Tag: Machine Learning

Evolving Machines

I, for one, welcome our new metal overlords ;)
I, for one, welcome our new metal overlords 😉

Following my post on AI for understanding ambiguity, I got into a discussion with a friend covering the limitations of AI if we only try to emulate ourselves.  His premise was that we know so little about how our brains actually enable us to have our rich independent thoughts that if we constrain AI to what we observe, an ability to converse in our native language and perform tasks that we can do with higher precision, then we will completely limit their potential.  I had a similar conversation in the summer of 2015 while at the start-up company I joined1– we spent a whole day2 discussing whether in 100 years’ time the only jobs that humans would do would be to code the robots.  While the technological revolution is changing how we live and work, and yes it will remove some jobs and create others just like the industrial revolution did and ongoing machine automation has been doing, there will always be a rich variety of new roles that require our unique skills and imagination, our ability to adapt and look beyond what we know. Continue reading Evolving Machines

So I get a blue shirt… Chief Science Officer

Chief Science Officers have a lot to live up to... (image credit Wikipedia)
Chief Science Officers have a lot to live up to… (image credit Wikipedia)

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.

This week, I finished the handover of all my old responsibilities and started the role I was actually hired for back in 2015, but didn’t start with because the business needed a strong pair of hands elsewhere. I am now Chief Science Officer and have a new team of Computer Vision researchers and am taking over all of the data science and machine learning activities worldwide.  I’ve been given a remit of thought leadership with the team, so I’ll be attending (and speaking at) conferences, writing blog posts, publishing papers and let’s not forget that I’ll be neck deep in the research myself, leading a team of academics within a corporate environment on computer science research.
While I won’t be able to talk about what we’re doing until we’re ready to make it public, I will be blogging about the kit we’re using and some generic machine learning issues, as well as interesting papers as and when I find them.
It’s going to be a pretty exciting time – there are several cool projects that we’ve started and I’ve given myself some harsh deadlines so that we can have some results ready for a conference…

AI for understanding ambiguity

Please do not park bicycles against these railings as they may be removed - the railings or the bikes? Understanding the meaning is easy for us, harder for machines
Please do not park bicycles against these railings as they may be removed – the railings or the bikes? Understanding the meaning is easy for us, harder for machines

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.

Continue reading AI for understanding ambiguity

Rework DL Boston 2016 – Day 2

Me, networking at breakfast
Me, networking at breakfast

This is a summary of day 2 of ReWork Deep Learning summit 2016 that took place in Boston, May 12-13th.  If you want to read the summary of day 1 then you can read my notes here. Continue reading Rework DL Boston 2016 – Day 2

Can machines think?

The following tweet appeared on my timeline today:

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?

AI for image recognition – still a way to go

Result from IBM watson using images supplied by Kaptur
Result from IBM watson using images supplied by Kaptur

There’s a lot of money, time and brain power going in to various machine learning techniques to take the aggravation out of manually tagging images so that they appear in searches and can be categorised effectively.  However, we are strangely fault-intolerant of machines when they get it wrong – too many “unknowns” and we’re less likely to use the services but a couple of bad predictions and we’re aghast about how bad the solution is.

With a lot of the big players coming out with image categorisers, there is the question as whether it’s really worth anyone building their own when you can pay a nominal fee to use the API of an existing system.  The only way to really know is to see how well these systems do “in the wild” – sure they have high precision and recall on the test sets, but when an actual user uploads and image and frowns at the result, something isn’t right. Continue reading AI for image recognition – still a way to go

ReWork DL Boston 2015 – Day 1

brain-like computing
Brain-like computing

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.

Continue reading ReWork DL Boston 2015 – Day 1

Machine Learning – A Primer

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