I have been writing a lot in the past 12 months and most of it has not appeared on this blog. My output on the blog is reduced as a result but this is normal. Writing is tiring.
About 18 months ago I left my second start-up Fosanis, slightly burnt-out and paradoxically highly-motivated.
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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.
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Last week I published an article about Artificial General Intelligence. This week I want to follow-up with my second of three attempts to predict the future. As I said, last week, this was part of a game which is commonly played in incubators when trying to draw insights from deep-tech founders. This week I want to talk about the Virtual Patient for drug development
I founded my first company over three years ago. We made no secret of our interest in using in-silico methods to build a Virtual Patient for drug development. We didn’t succeed that time but our lack of success had little to do with either technical issues or a lack of a commercialisation option, it was entirely our own fault.
The Virtual Patient
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There is a game which is popular in incubation environments. It works particularly well in drawing insights from deep-tech founders. What is the one thing which you know which nobody else knows today?
I played the game a few weeks ago with a potential co-founder and I came up with three personal insights. I’m going to share them here, in three short articles, over the next three weeks.
Artificial General Intelligence is closer than you think
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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.
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a16z is a major Silicon Valley venture capital firm. I listened to their recent podcast episode where they talked with Jeff Lawson, CEO and author of Ask Your Developer. Generally I am very interested in the topic of software development as a creative exercise, this is probably why I chose that particular episode of the podcast to listen to. But what I found of particular interest came in the final 30 seconds of the interview.
Jeff has a view of his own trajectory as a CEO, from that of a Technical-CEO, through Product, and then to Go-to-market. The a16z interviewer pointed out that they have an entire series on the concept of how technical people develop as CEOs and they see them moving through the following sequence:
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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.
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The following is a version of something which I wrote for a team I was mentoring recently. They were in a market-place with 2-3 main competitors, and each of the competitors was best-in-class at one specific thing and considerably worse on the other factors. When the team, which I was mentoring, compared their planned product against this market-place they basically rated themselves as second-best in every factor. So only the respective market leader in that factor was better than them on that factor. They thought this seemed pretty good – with a little bit of compromise, the clients could order just one product, theirs. I was not so convinced…
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I have this influences series which I began a few years ago. I began it because I think it is very important to both understand and respect the influences which brought you to where you are in life. It’s been a while since I contributed to the series, but today I’d like to continue with Seth Godin.
Seth, as he is known, is a rather latter stage influence in my life. He’s been well-known in internet circles for over 20 years. Maybe I heard of his name earlier, maybe I didn’t. I certainly didn’t have an accurate view of what it is he does and why he is so famous. Today I want to highlight my favourite learnings from Seth Godin.
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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.
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