We’re just over a week into 2021 and I’ve been back at work (from home) for five days after a lovely two week, very relaxing break. The lockdown order was probably the best thing for me mentally as it completely removed all of the normal pressures of the Holidays. There were no long drives to family and finding someone to look after the cats while we were away, no rushing to fit in the trips to Santa or pantomimes. no panicking that we needed things in just in case we had visitors, and no injuries this year1. It was two weeks of pure, uninterrupted relaxation. I do miss my friends and family, but I am one of those people who just doesn’t stop and the only way I do is when I literally can’t do anything else.Continue reading Where did the last year go?
It’s inevitable, at the start of a new year, to reflect on what has gone before and what is yet to come. Janus, the Roman God for whom January is named 1, is depicted in such a way, so it’s difficult at this time of year to be anything other than retrospective :).Continue reading To everything there is a season
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’m proud to call myself Dr Bastiman. It’s on my email signature (personal and professional), it’s in my twitter name, it’s the title I use when dealing with I have to give my details for just about anything. I’m proud of it and have never consider this to be immodest. My title shows to the world that I’ve achieved something considerable. I was both surprised and then immediately not surprised when a storm started on Twitter…
My title is Dr Fern Riddell, not Ms or Miss Riddell. I have it because I am an expert, and my life and career consist of being that expert in as many different ways as possible. I worked hard to earned my authority, and I will not give it up to anyone.
— Dr Fern Riddell (@FernRiddell) June 13, 2018
I’ve had similar rants myself over the years. Particularly at one company where using my title in my email signature didn’t fit their cultural “tone of voice” yet at the same time senior males with PhDs were allowed to use their titles… I now use mine everywhere. However, the reason that the tweet came to my attention was one of the bizarre responses… Continue reading Dammit I’m a Dr not a Stereotype
If you follow my posts on AI (here and on other sites) then you’ll know that I’m a big believer on ensuring that AI models are thoroughly tested and that their accuracy, precision and recall are clearly identified. Indeed, my submission to the Science and Technology select committee earlier this year highlighted this need, even though the algorithms themselves may never be transparent. It was not a surprise in the slightest that a paper has been released on tricking “black box” commercial AI into misclassification with minimal effort. Continue reading Fooling AI and transparency – testing and honesty is critical
In the past few weeks my social feeds have been littered with articles citing “Hinton’s latest breakthrough” in AI: capsule networks. Like most people in the field, I make sure I read up on what’s new, and I’m yet to see the paper‘s first author Sara Sabour, get credit for her work in all of the tertiary reviews.
For those who aren’t in academia, there is a distinct order to the names on published papers either by contribution or alphabetically. For contribution, the first author is the one who actually did the research, the last author is the person who runs the lab/department and any other names are listed in order of contribution. Occasionally you will see notes that authors contributed equally. Some subject or countries list names alphabetically, but this is not the case for this paper published on arxiv. Continue reading Credit where it’s due in AI – capsule networks
I get very tired of the clickbaity journalism hyping up minor advances in AI, making news stories out of nothing or even the ones for those in the industry. You know the type: “Facebook AI had to be shut down”, “Google creates self learning AI”.
I demystify a lot of these when I’m asked about them – technology should be accessible and understandable and I deplore the tendency of those seeking to get article hits by over-egging with misleading headlines. What amused me over the weekend was that an AI not beating a human was a news story where the AI was “trounced”. Continue reading When did AI not being as good as humans be a news item?
There’s been a lot in the news recently about how Oxford and Cambridge are failing young people who are educated by the state system and how they perpetuate the elitist machine that runs the UK. As ever, I’m frustrated with the polarisation of the argument perpetuated by the media, which boils down to “it’s fine” and “everything is broken”, which no room for focussing on the nuances of the problems. As a state school student who went to Oxford1, I thought I’d weigh in with my own experiences and where I believe the issues are. Continue reading The Oxbridge Myth
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.