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
One of the things that I find I have to teach data scientists and ML researchers almost universally is understanding how to test their own code. Too often it’s all about testing the results and not enough about the code. I’ve been saying for a while that a lack of proper testing can trip you up and recently we saw a paper that rippled through academia about a “bug” in some code that everyone used…
A Code Glitch May Have Caused Errors In More Than 100 Published Studieshttps://www.vice.com/en_us/article/zmjwda/a-code-glitch-may-have-caused-errors-in-more-than-100-published-studies
The short version of this is that back in 2014, a python protocol was released for calculating molecule structure through NMR shifts1 and many other labs have been using this script over the past 5 years.Continue reading Let’s talk about testing
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
Early 2017 I got an Apple Watch. I wasn’t fussed about them at the time as I never normally wear a watch of any sort. But when my husband didn’t want his any more, I thought I’d give it a go. A few months later and I was addicted. While I used the word lightly at the time, what really worked for me were the regular achievements and challenges. It was the same thing that got me hooked into World of Warcraft many years ago1 and I know that if I do something, I throw everything at it, but once I can’t complete a challenge I usually drop something. After my initial post about the watch I found myself in a situation where I couldn’t achieve the challenges. Towards the end of 2017 I had a few too many days in front of the computer with work and just wasn’t active. What I noticed was that as soon as I missed a day of activity, and thus I couldn’t get a “perfect month” achievement, I stopped even trying to be active until the start of the next month. If there was no reward, even a completely irrelevant badge in an app, then why try… Long term health benefits don’t give the same level of accomplishment in the short term for most people, myself included. So after a particularly gluttonous December 2017 I made myself a promise. Continue reading A year of Apple Watch addiction and motivation
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?
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