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.Read more
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.Continue reading Facebook’s Maths Solving AI
Getting any role in IT can be daunting as a first timer, whether it’s your first ever job or you’ve changed career or you’ve had a break and are returning as a junior in a new field or anything else. Getting one in any part of AI can be even more of an up hill struggle. Job posting and recruitment agencies are asking for PhDs, academic papers and post-doctoral research as well as years of experience in industry. How can you get past that first barrier? I get a lot of people asking me this when I present at Meet-Ups so thought I’d collate everything into one post.
I’m going to break down how you can demonstrate the skills that businesses need and how to talk confidently about what you can offer without the fluff.Continue reading Getting a first job in AI or data science – what candidates need to know
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 1Continue reading Rework London 2019 Part 1
I’ve been speaking at several events recently giving practical advice on getting started with AI projects. There is a huge chasm between high level inspirational business pieces on all the usual sites1 that business leaders read and the “getting started in AI” guides that pretty much start with installing Tensorflow. There was nothing aimed at the non-AI CTO who didn’t want to fall behind. Nothing to indicate to them how to start a project, what talent they’d need or even which problems to start with. Sure, there are a lot of expensive consulting companies out there, but this knowledge shouldn’t be hidden.
This time last year, I sat down with David Kelnar of MMC Ventures and we talked about why so many AI projects don’t succeed. He asked me to contribute some ideas to be included in the new State of AI report for 2019, to which I gladly agreed. It soon became clear that to do this justice, it was more than just a chapter, and the MMC AI Playbook was born, which we recently launched. Contributing to this amazing publication took a lot of time and research, and this blog was the thing that had to give.
If you are trying to find the right time to start your first project and need help on where to begin, please take a look at the playbook. Here’s a taster, based on talks I gave at Austin Fraser’s #LeadersInTech event and the Barclays AI Frenzy event both in July 2019.Continue reading Starting your first AI project – a guide for businesses
It’s not often that I feel the need to write a reactionary post as mainly the things that tend to inflame me are usually by design. However today I read something on LinkedIn that caused a polarisation in debate within a group of people who should really appreciate learning from different data: Data Scientists.
What was interesting was how the responses fell neatly into one of two camps: the first praising the poster for speaking out and saying this, supported by nearly an order of magnitude more likes than the total number of comments, and the second disagreeing and pointing out that it can work. What has been lost in this was that “can” is not synonymous with “always” – it really needs a good team and better explanation than many companies sometimes use. What irked me most about the whole thread was the accusation that people doing data science with agile obviously “didn’t understand what science was”. I hate these sweeping generalisations and I really do expect a higher standard of debate from anyone with either “data” or “science” anywhere near their profile. Continue reading Agile Data Science: your data point is probably an outlier
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
This is part 2 of my summary of the Rework Deep Learning Summit that took place in London in September 2018, and covers the afternoon of day 1. Part one, which looks at the morning sessions can be found here. Continue reading ReWork Deep Learning London September 2018 part 2
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
ImageNet has been a deep learning benchmark data set since it was created. It was the competition that showed that DL networks could outperform non-ML techniques and it’s been used by academics as a standard for testing new image classification systems. A few days ago an exciting paper was published on arxiv for training ImageNet in four minutes. Not weeks, days or hours but minutes. This is on the surface a great leap forward but it’s important to dig beneath the surface. The Register sub headline says all you need to know:
This is your four-minute warning: Boffins train ImageNet-based AI classifier in just 240s https://t.co/j6Wu1yMMkM
— The Register (@TheRegister) August 1, 2018
So if you don’t have a relaxed view on accuracy or thousands of GPUs lying around, what’s the point? Is there anything that can be taken from this paper?