Today, things tend to be a little different. Now we get major iOS revisions every year and with the introduction of Swift back in 2014, the annual upgrade has become complicated. To be clear, I’m actually okay with all of this happening, as it’s actually very good to see so much development effort being put into Swift, but as the person doing the upgrade, the details can be highly frustrating.
Design thinking on the business side is used as a guide to problem solving so that we can get out of our ruts and develop innovative and often out-of-the-box solutions. In a world where we have somehow managed to glamorize failure into a celebrated roadblock to success, Design Thinking allows you circumnavigate failure so that you can instead celebrate a prudent achievement.
Summarizing the key learnings from Northwestern’s Kellogg School of Management’s Design Thinking workshop and pulling together various resources as well as articles on the subject. After graduating with an MBA from Kellogg in 2005, I have been involved in many aspects of user experience and this workshop brought it all together for me.
Design Thinking consists of applying the (design) process of designing physical products to business decisions, services and even process improvement initiatives.
When Amazon Echo with Alexa service came out in November 2014 I was skeptical. A speaker with voice recognition seemed like an unnecessary oddity.
Alexa’s SDK has been open to third party developers for a year now. As a software engineer it is important to keep up with emerging technologies and learn about them. I purchased an Amazon Echo about a month ago and had an opportunity to interact with the technology and try out the SDK. Subsequently, I discovered these important truths:
Having grown up as Generation Y and watching technology grow at what seems like hyper speed, I can’t describe just how interesting it has been watching each generation interact with new technology, new devices and how much the latest generation (Millennials) has influenced the rapid request for change across many industries.
The usefulness of NLP is largely seen when combined with machine learning capabilities. With regards to being leveraged by businesses, when a machine can learn the semantics and context of the questions it is asked and statements it is given, it will enable businesses to unlock the potential of its large volumes of data to streamline processes and extract information without needing the help of a subject matter expert.
Machine Learning is creeping into companies of all sizes and I’ve found that many of those who want to implement it are those who aren’t in IT. Business clients are able to explain the results of machine learning within an application or set of applications, yet have trouble understanding exactly what machine learning actually is, how it works and why it takes longer than they think to get the results they want.
Following from my previous blog post, we will continue to discuss the various demos in LPKTutorialThree.
In previous tutorials, we went through CRUD with LPKTutorialOne, and Function IDs / QueryFilters / Composites with LPKTutorialTwo. In this tutorial, I want to go over common components that LPK offers, that will expedite the process of building your iOS application.
Note: The components covered in this tutorial offered by LPK are only supported for the iPad device.
In this tutorial, we will go through LPKTutorialTwo, where we go through a configuration driven method of displaying data on the app, and the basics of filtering.
Reminder of the LPK examples git hub rep, where you can get all applications / code.
