Introducing: Innovation series

I have been distracted by doing rather than writing for the past year. While this is likely to continue, I did have time to make a lot of notes on Innovation, particularly in my own field of machine learning and healthcare. So for the next few months I will post one innovation snippet per week.

These snippets are very similar in format to those presented by Seth Godin or Bernadette Jiwa. This means that I won’t go into too much explanatory details. I was hoping to put them together into a small book, but for now I just offer them as is here on this blog.

Mathematics and Biology (the Pharma Vision)

I began this blog almost exactly three years ago. My goal was two-fold: first, to demonstrate thought leadership in an area in which I was then founding a company; and, second, to have a demonstrated track-record rather than a picture-perfect pitch-deck. This latter is clearly a sub-goal of the first.

In those early days, I began a series of articles on Mathematics and Biology. I began with a comparison of the two fields. From there I moved on to discussing the people and skills involved, essentially why mathematicians so rarely do biology. I then discussed the outlier which is bioinformatics. I had two further articles planned in the series, after which point I wanted to attempt a synthesis. The missing articles are on the Microbiome and Quantitative Systems Pharmacology (QSP). These will remain missing articles for now, time has moved on and my thoughts on all of these topics have progressed enormously.

A few weeks ago I published a futurecast about using virtual patient’s for pharma drug development. There was a key idea buried in that article which in essence is my current synthesis perspective on mathematics and biology. I want to highlight this idea here and flesh it out slightly.

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Three things I know: Future-casting 3 of 3

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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Three things I know: Future-casting 2 of 3

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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Three things I know: Future-casting 1 of 3

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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5 Big Challenges Facing Medical AI

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 and my own journey

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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Mind-map customer needs

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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