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.
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.
This post is the eleventh, and final, post in documenting the steps I went through on my journey to build an autonomous, voice-controlled, face recognizing drone. There are 10 other posts building up to this one which you can find at the end of this post.
Focus of this post
In this post I will share a video of the complete end-to-end demo and share details of the architecture which sits behind it. I will also share information on what I bought/used to bring this all together and relist all the different software, services and node packages in a single place.
Pulling It All Together
A lot of what we have been doing with this project is humanizing the way we communicate with machines/computers/things. That means talking and observing to drive intelligent interaction rather than using a mouse, keyboard or touch screen.
Our Autonomous Voice-Controlled, Face Recognizing, Drone is a smart drone which showcases, albeit crudely, how interaction with services filled with intelligence is going to evolve. It highlights the importance of cognitive services to the success of organizations in the future.
So with that said take a look at the entire end to end demo in the video below.
This post is the eighth post in documenting the steps I went through on my journey to build an autonomous, voice-controlled, face recognizing drone. There are 7 other posts building up to this one which you can find at the end of this post.
Focus of this post
We have come a long way from when we first started with a drone controlled from the computer. In the last post we spent time understanding how to use the Bing Speech API to convert supplied text to speech. In this post we will:
Show how you can use the Bing Speech API to derive text from speech.
Integrate that approach into our DroneWebServer.js web application and front end HTML so that we can control the drone via speech.