If you’ve been following this blog you’ll know that I’ve started a new role that requires me to build a deep learning system and I’ve been catching up on the 10+ years of research since I completed my PhD. With a background in computing and mathematics I jumped straight in to what I thought would be skimming through the literature. I soon realised that it would be better all round to jump back to first principles rather than be constrained with the methods I had learned over a decade ago.
So, I found a lot of universities who had put their machine learning courses online and have decided to work through what’s out there as if I was an undergraduate and then use my experience to build on top of that. I don’t want to miss an advantage because I wasn’t aware of it.
I’m four weeks in to my new role and one of the threads of work I have is looking into machine learning and how this has advanced since my own thesis. The current approach to machine intelligence is via learning networks where the data is abstracted: rather than recognising specifics about the problem, the algorithm learns the common elements of the problem and solution to match an input to the expected output, without needing an exact match. Our brains are very good at this: from a very early age we can recognise familiar faces from unfamiliar ones and quickly this progresses to identification in bad light, different angles, when the face is obscured. Getting machines to do the same has been notoriously difficult. Continue reading Machine intelligence – training and plasticity
If you’ve been watching anything on Channel 4 recently you’ll have seen a trailer for PersonaSynthetics – advertising the latest home must-have gadget. The ad itself is slightly creepy, despite the smiling family images, and the website supports this sterile AI view to an extent that some people have expressed concern over a genuine product being available. It’s a fantastic ad campaign for their new series Humans, which in itself looks like it’d be worth a watch (there’s a nice trailer on the website), but it has raised again the issues around artificial intelligence, and how far should it go.
This is of particular interest to me as I am starting a new project in machine learning and, while my work isn’t going to lead to a home based automaton, there are some interesting questions to be considered in this area to ensure that we don’t end up making ourselves obsolete as a species. Continue reading Artificial Intelligence
So, a few days ago, the internet had a new toy: How Old Robot – a very simple website where you can upload a photograph and it will guess your age and gender. For many people the guess was about right, but there were some howlers, with very similar images being uploaded and giving age results differing by (several) decades!
The site doesn’t hide the fact that it’s a learning tool based on Microsoft’s facial recognition technology and is built on the Azure platform as an example of how quickly it is to build and deploy sites using Azure. What started off as a quick demo from the Build conference soon became viral, with people all over the world loading their photos into the app and sharing the results on social media. This is exactly what Microsoft wanted and they’ve been oh so clever with this and here’s why.
I’m three days in to my new role and, while there is some run of the mill development that I’m managing there’s also a very exciting project just starting that I’ll be taking from the very beginning based on a discussion I had with the CEO on my first day.
This new secret project means I’ve got to become an expert in Deep Learning and also all the changes in AI and since I wrote my own thesis. I discovered very quickly that the way I knew was the “old way” and that machine learning has come on very considerably in a short space of time. So the past few days I’ve regressed into academic mode.
So, most people know by now that in a week’s time I start a new role. After 12 years of working for established business both small and large I am joining a start up in an area at the current edge of what is possible in computer science. I’m very much looking forward to having my technical and scientific abilities stretched as far as they’ll go and, not unsurprisingly, the immersion in a new venture where the focus is on the solution and not why things can’t be done (often the case in established companies).
I have a reading list as long as the references for my own thesis to get through in the next few weeks so I can become an expert in my new field: deep learning and artificial intelligence. One of the first things I’ll be doing is attending the ReWorkDL summit in Boston, MA, which is just a fascinating line up of some of the leading people in this space. All being well I will be presenting at the 2016 summit.
I’ll be tweeting throughout the event with thoughts and comments and will do a summary post afterwards.