Focus: The Leadership Superpower

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My wife is an event organizer, and in her early conferences, juggling a load of must-do items and decisions was a key pain-point as she kept many critical plates spinning. The stress of keeping all of it in her head at the same time filled up the cognitive capacity she had available to actually work on any of it.

We’ve all had people at work who’s answer to "When will that thing be done?" is always countered with "I’m working on it…" And, it never seems to be done. The most helpful people in taking on work are the least helpful in completing it.

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Scrum Retrospective 5: Close The Retrospective

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This is the fifth and last post of my blog post series about the five phases of a Scrum Retrospective. In this post, I cover Phase 5 – Close the Retrospective.

If you haven’t read the previous posts in this series you can start with Phase 1 – Setting the stage.

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Python Machine Learning: Tasks and Applications

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Python and Machine Learning

In this article, we will introduce you to Machine Learning with Python. Moreover, we will discuss Python Machine Learning tasks, steps, and applications. Then, we will take a look at 10 tech giants that adopt Python Machine Learning to improve what they do.

So, let’s begin!


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Training AI for AV: How Are Unannotated 2D Images Turned Into 3D Cuboids?

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For autonomous vehicles to successfully navigate myriad road obstacles, AI must be constantly trained to accurately perceive real-world 3D objects for what they are — traffic cones, pedestrians, electric scooters, etc. In order to do so, 2D images and video collected by sensor cameras must be refined and then annotated into 3D cuboid training data, which autonomous vehicle AI systems can leverage to become more intelligent. (This same method of creating 3D cuboid training data is also useful for teaching perception to AI in the field of robotics.) With cuboid annotation, drawings are first done manually and then calibrated for greater precision through a dynamic mathematical process that provides full 3D data for each cuboid. It’s an interesting process, and here’s a look under the hood at how it works.

Manual Cuboid Annotation

Manually annotating 2D images requires, rather simply, drawing boxes representing two sides of a cuboid around an object, like so:


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Scrum vs. Kanban: Pick One and Get on With It

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Don’t say this too loudly around agile conferences, but when it comes to the day-to-day work, Scrum and Kanban are basically the same.

Now, as an attendee of these conferences and an enthusiastic participant in discussions on pull systems; time boxes; empirical process control; and Little’s Law, I admit that it’s satisfying to go deep into these issues. However, it’s important not to lose focus on your team, your customers, and your product. Whether you’re doing Scrum or Kanban, the day-to-day work is about a team of skilled and experienced professionals collaborating, solving problems, and trying to make a positive impact. Sometimes this goes well – people succeed in creating great things together; sometimes it doesn’t – bad products are built by a disinterested team, producing poor results.

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How to Design for (and With) Developers

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Designers have a challenging task: Solve problems to empower users to do their best work. To understand how designers balance the demands of their roles as problem solvers with the evolving needs of an audience, I chatted with UX Manager Sarrah Vesselov about the considerations that go into designing for developers.

How Has Designing for Developers Evolved Over Time?

"It has become more complex since developers are using tools to do multiple things rather than a single thing."

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Software Test Automation and AI

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Pushing the Bounds of What We Can Automate in Software Testing

We have this funny little tagline about how we’re pushing the boundaries of test automation. It’s a simple enough thing when you say it, but what do we really mean by it?

Recently, we were recognized by several industry analysts for the work we’ve been doing pushing those boundaries. At voke, they said, "Parasoft is a company borne of innovation with a relentless focus on software quality," and Forrester said, " Regarding AI, Parasoft has an impressive and concrete roadmap to increase test automation from design to execution, pushing autonomous testing."


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The Rise of Machine Learning

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The recent surge of data has empowered a field of computer science that uses statistical techniques to give computer systems the ability to learn: Machine Learning. Modern Machine Learning Algorithms are able to overcome strictly static program instructions and make data-driven predictions that help companies make decisions with minimal human intervention.

IDC forecasts that spending on Machine Learning will grow from $12 billion in 2017 to $57.6 billion by 2021. What’s more, Machine Learning patents grew at a 34 percent CAGR between 2013 and 2017, making it the third-fastest growing category of all patents granted.


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Assessing Quality of AI Models from QA Standpoint

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In this post, you will learn about the definition of quality of AI/Machine Learning (ML) models. Getting a good understanding of what is the high and low quality of AI models would help you design quality control checks for testing Machine Learning models and related quality assurance (QA) practices. This post would be a good read for QA professionals in general. However, it would also help set perspectives for data scientists and Machine Learning experts.

The following are some of the key quality traits that are described in detail for assessing the quality of AI models:


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ML and AI in Mobile App Development

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Primarily, Machine Learning is the part of Artificial Intelligence that brings the computer systems a greater ability to enhance and study automatically from experience. Over the past few years, it has been creating very serious waves. Very recently, the applications of smartphones and other small-screen experiences have started to take shape that drives millions of interactions with their mobile devices. More importantly, the Machine Learning platform can make your smartphone very smarter by just increasing a host of processes as well as functions instantly. In reality, many smartphones are already using some kind of Machine Learning or intelligent automation application, which helps mobile phones in becoming more effective and efficient as well.

Why Machine Learning?

Overall, the businesses are ramping up their Machine Learning investment. Traditionally, the Machine Learning needs a fabulous quantity of power in which the mobile devices simply did not have. Still now, most of the businesses can install the special chips in automobiles, drones, and also in smartphones, which enables them to consume 90% less power. In the end, these mobile devices, even without an online connection, can do a wide array of complex tasks that include:


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