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.
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
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
By now, the majority of people who keep up with the news will have heard of Cambridge Analytica, the whistle blower Christopher Wylie, and the news surrounding the harvesting of Facebook data and micro targeting, along with accusations of potentially illegal activity. In amongst all of this news I’ve also seen articles that this is the “awakening ” moment for ethics and morals AI and data science in general. The point where practitioners realise the impact of their work.
“Now I am become Death, the destroyer of worlds”, Oppenheimer
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
If you’ve been following this blog you’ll know that there have been great advances in the past few years with artificial image generation, to the stage where having a picture of something does not necessarily mean that it is real. Image advances are easy to talk about, as there’s something tangible to show, but there have been similar large leaps forward in other areas, particularly in voice synthesis and handwriting.
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