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?
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.
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.
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
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
If you’ve read pretty much any other of my artificial intelligence blog posts on here then you’ll know how annoyed I am when the slightest advance in the achievements of AI spurs an onslaught of articles about “thinking machines”, that can reason and opens up the question of robots taking jobs and eventually destroying us all in some not-to-be-mentioned1 film franchise style. Before I get onto discussing if and when we’ll get to a Detroit Become Human scenario, I’d like to cover where we are and the biggest problem in all this. Continue reading Thinking machines – biological and artificial
If you’ve not read the day 1 summary then you can find that here.
Day 2 had a new host for track A in the form of David D’Souza from CIPD. His opening remarks quoted Asimov and Crichton and encouraging us not be magicians and to step back and think about what we should do rather than just what we could. Continue reading AI Congress London 2018 Day 2
London is a hive of AI activity. The UK is positioning itself as a leader in AI technology and you can barely walk around London without passing an AI company or meetup or training course1. If I didn’t actually have a day job, I could fill my time with AI conferences without actually doing much more than my daily commute. That said I am quite picky about the ones I go to. I’d never been to the AI Congress before and liked the diverse set of speakers and topics. I was lucky that the team at Logikk had invited me as their guest for the two days. So how did it stack up? Well, day 1 was at a much higher level than some of the other conferences I’ve been to, with a lot of implementation and enterprise discussions and far fewer talks on the technical implementations. If you’re senior then these conferences are for you. If you want someone to talk about their latest paper on arxiv then there are far more technical events that will suit you better.
One of the biggest problems I had was that there were three separate tracks and only one of me, so if I didn’t make notes on a particular talk then hopefully the slides will be available after the event at some point. I missed some of the high profile talks, in preference of other speakers, on purpose as I’d already heard those speakers at other events. Continue reading AI Congress London 2018 Day 1
I’ve taken longer than I normally would to respond to some recent news stories about AI “outperforming humans” in reading comprehension “for the first time”. Partly because I can’t help the wave of annoyance that fills me when I see articles so obviously designed to instil panic and/or awe in the reader without any detail, but also because I feel it’s important to do some primary research before refuting anything1. The initial story broke that an AI created by Alibaba had met2 the human threshold in the Stanford Question Answering Dataset (SQuAD) followed closely by Microsoft outperforming Alibaba and exceeding the human score (slightly). Always a safe bet for sensationalism, mainstream media pounced on the results to announce millions of jobs are at risk…. So what’s really going on? Continue reading AI better than humans at reading?