Announcing our Final Line-Up & Reduced Prices: Spring Summit on Fundamental Challenges for AI!

bit.ly/springsummitai…

We discuss core challenges & promising avenues in a day of high-profile keynotes & panels. Lunch & Reception included.

Come & join us!

April 15th,…

If you're investing and bio and want to drive sector-wide productivity gains, these are the areas you should be thinking about. Full thoughts here: sam-rodriques.com/post/key-probl… 5/

3. We need better tools for analyzing data and generating hypotheses. There are ~100M biology papers, and no human can keep all the info relevant to their experiments in their heads at once. We need automation to vastly expand data capture, data analysis, and data synthesis. 4/

2. We need to get access to the ground truth. When things don't work in a biology experiment, we rarely if ever know why. We need to develop methods to 'see what's going on down there.' Better microscopes (CryoET, APM, etc.) and molecularly accurate cell simulations. 3/

1. We need to be able to do experiments in human biology. The vast majority of experiments today are done in models that don't accurately reflect disease bio. We need to accelerate path from molecule to human, by improving safety predictions and access to consenting subjects. 2/

What are the key problems we need to solve to accelerate the design-build-test cycle in biomedicine today? 1/

I lost a bet about this poll because I am miscalibrated. I explore the reasons further in my new blog post, 'Who follows Richard Ngo on Twitter?'

sam-rodriques.com/post/who-follo…

For anyone who doesn't know about Woolf University, you should learn. It could completely change the way that higher education works. So glad Joe Betts-LaCroix et al. are doing this.

Teleportation is what you get when you have extremely good sensory and motor BCIs, so you can just switch to controlling any robot body you want around the world.

How many places (towns, cities, etc) have you lived in for 6 months or more?

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Things we learned starting FutureHouse #1: You can hire people who know more than you do*.

FutureHouse launched in September. Prior to that, I was an academic. In academia, you are a teacher, and so you are always hiring people who are less accomplished than you are. You hire…

The default approach to solving biology with ML is to gather as much data as possible as fast as possible and train a Really Big Model. The problem is: if you want to cure diseases, extend lifespan, and engineer human biology, the dataset you want is millions or tens of millions…