Over the past two weeks I have presented two future-casts. The first involves the ubiquitous appearance of AGI inside of ten years. The second concerns the tipping-point appearance of a Virtual Patient for drug development. This week is the third and final installment of the deep-tech incubation game.
I think we will have an Excel for Data Science within the next 3-5 years.
Continue reading “Three things I know: Future-casting 3 of 3”
I have been working on medical AI, in some form or other, for most of my adult life. For the past 12 months I have taken the opportunity to pause from racing forwards with my own start-ups and to look again, partly as a researcher, at the tools at my disposal and their intended applications. What I have seen worries me.
Part of my efforts to improve things, have taken the form of a number of peer-reviewed scientific articles. A few more such articles are still under review or exist only as work-in-progress. Today I want to summarise the 5 greatest problems which I see facing medical AI systems. For some of them I think that there are clear mitigations. For others, I suspect that we will need to rethink the entire system.
Continue reading “5 Big Challenges Facing Medical AI”
I have 20 years of professional experience. It took me longer than I would like to admit to learn how much we all lie to ourselves. I am a smart person. I am particularly good at constructing convincing narratives which keep me happy and oblivious to reality. It was only when I was working with incredibly smart people, during my PhD, that I was finally forced to write my ideas down. And then I didn’t need the other people to point out the flaws in my thinking; they were there in black-and-white, clear for me to see.
From this experience, I now encourage teams which I work with to make knowledge explicit. This is even more important the more intelligent the team are. The following is an example of how I did this with a team for their Customer Needs mapping, but the same advice applies equally to the Business Model and the Go-to-market Strategy.
Continue reading “Mind-map customer needs”
A good entrepreneur has a canny intuition for their True North. I’ve heard this from many good investors.
Personally I’ve always believed it. One of the bases through which I judge my professional contacts is on their decision making ability. Some people seem to always make good choices. Others, faced only with good outcomes, somehow still manage to find a more painful outcome.
Continue reading “True North”
I am a really lucky guy. I am deeply talented. I had access to computer and internet technology from the 1980’s. And people around me have always given me the space to do projects that I am passionate about.
Recently I was forced to confront myself with the realisation that, throughout my life, I have always worked on exactly the projects that I most wanted to work on. Even in school, I just didn’t go to class if I didn’t want to. I learned ten times as much at home, about much more interesting topics, and still managed to ace the exams.
As part of this self-confrontation, I learned that i) this is entirely selfish behaviour on my part, ii) it’s not such a surprise that I have often lacked a mentor at key points in my career.
Continue reading “My art”
Did you know that the development of new drugs has been unprofitable since 2005? (article, original study) I knew we were approaching that point, but I didn’t realise that we had already passed the threshold 13 years ago.
I have an insight which will completely disrupt (transform) the pharma industry. I have the background of working in two different domains in order to give credibility to my insight. And I have the ability, on a technical level, to execute on it.
How many of you have heard this pitch before? Continue reading “Designing a Disruptive Technology”