Mike Ando

@dmikeando.bsky.social

ML @ Calico. Previously at Google Research and UCSF. Still amazed and excited by the power of microscopy to understand biology.

My book, Reinforcement Learning from Human Feedback is done! This is the book I wish I had when learning to fine-tune, align, & now post-train models since ChatGPT. The resource has been built by me finding time to study and document the fundamentals on nights and weekends since 2024.

Bild

Saw this lovely and informative work from the Bement lab and collaborators on a winter visit to Madison. Amazing mechanistic insights but the movies will blow your mind! Those are real patterns, not imagined AI versions 🧪

Journal of Cell Biology@jcb.org · 2w ago

Michaud, @andrewgoryachev.bsky.social, von Dassow, @xenresearchcenter.bsky.social et al. identify a versatile cortical pattern-forming circuit based on #Rho, F-#actin, Ect2, & RGA-3/4 rupress.org/jcb/article/... 📕 In "Collective Behaviors and Self-Organization in Cells" rupress.org/jcb/collecti...

Can you write down a cheap *verifier* for something's correctness, and come up with a way to cheaply propose possible solutions? If so you should expect that thing to be automated within the next few years Many important problems are hard to frame in this way (writing, art, some bio...)

Citizen Platano 🇵🇷@daniloc.xyz · 2w ago

going to be an emotional crisis for lots of *specialists* folks decided to go all in on narrow depth and the rewards from that are going to be under steady threat a diversified portfolio of skills and interests, meanwhile, enjoys steady returns under this paradigm compositional problem solving

the most realistic scene in Jurassic Park is when two scientists make a questionable decision — traveling to an undisclosed location with a strange man — on the promise of three years of modest funding

Back home now from what was a wonderful GRC on Biophotonics in Biology and Medicine! Bates College was picturesque (#dormlife) and it was a great mix of photonics, microscopy, and spectroscopy for both basic research and clinical application. Here is a thread of key takeaways in no particular order.

🎉 Big news: our paper "Representation matters" just got accepted as a Registered Report (Stage 1) in Nature Methods! 🧬 We're building the first rigorous, preregistered benchmark of instance segmentation representations in bioimage analysis. 🧵👇 (1/3)

Examples of intermediate representations used in bottom-up instance segmentation for bioimage analysis. Shown are representative pixel- or voxel-level targets derived from instance annotations, including binary feature maps (for example, foreground, contours, centroids, and skeletons), distance-based representations (for example, horizontal/vertical offsets, radial distances, distance to boundary, and distance to center), and affinity-based representations. Here, neighbor affinities denote local connectivity predictions indicating whether adjacent pixels or voxels belong to the same object. The rows show illustrative examples from different datasets and are not intended to imply a sequential workflow or a one-to-one correspondence between representations. In the benchmarked pipelines, such intermediate
representations are subsequently converted into final object instances by downstream grouping or post-processing procedures such as watershed, clustering, or graph partitioning.