I have had to move my website hosting this week. I was hosted by the computer society at National University of Ireland, Galway for many years. Their hardware is now on its last legs and the building it is hosted in has been turned into a field hospital for Covid patients. It was time to move on.
Many thanks to Compsoc at NUI Galway for the years of hosting.
I had the opportunity to interview for a senior position at a very big company recently. The entire process was fascinating for what it says about human nature and about large companies. The outcome of the process is unclear at the time of writing this but I am expecting the intrinsic misalignments in the process will lead it into the reeds from which it is unlikely to emerge. I am as close to the perfect candidate for the role that they will ever interview, but the internal parties are not all aligned around the very existence of the position.
I had quite a nice spring season of talks planned for 2020. I was invited to deliver a keynote on AI in Healthcare at Biovaria. And, I was one of the invited speakers for the Dynamics of Immune Repertoires conference where I would also have given a workshop, in Dresden. Covid-19 struck and the rest is history.
Emergencies lead to quick changes of plans. Anthony Kelly from AI in Action reached out to me asking me to take part in a special on AI in Healthcare.
Every blog I read eventually contains a post about i) navigating the blog, and ii) the author’s policy on writing. Consider this my attempt at the latter.
I have mentioned before that I find that writing benefits my long-term thought processes. It is meditative. I am forced to formalise my thoughts and chase-up loose ends. I have never considered myself to be good at writing – I failed English in school – but I find my confidence growing as I get older.
I have been reconnecting with some of my academic friends. We all belong more or less to the same age cohort. In recent weeks, I have been watching them interacting with one another on Twitter and through various other media. They each have achieved considerable degrees of success in their chosen fields – all have tenure at global top-50 ranked institutions. Through my observations, I have come to the realisation that the era of the solo contributor is dead.
I am asked quite often how I see Data Science in the biomedical industry. I have, of course, many answers each of which is context dependent. However one theme which I find frequently recurring is a sort of straw-man debate which seems to inherently attract technical practitioners.
The debate is usually structured as follows: How do you see the validation of medical AI products working in practice? Answer: clinical trials, test-validation sets, blah, blah But doesn’t this lead to enormous overheads? Answer: yes, but there are shortcuts But if you take these shortcuts then don’t you run the risk of running into costly failures when you finally run the clinical trials? It goes on….
Working in industrial research is usually very motivating but occasionally it is also frustrating. You’ve just done something really cool but you’re not allowed to tell anybody outside the company about it. Indeed, in a small company there might not be anybody inside of the company who can even appreciate it!
I have worked on roughly 4 really cool projects since leaving academia at the end of 2017. And apart from some basic mentions in my blog (e.g. here and here) most of what I have done has been known only to a few key stakeholders.
I had the opportunity to talk recently with a relatively advanced researcher in machine learning methods. The conversation turned briefly to the study of embeddings when he mentioned that most of his work involves things that can be embedded in Euclidean space. Since I’ve been spending a bit of time thinking about embeddings recently, I asked him some questions to get the official ML take on the subject. I was resonably gratified to learn that – although most ML engineers don’t think much about embeddings – the research on this topic considers the embedding to be tightly bound to the network architecture. It is not possible to study abstract embeddings, divorced from applications. I fully agree with this point-of-view.