Over my career in IT there have been a lot of changes in documentation practises, from the heavy detailed design up front to lean1 and now the adoption of literate programming, particularly in research (and somewhat contained to it because of the reliance on as a markup language2). While there are plenty of getting started guides out there, this post is primarily about why I’m adopting it for my new Science and Innovations department and the benefits that literate programming can give. Continue reading Using Literate Programming in Research
I’ve been with my current company for 9 months as Chief Information Officer and had responsibility for everything technical from production systems down to ensuring the phone systems worked and everything in between. The only technical responsibilities not under me was the actual development and QA of our products. CIO is a thankless role – when everything is going right, questions are raised over the size of the team and the need to replace servers and budget for new projects. When something breaks, for whatever reason, you are the focus of the negativity until it is resolved. The past 9 months have been a rollercoaster of business needs, including many sleepless nights. However, I can look back on this knowing that when I do finally get around to writing about my experiences as a woman in IT I will have a lot of fun stories for the CIO chapter1. While I didn’t have the opportunity to finish off as many of the improvement projects as I would have liked, I’ve built up a fantastic team and know that they’ll continue to do a fantastic job going forward.
Last year I wrote a post on whether machines could ever think1. Recently, in addition to all the general chatbot competitions, there has been a new type of test for deeper contextual understanding rather than the dumb and obvious meanings of words. English2 has a rich variety of meanings of words with the primary as the most common and then secondary and tertiary meanings further down in the dictionary. It’s probably been a while since you last sat down and read a dictionary, or even used an online one other than to find a synonym, antonym or check your spelling3 but as humans we rely mostly on our vocabulary and context that we’ve picked up from education and experience.
The day started with a great intro from Jana Eggers with a positive message about nurturing this AI baby that is being created rather than the doomsday scenario that is regularly spouted. We are a collaborative discipline of academia and industry and we can focus on how we use this for good. Continue reading ReWork DL Boston 2016 – Day 1
The following tweet appeared on my timeline today:
The Turing test is like saying planes don’t fly unless they can fool birds into thinking they’re birds. (h/t Peter Norvig) #AI
— Pedro Domingos (@pmddomingos) July 19, 2015
Initially I thought “heh, fair point – we are defining that the only true intelligence is described by the properties humans exhibit”, and my in-built twitter filter1 ignored the inaccuracies of the analogy. I clicked on the tweet as I wanted to see what the responses were and whether there was a better metaphor that I could talk about. There wasn’t – the responses were mainly variants on the deficiencies of the analogy and equally problematic in their own right. While this didn’t descend into anything abusive2, I do feel that the essence of what was trying to be conveyed was lost and this will be a continual problem with twitter. One of the better responses3 did point out that cherry-picking a single feature was not the same as the Turing Test. However, this did get me thinking based on my initial interpretation of the tweet.
In order to answer a big question we are simplifying it in one way. Turing simplified “Can machines think?” to “can machines fool humans into thinking they are human?”. Continue reading Can machines think?
A few days ago, researchers from Facebook published a paper on a deep learning technique to create “natural images”, with the result being that human subjects were convinced 40% of the time that they were looking at a real image rather than an automatically generated one. When I saw the tweet linking this, one of the comments1 indicated that you’d “need a PhD to understand” the paper, and thus make any use of the code Facebook may release.
I’ve always been a big believer in knowledge being accessible both from being freely available (as their paper is) and also that any individual who wants to understand the concepts presented should be able to, even if they don’t have extensive training in that specialism. So, as someone who does have a PhD and who is in the deep learning space, challenge accepted and here I’ll discuss what Facebook have done in a way that doesn’t require advanced degrees, but rather just a healthy interest in the field2. Continue reading Facebook’s latest deep learning research
The first session kicked off with Kevin O’Brian from GreatHorn. There are 3 major problems facing the infosec community at the moment:
- Modern infrastructure is far more complex than it used to be – we are using AWS, Azure as extensions of our physical networks and spaces such as GitHub as code repositories and Docker for automation. It is very difficult for any IT professional to keep up with all of the potential vulnerabilities and ensure that everything is secure.
- (Security) Technical debt – there is too much to monitor/fix even if business released the time and funds to address it.
- Shortfall in skilled people – there is a 1.5 million shortage in infosec people – this isn’t going to be resolved quickly.
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.
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.
So I picked up two key tutorials from different Professors: Continue reading Machine Learning – A Primer