Making an "Impossible List"

Hey there,

Greetings from London! This week I created an “Impossible List” (see below) and shared the fourth video in my Machine Learning for Healthcare series (on neural networks - link at bottom).

Until next week,


📋 My “Impossible List”

I've often had goals - sometimes in my head, at other times written down - but until now I've had no central place that I've stored them. Inspired by Thomas Frank's "Impossible List", I've decided to change that.

Apparently an "Impossible List" is different to "just a bucket list" in that it is "an ever-evolving list of experiences that build upon each other, help others as well as yourself, and implore you to take action."

As I understand, the key ideas differences:

  • It inspires you to take action right now (rather than achieve something further down the line)

  • It's public (I guess for accountability, and perhaps to help others)

  • It's iterative (so when you achieve a goal, you may decide to set a further, more challenging, next step)

I'm hoping this list will help me retain clarity and focus, and also avoid the sensation that I haven't accomplished anything in my life by having a concrete record of the small wins.

So this week I made my Impossible List and here it is. I’m not quite ready to share this fully publicly yet, so it’s currently not linked to from anywhere else on my website. But I’m sharing it with you guys because this is my personal mailing list.

It’s definitely not complete yet, and I’m expecting it to evolve over time.

On the whole I found it a really helpful experience. I’ve had various goals bumping around in my head for quite some time, so it was cathartic getting them all down on paper. I found creating an ‘impossible list’ a pretty low-friction way to do so.

If you decide to start one, let me know!

This week’s links:

(1) An academic update on Coronavirus (a video)

This week, the Editor-In-Chief of JAMA (one of the top research publications) interviewed Dr Eric Topol (a highly-cited researcher) to talk about all things coronavirus. Having not read much coronavirus literature recently, this was a helpful update for me, including research showing:

(i) wearing masks reduces viral load and thus severity of infection

(ii) asymptomatic carriers can still show internal organ damage (such as lung fibrosis on CT)

(iii) most major organ systems appear to be affected by coronavirus, including even the pancreas (leading to increased diabetes risk)

(2) A technical primer on causality (a blog)

Since taking a probabilistic graphical models course at university, I’ve been really interested in how we can frame every day problems them. At work this week, we talked a lot about causality, and this introductory blog post was recommended.

(3) A monthly machine learning update (a newsletter)

This week I came across a weekly newsletter from a Machine Learning engineer + YouTuber called Daniel Bourke. It’s an update of 10 or so machine learning happenings from each month.

This week’s video:

This week I shared the fourth video in my Machine Learning for Healthcare series.

This video goes inside a neural network to understand how it works and why it's such a powerful machine learning algorithm. Despite initially being designed in the 1980s, it was only the increase in data and computing power that led neural networks to take off, and there are now many potential applications in healthcare.

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About Me

Hi! I’m Chris Lovejoy, a Junior Doctor and Data Scientist based in London.

I’m on a mission to improve healthcare through technology (particularly AI / machine learning), but along the way I want to share learnings that are relevant no matter your career choice or background.

In this weekly newsletter, I share my top thoughts and learnings from each week, as well as links to the best things on the internet that I come across.