No, this blog is not the start of a romantic comedy. It is focused on the topic of how we go about making massive, potentially life changing, decisions. The impossible choice arises when you have to choose between things in which one side of the equation starts out looking much better than the other but the obvious choice just does not feel 100% right. Later it turns out not to be so simple to differentiate since they are both fantastic in non-comparable ways.
Recently I had to make such an impossible choice. I admit it. I struggled a lot with it which surprised me. Normally I am a very decisive person with facts driving me along quickly. Not this time! I agonized, lost sleep, had arguments, questioned my motivations in life and more.
It seems a long time ago that the magic 3 V’s of volume, variety and velocity was unleashed on the world to describe the evolution of data organizations were about to see. In fact it has been a whole 17 years since Doug Laney first used the term.
The story was that we needed to get ready for a new wave of data. We needed to be ready to store more data, take data that might not look like we had traditionally from operational systems (such as textual unstructured data) and handle data arriving more quickly. This was in the web era long before mobile and social took off.
Then we had the Big Data storm where all the V’s got bigger, faster and more diverse. Social Media had arrived and the use of external data to help make decisions was on the rise. I went on record at the Gartner summit in 2014 saying I thought the buzz word would die out. It turns out it is still around but we hear it much less frequently nowadays.
Using Cognitive Computing to Humanize Computer Interaction
Every so often there is a fundamental shift in how we interact with “machines”. The era of cognitive computing is about to trigger a new shift.
The background
It is easy to forget how far input devices have evolved since the first automated computing devices were introduced just over a century ago. Today we are all used to touching, swiping and pinching using our fingers, on the screens in order to interact with machines.
This move to touch was a radical shift from typing on a computer keyboard or using a mouse (more recent), which was the norm, since the first automated computing devices were introduced just over a century ago.
It is pretty amazing to think that today we use touch to interact with machines daily. We rarely give that a second thought. Touch has undoubtedly augmented existing interaction models and opened new possibilities. There are now many things where touch is simply the best method of interacting with a machine.
Despite the innovation it is a fact that we have still not been able to really humanize the experience of working with computers or machines.
Have you ever wondered just what this phrase Deep Learning is referring to and why it matters? If so then this post is for you!
In my last post, I demystified a variety of buzzwords, and explained that Deep Learning is a subset of Machine Learning. This post explores the world of deep learning for non-mathematicians (just like myself). In doing so it:
Touches on convolutional neural networks;
Explains the impact Deep Learning is having on Cognitive Computing;
Outlines a few examples of Cognitive Computing (Deep Learning) in action.
Starting with Artificial Neural Networks
To understand Deep Learning, you must first understand a little about Artificial Neural Networks. Don’t worry – as I am not a data scientists I will not try to describe the mathematics behind it all. That means no talk beyond this sentence of weighting, activation functions and more.
Deep Learning normally revolves around the use of Artificial Neural Networks with more than 1 hidden layers. More on what a hidden layer is shortly. The theory is that the more hidden layers you have the more you can isolate specific regions of data to classify things.
Artificial Intelligence (AI) – Demystifying the latest buzzword
Everywhere you turn today someone is talking about Artificial Intelligence (AI). It appears to have taken over as the largest buzzword since Big Data.
Progressive organizations are actively seeking ways to apply AI. They want to use it to advance their businesses and build new experiences for those they interact with internally and externally.
Alas there is great confusion as to what AI means. That just gets worse when people mix that up with terms such as Machine Learning and Deep Learning. If you ask several different people their view on what is AI, or Machine Learning, you will get several different answers.
It is the age-old problem of describing an elephant dependent on which side of it you touch while blindfolded.
AI is not a new topic. People have been pursuing AI since the 1940s. Machine Learning, which has developed from the field of Artificial Intelligence, has been around since at least the 1980s and Deep Learning, which is a subset of Machine Learning, has been rapidly gaining in popularity over the past 10 years. This post explores all these topics setting the scene for some upcoming posts.
The impossible choice arises when you have to choose between things in which one side of the equation starts out looking much better than the other but the obvious choice just does not feel 100% right. Later it turns out not to be so simple to differentiate since they are both fantastic in non-comparable ways. 
