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.
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.
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 possible to run Linux on Windows for a few years now. Windows Subsystem for Linux (WSL) was released in 2016, allowing native Linux applications to be run from within Windows without the need for dual boot or virtual machine. In 2019 WSL2 was released, providing a better architecture in terms of the kernel and improving the native support. A few weeks ago, Microsoft and NVIDIA announced GPU support on WSL2 and the potential for CUDA accelerated ML on Ubuntu from within Windows. Before I dive into this in detail, I want to take a quick aside into why you might want or need to do this…
While it’s no secret I love Lego and tech in general, I also love the educational STEM toys that are released. Sometimes, the ages on the toys don’t always make sense for their complexity, leaving a child who is either frustrated at something too tricky or too simplistic. Both can leave a young person slightly disengaged with STEM, the exact opposite of the idea of these toys!
Christmas 2019 I was given this Hydraulic Robot Arm kit, suitable for ages 10+1. With work, OU study and general life I’ve only just got around to building it2. So, let’s take a look – is it suitable for ages 10 and up for both build and principles it teaches?
There’s a trend in job descriptions that the company may be looking for “Data Science Unicorns”, “Python Ninjas”, “Rockstar developers”, or more recently the dreaded “10x developer”. When companies ask this, it either means that they’re not sure what they need but they want someone who can do the work of a team or that they are deliberately targeting people who describe themselves in this way. A couple of years ago this got silly with “Rockstar” to the point that many less reputable recruitment agencies were over using the term, inspiring this tweet:
Many of us in the community saw this and smiled. One man went further. Dylan Beattie created Rockstar and it has a community of enthusiasts who are supporting the language with interpreters and transpilers.
While on lockdown I’ve been watching a lot of recordings from conferences earlier in the year that I didn’t have time to attend. One of these was NDC London, where Dylan was giving the closing session on the Art of Code. It’s well worth an hour of your time and he introduces Rockstar through the ubiquitous FizzBuzz coding challenge.
After watching this I asked the question to myself, could I write a (simple) neuron based machine learning application in Rockstar and call myself a “Rockstar Neural Network” developer?
One of the things that I have been complaining about with many of the data science masters courses is that they are missing a lot of the basic skills that are essential for you to be able to be effective in a business situation. It’s one of the things I was going to talk about at the Women in AI event that was postponed this week and I’m more than happy to work with universities who want to help build a course1. That said, some universities are realising this is missing and adding it as optional courses.
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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