t always give a reason for their conclusions other than the patterns they detect. Even though these AI-recommended solutions can’t be explained in terms of human understanding, many such treatments might improve the quality of patients’ lives and even save lives. This article discusses the controversial topic of medical explainability from a viewpoint that supports applying technological advancements to healthcare.

It’s no secret that the AI revolution has begun. I’m not the only one who believes that AI is making significant changes to our world. These quotes from some of the best-known leaders in science and technology point in the same direction

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Payment platforms for mobile apps

It’s common these days to see apps that offer to sell you in-app content on your mobile device. Sometimes they only sell virtual things like coins, diamonds, and credits that give users access to certain functionalities. But on a lot of e-commerce apps, like Amazon, you can buy physical goods. Both types of apps have platforms that manage payments. And there are many payment platform options to choose from. This article lists some of them, along with their pros and cons.

You can’t always use the payment platform you want. You might want to sell virtual or digital goods on your app like coins and credits, but you don’t provide another way to do that like a web page. In this case, you must use in-app purchases to be approved by Apple. But if you run an e-commerce page where you sell solid goods like clothes, you can use other payment methods.

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Data Revolution Inside Organizations

How to be prepared for the change that will transform the business landscape forever.

Worldwide access to vast amounts of data has changed the business landscape. Competitive marketing depends on knowing how to manage, process, and analyze that data. This article describes the path organizations need to take from collecting data to maximizing its use.
Today’s organizations are undergoing a challenging transformation process around their technical systems. The static software platforms that might have stored and processed a business’ data are no longer sustainable in the current web environment. Enterprises need cutting-edge technology to collect big data in real-time, analyze that data, and then get the information they need to stay competitive in today’s marketplace.

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Correlation is not causation

Why the confusion of these concepts has profound implications, from healthcare to business management

In correlated data, a pair of variables are related in that one thing is likely to change when the other does. This relationship might lead us to assume that a change to one thing causes the change in the other. This article clarifies that kind of faulty thinking by explaining correlation, causation, and the bias that often lumps the two together.
The human brain simplifies incoming information, so we can make sense of it. Our brains often do that by making assumptions about things based on slight relationships, or bias. But that thinking process isn’t foolproof. An example is when we mistake correlation for causation. Bias can make us conclude that one thing must cause another if both change in the same way at the same time. This article clears up the misconception that correlation equals causation by exploring both of those subjects and the human brain’s tendency toward bias.

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Monolithic versus microservices, and all in between

Developers often decide whether to build monolithic or microservices architectures based on personal preference. This article tells you how to design the best platform for your client by considering both methods.

Monolithic all-connected platforms might serve a startup’s needs, but they often have problems with scaling to support growth. Architectures built with modular microservices work well for bigger enterprises, but they might be overengineered to require more resources than a startup can spare. This article explains how to incorporate both these build approaches to design a functional strategy from the start that evolves to fit each point in a project’s lifecycle.

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Data Demystified — Machine Learning

A bird-eye view of the machine learning landscape.

The main goal of this article is to cover the most important concepts of machine learning, and lay-out the landscape. The reader will have the vision to understand what kind of solution matches a specific kind of problem, and should be able to find more specific knowledge after diving into a real-life project.

I’ll start with a 60 years old definition, but still valid today:

The name is pretty self-explanatory, and the definition reinforces the same concept.

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Build and grow cross-company knowledge

Culture is the way a company does things, its processes and values, and how it generates outcomes. It’s never easy to build, share, and promote knowledge across a medium-sized organization. That task requires leadership, rules, a strong culture, and having effective systems in place.

I’ve always been passionate about how knowledge sharing has a multiplier effect on the quality of what each person can deliver. I’ve seen junior developers, after a just few weeks, deliver higher quality work than what I could have produced years ago — even in a nonchallenging environment and even after years of experience.

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Ruby doesn’t scale

Why you should stop blaming a programming language for your low quality work.

I’ve heard too many times that Ruby on Rails (also called RoR) doesn’t scale. Guess what? Java doesn’t scale, .NET doesn’t scale, PHP doesn’t scale, and Node.js doesn’t scale. No programming language scales if you build terrible software with it.

In this article, I focus on Ruby, but the information is valid for almost any programming language. If you typically benchmark Ruby against other languages like Python or C++, it’s probably slower in most contexts.

The real question is not how long it takes or how many resources it consumes to run some algorithms like regex redux, binary tree searches, or reading DNA sequences.

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Improve data quality by using the pandas library and Python

Data quality is a broad concept with multiple dimensions. I detail that information in another introductory article. This tutorial explores a real-life example. We identify what we want to improve, create the code to achieve our goals, and wrap up with some comments about things that can happen in real-life situations. To follow along, you need a basic understanding of Python.

Python Data Analysis Library (pandas) is an open-source, BSD-licensed library that provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

You can install pandas by entering this code in a command line: python3 -m pip install — upgrade pandas.

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Data Visualization and The Truthful Art

An amazing book about data visualization that I can’t recommend enough is The Truthful Art by Alberto Cairo.

In The Truthful Art, Cairo explains the principles of good data visualization. He describes five qualities that should be your foundation when you work with data visualization: truthful, functional, beautiful, insightful, and enlightening. Cairo also gives some great examples of biased and dishonest visualization.

Before I dive into the “Five Qualities of Great Visualizations,” there’s another related concept that I want to cover: data-ink ratio, introduced by Edward Tufte in The Visual Display of Quantitative Information.

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