Conference season online

October has always been a super busy month for me. I’m usually starting a new OU module and travelling around speaking at conferences and meetups, all while doing my day job, spending time with my family and enjoying my hobbies. Sometimes I’ve not got the balance right! 2019 I remember was particularly hectic. I optimistically submitted conference sessions at the start of the year on a variety of different topics and, as the year went on I was invited to speak at various meetups in the UK and even stepped in to do some last minute presentations where other speakers had dropped out. This time last year I had just finished 8 weeks where I had a week’s holiday, spoken at 5 conferences, 2 breakfast briefings and 8 meet ups, all of which were on slightly different topics!

I really enjoy speaking at these events, otherwise I simply wouldn’t do them! As an attendee I get to learn from my peers and be inspired by steps forward in areas that I just don’t have time to keep up to date on. As a speaker, I get to pass on some of the things I’ve learned over the years in what I hope is an entertaining way, and I always love the conversations after the talks.

This year has, inevitably, been very different. February was my first event, where I spoke at The European Information Security Summit in London on the risks that Deep Fakes pose to the security sector. I spoke to a lot of security professionals at the event who were unaware of how AI was progressing in both voice and face cloning. As an attendee, I learned a lot about the state of security in many of the systems we take for granted. If you can justify the time and cost, attending a conference outside of your area of expertise can be incredibly informative.

A mere few weeks later, and I had several sessions at Big Data and AI World. I had a panel session with the amazing Sue Daley and Vitaliy Yuryev on why basics are often overlooked in data projects, followed a few hours later by my main presentation on learning from projects that go wrong. This was the 12th of March. While the organisers were doing everything they could, practically all the international speakers and attendees had decided not to attend. The sessions were reorganised to prevent large gaps in the program and many of the sessions I had been personally looking forward to were no longer happening. I really enjoyed both my sessions and got some great questions after them, but it was clear that people were nervous about the crowds and conferences and meetups would be on hold from that point onwards.

As I headed home on the train that afternoon I knew I wouldn’t be back in London for a while. My company was considering a trial of homeworking for a few days a week1, but I’d already decided to swap to home working for the foreseeable future and told my team to do the same if they wanted. My team had been at the conference with me and I didn’t realise then that it would be the last time I’d see them2.

March and April would normally be the time that I would be submitting keynote suggestions for the Autumn conference season and spending my evenings talking to University students at meetups and I really missed those interactions.

While I was interviewed over the summer (Humans of AI, Agile Data Science), I really did miss the chance to interact with a wider audience. You can’t respond to questions in a pre-recorded video.

I was delighted when Barclays Eagle Labs asked me if I would rerun a talk on Deep Fakes that I had given in person late in 2019, as a series of three online events. Despite the strangeness of talking into a camera without the feedback of the audience’s faces and the ever present anxiety that one of my neighbours would start noisily doing DIY during the session3, it was great to see so many people take 30 minutes out of their day for three consecutive weeks to learn. After the final session, I got a lot of messages from people who had made their own fakes and really understood both the positive and negative aspects of the technology and thanking me for making it accessible. It’s this type of interaction that makes these events worthwhile. Sadly these sessions were not recorded, but the slides are on my slideshare (Part 1, Part 2 and Part 3) and a variation of the talk that I gave at Tech Exeter in 2019 is available on sitepoint.

At the end of September, one of the events that was cancelled from March was resurrected as an online event sponsored by DevelopHer. I had 5 minutes, which is both an eternity (if you’ve ever heard Just a Minute) and the blink of an eye (if you have more than a single thing you want to say)! I managed to condense the 25 talk on getting into Data Science and AI into (just over) 5 minutes alongside an amazing line up of other women in AI.

What really stood out to me from this event is how many people attended who may not otherwise have been able to go to an in person meetup. Not everyone has the luxury of being able to stay late after work, or travel in for these events, or may not want to even if they could. One of the huge benefits of everything moving online is that it has made many of these events far more accessible, and I hope that this continues in some form.

Post by Bethan Reeves watching my talk at home in comfort 😀

Last week I spoke at the online version of one of my favourite conferences, Minds Mastering Machines. The invite advertised me as one of their veteran speakers :D. I’ve done some heavily technical talks at their event over the past few years, but for 2020 I decided to be a bit lighter and given world events I’m glad I did. One of the things I’ve noticed in all the projects I’ve led, advised on, or done due diligence for, is that testing never seems to be a priority for data science and AI. This is something that drives me crazy so I thought I’d approach it in a light hearted way and try and convert the attendees to testing thinking with a talk titled: Your testing sucks – what should you be doing? I paired seven best practises of testing thinking alongside examples (mostly) from spacecraft. I think it went down well and hopefully it was memorably enough to make people want to make time for testing by remembering the various missions.

My presentation from MCubed. Don’t coerce your data.

While I’ve nothing else planned for this year or even 2021, I intend to speak at more conferences. Even when large gatherings are safe again, I hope that there will still be online streams for those that cannot attend. Let’s keep tech accessible.

A diagnostic tale of docker

twenty sided die showing common excuses for developers not to fix problems, the top of the die shows "Can't reproduce"
Developer d20 gives the answer 🙂 (from Pretend Store)

If you’ve been to any of my technical talks over the past year or so then you’ll know I’m a huge advocate for running AI models as api services within docker containers and using services like cloud formation to give scalability. One of the issues with this is that when you get problems in production they can be difficult to trace. Methodical diagnostics of code rather than data is a skill that is not that common in the AI community and something that comes with experience. Here’s a breakdown of one of these types of problems, the diagnostics to find the cause and the eventual fix, all of which you’re going to need to know if you want to use these types of services.

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Facebook’s Maths Solving AI

In December, Lample and Charton from Facebook’s Artificial Intelligence Research group published a paper stating that they had created an AI application that outperformed systems such as Matlab and Mathematica when presented with complex equations. Is this a huge leap forward or just an obvious extension of maths solving systems that have been around for years? Let’s take a look.

Continue reading Facebook’s Maths Solving AI

Getting a first job in AI or data science – what candidates need to know

Me in Lego – well not really, but it does look a lot like me 😉 – this was a very fortuitous collector fig from Series 18.

Getting any role in IT can be daunting as a first timer, whether it’s your first ever job or you’ve changed career or you’ve had a break and are returning as a junior in a new field or anything else.  Getting one in any part of AI can be even more of an up hill struggle.  Job posting and recruitment agencies are asking for PhDs, academic papers and post-doctoral research as well as years of experience in industry.  How can you get past that first barrier?  I get a lot of people asking me this when I present at Meet-Ups so thought I’d collate everything into one post.

I’m going to break down how you can demonstrate the skills that businesses need and how to talk confidently about what you can offer without the fluff.

Continue reading Getting a first job in AI or data science – what candidates need to know

Rework London 2019 Part 1

The ReWork Deep Learning summit in London in September has become one of my must have go to conferences. It’s a great mix of academic talks and more practical sessions regarding applications of various types of Ai in business, so I couldn’t miss it this year either. Here’s a summary of Day 1

Continue reading Rework London 2019 Part 1

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

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