By Ed Yau
Not a day goes by without another media article talking about how AI will revolutionise our industries and make us all redundant. Much of this is hype, as we well know what is being talked about in research circles is still some way off a production ready system that is about to roll into operations. But how much of it is real and how much is due to journalists being over generous?
Join me for orientation of what is going on in the AI jungle to get a rebalanced view. A grounded review of AI research over the last 70 years and how far it has to go before we get replaced by computers. That's right, 70 years...
Orientation: reference points to frame your understanding
'Just Enough' Theory to Get Started
Practical Demo of a few useful tools
The Hard Problems in ML: the gotchas we need to think about
Further resources to continue your journey.
A technical specialist who has been developing enterprise software for 15 years.
I come from a traditional Computer Science and Systems Engineering developer background but these days I work in a hybrid technical and customer-facing role as a Solution Architect at a boutique software engineering team at cloudThing. Our focus is building software that helps organisations of all shapes and sizes solve complicated business problems with software. We create and build software products that match the best technologies to solve their problems; not the cheapest or quickest.
As the name suggests, cloudThing develop applications for the Cloud. We are also as vendor and technology agnostic as possible, but my areas of special interest within the team are the left-of-centre applications of Blockchain and Machine Learning. As part of my role, I am encouraged to spend time increasing my knowledge in these areas and develop real world business applications for emerging technologies. This is an area of real passion for me and I am always happy to bore anyone!
Practically, most of the Machine Learning I have used is in chatbots, but I have been on a journey of my own over the past 2 years to becoming a Machine Learning practitioner so I can give help to companies who want to understand what their goals should be for apply this technology: and how to do it responsibly.