So, day one of the ReWork Deep Learning Summit Boston 2015 is over. A lot of interesting talks and demonstrations all round. All talks were recorded so I will update this post as they become available with the links to wherever the recordings are posted – I know I’ll be rewatching them.
Following a brief introduction the day kicked off with a presentation from Christian Szegedy of Google looking at the deep learning they had set up to analyse YouTube videos. They’d taken the traditional networks used in Google and made them smaller, discovering that an architecture with several layers of small networks was more computationally efficient that larger ones, with a 5 level (inception-5) most efficient. Several papers were referenced, which I’ll need to look up later, but the results looked interesting.
While on my flight to Boston for ReWorkDL I watched Ex Machina the “must see” latest AI film. I’d been warned that it wasn’t very good by my husband (who’d just flown home the day before!) but I thought that since he’d already seen it, I’d better take the chance to watch it since it’s unlikely to be something we’d watch together in the future. If you haven’t seen it, then please be aware that this post does contain spoilers so read on with caution. Continue reading Ex Machina – film review
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, 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.
Privacy & Cookies Policy
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.