At the end of my last post in this series, we had added the rear hood. This post focuses on the hood frame and lifting mechanism, covering issues 85-90 of 3D Create and Print by Eaglemoss Technology. If you’ve skipped a part of this series you can start from the beginning, including details of the Vector 3 printer I’m building on my 3D printer page. You will also need the parts from issue 78 that you should have kept safe… Continue reading 3D Printer Part 22: Hood frame and lifting mechanism
At the end of my last post in this series, we had added the top cover, filament guide and hood. This post focuses on the rear cover and some additional maintenance, covering issues 82-84 of 3D Create and Print by Eaglemoss Technology. If you’ve skipped a part of this series you can start from the beginning, including details of the Vector 3 printer I’m building on my 3D printer page.
It’s been a while since my last post where I was hoping that I would have a post on my first print. However, after reflashing the firmware as advised, I’ve struggled to get the laptop speaking to the printer. There are things to be done that I’m working through and as soon as I have a solution I will post it up. If you’ve got to this point and your printer is not working, please do not panic, I’ll put up diagnostic steps and solutions as soon as I have them. Continue reading 3D Printer Part 21: Rear hood and maintenance
Presented by Humans actress Gemma Chan, the show combined realistic prosthetic generation with AI to create a synth, but also dug a little deeper into the technology, showing how pervasive AI is in the western world.
There was a great scene with Prof Noel Sharkey and the self driving car where they attempted a bend, but human instinct took over: “It nearly took us off the road!” “Shit, yes!”. This reinforced the delegation of what could be life or death decisions – how can a car have moralistic decisions, or should they even be allowed to? Continue reading How to build a human – review
In September 2016, the ReWork team organised another deep learning conference in London. This is the third of their conferences I have attended and each time they continue to be a fantastic cross section of academia, enterprise research and start-ups. As usual, I took a large amount of notes on both days and I’ll be putting these up as separate posts, this one covers the morning of day 1. For reference, the notes from previous events can be found here: Boston 2015, Boston 2016.
When I attended the ReWork Deep Learning conference in Boston in May 2016, one of the most interesting talks was about the Echo and the Alexa personal assistant from Amazon. As someone whose day job is AI, it seemed only right that I surround myself by as much as possible from other companies. This week, after it being on back order for a while, it finally arrived. At £50, the Echo Dot is a reasonable price, with the only negative I was aware of before ordering being that the sound quality “wasn’t great” from a reviewer. Continue reading Amazon Echo Dot (second generation): Review
We’re all starting to get a bit blasé about self driving cars now. They were a novelty when they first came out, but even if the vast majority of us have never seen one, let alone been in one, we know they’re there and they work1 and that they are getting better with each iteration (which is phenomenally fast). But after watching the formula 1 racing, it’s a big step from a 30mph trundle around a city to over 200mph racing around a track with other cars. Or is it? Continue reading Formula AI – driverless racing
If you’ve been reading my blog for a while you’ll know that I start off with good intentions for my OU modules and then finding myself rushing TMAs, skipping a lot of the text and generally revising the day before the exam. While I’ve got away with this so far, it is getting harder to get the scores I want and I knew going into MST210 that my focus and time management would need to improve to take this seriously.
One of the things I took into account when doing this module was the amount of time I spend commuting. Working in London I have a train journey of between 30 mins and 1 hour 15 (depending on whether I travel in rush hour or not) and a tube journey of 27 minutes (fortunately on a single line) in each direction, so at a minimum I have 2 hours on public transport in four good half hour blocks. That’s 10 hours study time a week, which should be sufficient1.
I’m getting a head start on MST210 as with previous modules I’ve fallen behind due to work commitments and I don’t want to impact my family time playing catch up as I did last year. I’ve done one full week and completed book A unit 1. This is on par with the pace that the study calendar sets2 and I have made notes on all the examples and done every single exercise in the unit.
Keeping focus has been really hard. It’s really easy when you’re on a train at 6.30am to sip coffee and stare out of the window as you wake up gradually. It’s so easy when you get on a train or tube and have to stand to just leave my surface and book in my rucksac and play Peak on my phone. It takes no effort after a day at work to grab a gin and tonic and read my Kindle. What is hard is having that focus and discipline to make every minute count – every minute I spend geting ahead now is a minute I can spend having fun with my family rather than having to isolate myself to rush that TMA. It seems like a no-brainer, but humans do tend to make short-term decisions at the expense of long-term success . One of the best things we can do to overcome how our brains work is have a routine and stick to it3.
This is what I’ve been doing – every morning and evening, I’ve forced myself to get my MST210 books out, not only when I’m actually on the train/tube, but also while waiting for them – I keep them in my hands while changing trains; if I don’t put them away, then there isn’t the effort to get them out again. If I need to sit on the floor of a train so I can write, then that’s what I do. This focus has taken a lot of effort and I’m not sure how long it will be before it’s automatic, nor indeed what will happen when my routine changes due to business travel.
However, backed by the science that our brains are dumb enough to make bad short term decisions even if we are aware of the long term consequences, I know that the focus I need is entirely in my own control and if I stick to the routine long enough, it will become the go-to task for my selfish limbic system.
Today, after a lot of pondering I finally signed up for MST210 to start in October. This is the second 60 point module and, just like M208, is mandatory on the BSc Maths pathway. I’d been holding back for a number of reasons and reviewing my post from last year, I realised that nothing had changed. If anything my job is now more mathematically demanding as I dig deeper into the bleeding edge internals of machine learning. My 3D printer is nearly finished and my daily commute is now 3 hours a day, giving me 2 hours a day sitting on trains. That time is currently occupied with getting through a ridiculous amount of books1. What I really want to avoid with MST210 is some of the rushing that I did for M208 – I want to enjoy this module. Continue reading MST210 – mathematical modelling – registered
After my introductory post on Literate Programming, it occurred to me that while the concept of being able to create documentation that includes variables from the code being run is amazing, this will obviously have some impact on performance. At best, this would be the resource required to compile the document as if it was static, while the “at worst” scenario is conceptually unbounded. Somewhere along the way, pweave is adding extra code to pass the variables back and forth between the python and the , how and when it does this could have implications that you wouldn’t see in a simple example but could be catastrophic when running the kind of neural nets that my department are putting together. So, being a scientist, I decided to run a few experiments….1 Continue reading Literate programming – effect on performance
Over my career in IT there have been a lot of changes in documentation practises, from the heavy detailed design up front to lean1 and now the adoption of literate programming, particularly in research (and somewhat contained to it because of the reliance on as a markup language2). While there are plenty of getting started guides out there, this post is primarily about why I’m adopting it for my new Science and Innovations department and the benefits that literate programming can give. Continue reading Using Literate Programming in Research