OU Mathematics – final module choice

I started this degree not long after my daughter was born, a choice between an MBA and a degree for “fun” (learning for me rather than for career reasons) and it’s strange that I’m so close to the end of it. I have one level 3 module left to take. 30 points (or a quarter of a full time degree) to fit in around work and family1. I wrote a post a couple of years ago on my choices for level 3 and looking back at it now, I’m not sure I feel the same way.

Complex numbers (M337) was far more interesting and practically useful that I thought it was going to be. While it was a little drudgey at the start, towards the end everything came together beautifully. Deterministic and stochastic dynamics (MS327) was dull, it was essential mathematics and really useful, but there was nothing to excite me. Maybe this was a product of trying to study alongside long hours at work at the time. I wish I started with M303, the pure mathematics course, and then done complex numbers and the quantum world (my favourite module so far). However, I now have a choice again and I’m finding it tricky.

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Starting your first AI project – a guide for businesses

I’ve been speaking at several events recently giving practical advice on getting started with AI projects.  There is a huge chasm between high level inspirational business pieces on all the usual sites1 that business leaders read and the “getting started in AI” guides that pretty much start with installing Tensorflow.  There was nothing aimed at the non-AI CTO who didn’t want to fall behind.  Nothing to indicate to them how to start a project, what talent they’d need or even which problems to start with.  Sure, there are a lot of expensive consulting companies out there, but this knowledge shouldn’t be hidden.

This time last year, I sat down with David Kelnar of MMC Ventures and we talked about why so many AI projects don’t succeed.  He asked me to contribute some ideas to be included in the new State of AI report for 2019, to which I gladly agreed.  It soon became clear that to do this justice, it was more than just a chapter, and the MMC AI Playbook was born, which we recently launched.  Contributing to this amazing publication took a lot of time and research, and this blog was the thing that had to give.

If you are trying to find the right time to start your first project and need help on where to begin, please take a look at the playbook.  Here’s a taster, based on talks I gave at Austin Fraser’s #LeadersInTech event and the Barclays AI Frenzy event both in July 2019.

Continue reading Starting your first AI project – a guide for businesses

Quantum Journey – coming to the end of SM358

This week I finished the last of the books in SM358 – the quantum world and am now starting two and a half weeks of intense revision to prepare for the exam. This has been by far the most enjoyable module to study in my Open University mathematics journey so far, even if it was also the first one without face to face tutorials.

While I am very happy at studying on my own, one of the aspects I have really enjoyed in previous modules was spending a few hours every month with fellow students. Not so much to solve problems (as I am always well behind where everyone else is!) but to be inspired. When work and family commitments get overwhelming, study is easy to put to one side, having a checkpoint in the diary helps prioritise and I always left tutorials feeling motivated. I’m not sure whether SM358 didn’t have face to face tutorials because it is a physics module or if it’s just a module that has never been successful with these. Online tutorials are just not the same. Partially because I don’t get home early enough to attend them, but also after a day at work, trying to switch to student mode and find a quiet corner of the house just isn’t possible.

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A year of Apple Watch addiction and motivation

Early 2017 I got an Apple Watch.  I wasn’t fussed about them at the time as I never normally wear a watch of any sort.  But when my husband didn’t want his any more, I thought I’d give it a go.  A few months later and I was addicted.  While I used the word lightly at the time, what really worked for me were the regular achievements and challenges.  It was the same thing that got me hooked into World of Warcraft many years ago1 and I know that if I do something, I throw everything at it, but once I can’t complete a challenge I usually drop something.  After my initial post about the watch I found myself in a situation where I couldn’t achieve the challenges.  Towards the end of 2017 I had a few too many days in front of the computer with work and just wasn’t active.  What I noticed was that as soon as I missed a day of activity, and thus I couldn’t get a “perfect month” achievement, I stopped even trying to be active until the start of the next month. If there was no reward, even a completely irrelevant badge in an app, then why try…  Long term health benefits don’t give the same level of accomplishment in the short term for most people, myself included.  So after a particularly gluttonous December 2017 I made myself a promise. Continue reading A year of Apple Watch addiction and motivation

SM358 The Quantum World 25% in…

When I was looking at the level three maths modules for my Open University degree, one of the ones that really drew my eye was SM358, the quantum world.  I decided to only do a single module this year as I’d committed to a lot of speaking engagements in October and, in addition to my day job, I’ve been spending time on another project that I’m really excited about for the start of 2019.  From past experience, if you fall behind on OU modules at the beginning, it can be very hard to catch up.  This was really noticeable with the complex analysis and stochastic dynamics modules I started in 2017.  Rather than taking on too much, I decided on just one level 3 module.  Given my progress so far I’m only about a week behind and I’m pretty happy with that. Continue reading SM358 The Quantum World 25% in…

Agile Data Science: your data point is probably an outlier

It’s not often that I feel the need to write a reactionary post as mainly the things that tend to inflame me are usually by design.  However today I read something on LinkedIn that caused a polarisation in debate within a group of people who should really appreciate learning from different data: Data Scientists.

 

What was interesting was how the responses fell neatly into one of two camps: the first praising the poster for speaking out and saying this, supported by nearly an order of magnitude more likes than the total number of comments, and the second disagreeing and pointing out that it can work.  What has been lost in this was that “can” is not synonymous with “always”  – it really needs a good team and better explanation than many companies sometimes use.  What irked me most about the whole thread was the accusation that people doing data science with agile obviously “didn’t understand what science was”.  I hate these sweeping generalisations and I really do expect a higher standard of debate from anyone with either “data” or “science” anywhere near their profile. Continue reading Agile Data Science: your data point is probably an outlier

ReWork Deep Learning London September 2018 part 3

This is part 3 of my summary of ReWork Deep Learning London September 2018. Part 1 can be found here, and part 2 here.

Day 2 of rework started with some fast start up pitches. Due to a meeting at the office I missed all of these and only arrived at the first coffee break. So if you want to check out what 3D Industries, Selerio, DeepZen, Peculium and PipelineAI  are doing check their websites. Continue reading ReWork Deep Learning London September 2018 part 3

ReWork Deep Learning London September 2018 part 2

This is part 2 of my summary of the Rework Deep Learning Summit that took place in London in September 2018, and covers the afternoon of day 1. Part one, which looks at the morning sessions can be found here. Continue reading ReWork Deep Learning London September 2018 part 2

ReWork Deep Learning London September 2018 part 1

Entering the conference (c) ReWork

September is always a busy month in London for AI, but one of the events I always prioritise is ReWork – they manage to pack a lot into two days and I always come away inspired. I was live-tweeting the event, but also made quite a few notes, which I’ve made a bit more verbose below.  This is part one of at least three parts and I’ll add links between the posts as I finish them. Continue reading ReWork Deep Learning London September 2018 part 1

ImageNet in 4 Minutes? What the paper really shows us

ImageNet has been a deep learning benchmark data set since it was created.  It was the competition that showed that DL networks could outperform non-ML techniques and it’s been used by academics as a standard for testing new image classification systems.  A few days ago an exciting paper was published on arxiv for training ImageNet in four minutes.  Not weeks, days or hours but minutes.  This is on the surface a great leap forward but it’s important to dig beneath the surface.  The Register sub headline says all you need to know:

So if you don’t have a relaxed view on accuracy or thousands of GPUs lying around, what’s the point? Is there anything that can be taken from this paper?

Continue reading ImageNet in 4 Minutes? What the paper really shows us