Ive seen the analogy that AI is to biological intelligence as airplanes are to birds. I think that’s appropriate. And furthermore, we find that when we make better airplanes they don’t become more bird-like. We are optimizing for something that biology didn’t optimize for. I think AI will be similar

Just as the field of ML has become a distinct discipline from statistics (but have a lot of overlap… it’s a continuum), I feel like there’s a new field of AI that is diverging from ML. Again they have a lot of overlap, but the study and use of these massive AI models feels like a new thing

It seems that computational work is undergoing two simultaneous transitions that resemble transitions of the past: 1) The transition from coding in a programming language to describing via natural language (AI writes the code) resembles the transition from machine code to programming languages.

When trying to reason or prove something in a rigorous mathematical way as it relates to a real-world problem, most of the work seems to lie in developing the right formalism. Once the formalism is established, the math becomes "disinhibited" and flows more effortlessly.

Check out my recent talk on two tools I helped develop over the last few years (SpatialCorr and Monkeybread) for analyzing spatial transcriptomics data youtu.be/R8pEvndFHyo?...

Amazing (Here's the open access article: pmc.ncbi.nlm.nih.gov/articles/PMC...)

A nice interface to track the status of various NIH websites and resources given the recent instability: stats.uptimerobot.com/Zrqh8AhvKn

Thrilled to share Stellaromics has raised $80M in Series B to advance 3D spatial biology & launch Pyxa! This groundbreaking platform enables unprecedented visualization & analysis of cells & molecules in thick tissue samples. bit.ly/3QaGiRN #Stellaromics #Pyxa #SpatialBiology

Really interesting thesis: that the decline of American democracy and prosperity is driven largely by our declining mobility. www.theatlantic.com/magazine/arc...

Check out my recent talk on dimensionality reduction, where I try to lay out my thoughts on the topic: youtu.be/AuJzMnH78wM?...

Glad to see this issue of cell segmentation errors affecting spatial transcriptomics analysis (which most users observe) is now being investigated thoroughly: www.biorxiv.org/content/10.1...

Here's a blog post on Euler's number: a constant I only ever partially understood despite it appearing in equations across all of math and science: mbernste.github.io/posts/eulers...

More of an art project than science project, The Pink Chicken project is thought provoking nonetheless pinkchickenproject.com#what

It’s crazy to me that this works

Cells don’t interact in just 2 dimensions—so why study them that way? In 2D spatial analysis, critical interactions between cell types can be underestimated, leading to distorted biological insights. #3DSpatial #SpatialBiology #research Image from from Sui et al., bioRxiv

Cells transport proteins in membrane vesicles. These vesicles are formed by self-assembled exoskeleton. The exoskeleton scaffolds are cuboctahedrons made of proteins. Life is amazing!

Bizarre story. Genetic Analysis of a Sarcoma Accidentally Transplanted from a Patient to a Surgeon | New England Journal of Medicine www.nejm.org/doi/full/10....

Good software engineering requires understanding human cognition to reduce cognitive load. I think all the fancy paradigms are just ways to accomplish this. minds.md/zakirullin/c...

As someone new to image analysis, I recently came upon this perspective that seems trivial, but is actually important: A pixel is a "point sample" and not a little square. A pixel is a data point, sampled from the world, like any other data point would be. pages.graphics.cs.wisc.edu/559-f14/2014...

I like ideas that find hidden biological information in seeming “artifacts”. A similar idea in this vein is RNA-velocity where intronic RNA can be used to estimate change in transcription rate.

Excited to finally share Perceptein, a PERCEPtron made of proTEINs: www.science.org/doi/10.1126/...

Understanding kernel methods, like SVMs, at a deep level requires understanding a pretty complicated mathematical object called the Reproducing kernel Hilbert Space. I wrote a blog post detailing my understanding of these objects and how they relate to kernel methods: mbernste.github.io/posts/rkhs/

Over 30 prominent scientists call for a ban on the creation of a "mirror cell"--a microbe made of molecules that are mirror images of their natural forms. It could cause a mind-boggling global disaster. Here's my story [gift link] 🧪https://nyti.ms/3OUCXp6

The 100 Prisoners Problem is a really nice and compact example of how a seemingly intractable problem can have a hidden structure that can be exploited, but utterly belies human intuition. When I first read it, I couldn't believe it had a non brute force solution. en.wikipedia.org/wiki/100_pri...

I think a key quality that demonstrates scientific maturity is appropriately balancing quantitative assessments with qualitative observations. Beginners tend to err too far on one side or the other: www.scientificamerican.com/article/what...

Can AI improve the current state of molecular simulation? www.owlposting.com/p/can-ai-imp... in my first podcast, I spend 2 hours interviewing Corin Wagen and Ari Wagen, two brothers who are building the next generation of molecular simulation for drug discovery and material science

Come join our team and work on exciting computational problems to advance biomedical research! jobs.gusto.com/postings/ste...

Three BioML starter packs now! Pack 1: go.bsky.app/2VWBcCd Pack 2: go.bsky.app/Bw84Hmc Pack 3: go.bsky.app/NAKYUok DM if you want to be included (or nominate people who should be!)

It's hard for me to imagine working in a field outside of biomedical research and computational biology. Few fields offer all of the following: 1. A vast, open frontier waiting to be explored 2. The promise of saving lives and improving human health 3. Fascinating computational problems

Cells are crowded spaces packed with colliding and interacting biomolecules. Despite this, intracellular functions are often described using the clean language of networks. In this post I attempt to reconcile these seemingly contradictory perspectives. mbernste.github.io/posts/intuit...

The first list filled up, so here's a second list of AI for Science researchers on bluesky. Let me know if I missed you / if you'd like to join! bsky.app/starter-pack...

Two BioML starter packs now: Pack 1: go.bsky.app/2VWBcCd Pack 2: go.bsky.app/Bw84Hmc DM if you want to be included (or nominate people who should be!)

Why Recursion Pharmaceuticals abandoned cell painting for brightfield imaging www.owlposting.com/p/why-recurs... After over a decade,Recursion Pharmaceuticals changed its primary assay. Why is that? I answer that question over 5.6k words (26 minutes to read) first journalism-y piece!

Software engineering is difficult because you have to optimize two objective functions simultaneously: 1. Maximize correctness and performance (the obvious objective) 2. Minimize the expected future effort needed to modify, fix, or enhance the code (the less obvious objective)

I think a lot about this commentary about "Research Debt". This idea that humanity is drowning in a mountain of information with no easy way to understand it. Our problem isn't generating new knowledge, but rather, "distilling" the knowledge we have already generated. distill.pub/2017/researc...

As a transitionary post, my last post on X was a two-part blog post on diffusion models. I like to write pedagogical material on math and computer sciences topics I found challenging to teach myself Post 1: mbernste.github.io/posts/diffus... Post 2: mbernste.github.io/posts/diffus...

The experience on X is really degrading. Follow me here for thoughts on topics related to computational biology, computer science, and technology!