Last week, I attended the Re Work Explainable AI mini summit. I am really loving so many great speakers being accessible online, particularly in a three to four hour format, which makes it easy to fit in around work commitments better than an in person summit – had it not been online I would have missed out on some great speakers.
Explainability is something I’ve been really focussing on recently. While it’s always been important, my frustration has been in research focussing on tools for machine learning engineers and not on clear explanations for the general public – the very people using, and being affected by, the systems we build. I was keen to attend this summit in particular as a refresh of where we were in terms of current best practise.
It will come as no surprise to anyone who has read any of my other posts that I loved puzzle books as child. I started with the Usbourne puzzle adventure series – cartoon books with clues on each page either in the pictures or what was said. I think my favourite was “Escape from Blood Castle“, which I got as a Christmas present in 1985. It was a perfect mix of slightly creepy and logical deduction that really appealed to me and I loved having new books from this series as they were published.
I quickly moved on to more text based mysteries – particularly the Hawkeye Collins and Amy Adams sleuth series. These were typical kids solve the mystery books. Unlike the Usbourne puzzle solvers, each mystery was stand alone and the answers were in the back of the book in mirror writing. At that age I didn’t have a small pocket mirror so taught myself how to read backwards to check my answers1. After that, it was whatever puzzles I could get may hands on.
One of the great benefits of lockdown for me is the time I have to catch up on some of the papers released that are not directly related to my day to day work. In the past week I’ve been catching up on some of the more general outputs from NeurIPS 2020. One of the papers that really caught my eye was “Ultra-Low Precision 4-bit Training of Deep Neural Networks” by Xiao Sun et al.
It’s no doubt that AI in its current form takes a lot of energy. You only have to look at some of the estimated costs of GPT-3 to see how the trend is pushing for larger, more complex models with larger, more complex hardware to get state of the art results. These AI super-models take a tremendous amount of power to train, with costs out of the reach of individuals and most businesses. AI edge computing has been looking at moving on going training into smaller models on edge devices, but to get the accuracy and the speed, the default option is expensive dedicated hardware and more memory. Is there another way?
What does this even mean and why are people putting it on their CVs? 🙂
Towards the end of 2020 I was lucky enough to be hiring for several new positions in my team1. Given the times that we are in, there are many more applicants for roles than there was even a year ago. I’ve spoken before about the skills that you need to get a role as a data scientist and there are specific things I expect to see so I can judge experience and competency when I’m looking at these pieces of paper so I can decide who I want to interview.
Sadly I’m seeing a lot of cringeworthy things on CVs that are the fastest way to put a candidate on the no pile when they reach me. These things might get you past HR and also past some recruitment agents, and I wonder if this is why candidates do them. I try and give as much feedback as I can, although sometimes the sheer volume of CVs and the time taken for constructive feedback would be more than a full time job. By sharing some of these things more publicly I hope to pass this advice on to as many as possible.
We’re just over a week into 2021 and I’ve been back at work (from home) for five days after a lovely two week, very relaxing break. The lockdown order was probably the best thing for me mentally as it completely removed all of the normal pressures of the Holidays. There were no long drives to family and finding someone to look after the cats while we were away, no rushing to fit in the trips to Santa or pantomimes. no panicking that we needed things in just in case we had visitors, and no injuries this year1. It was two weeks of pure, uninterrupted relaxation. I do miss my friends and family, but I am one of those people who just doesn’t stop and the only way I do is when I literally can’t do anything else.
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!
It’s been seven years of studying while working full time (and in some cases nearly double full time hours!) and I’ve now finished the degree I started for “fun” because I wasn’t being intellectually challenged in the job I had at that time. I was sceptical of all aspects of the Open University but thought I’d give it a go, knowing that without a cost to me and an exam, I would never make the time to study. While I’ve been blogging about individual modules over the years I’ve had quite a few conversations with many of you reading this blog about the pros and cons of study with the OU and one of the comments on my last post was from Korgan, who suggested I do a post about this and I’ve combined their questions with all of the others I’ve had.
There are a lot of people interested in data right now and there are a lot of visualisations to make that data easier to consume for people who are not data scientists. However, like any branch of statistics, visualisations can easily mislead. We are programmed to see patterns. If we are presented with a graphic that supports the surrounding text then we are more likely to believe the argument presented without further research1. I wrote about this on the Royal Statistical Society Data Science Section Blog in May, where reversing the colours in successive graphics can cause confusion. I’ve seen further examples and one caught my eye this month because it was being called out.
Seven years ago I was in work bored and desperate for a new challenge. My daughter had recently been born and I had decided to stop playing World of Warcraft. Needing a new challenge, I had toyed with an MBA but really wanted to do something for me. So I signed up for a BSc in Mathematics with the Open University, which I knew would take about 6 years part time while working. This week, I got the results for my final module and it was confirmed I had earned a first class honours degree. But why didn’t I do maths the first time round?
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