martin palazzo

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martin palazzo

martin palazzo

@boardsofdata

tech @StammBio. teach @UdeSA.

high dimensional space Joined Kasım 2011
2.1K Following1.4K Followers
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Stämm
Stämm@StammBio·
We’re redefining biomanufacturing. Today, we introduce the High-Throughput Bioprocessor (HTB), Stämm’s new automated platform to rethink biological scaling.
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Tomás G.
Tomás G.@tomasgna·
my (first!) article, “From the virtual community to ‘Trust and Safety’: eBay (1995–2007) and the rise of platform governance” is out on Big Data & Society! it is a history of content moderation at eBay, where they coined the term Trust and Safety 🌐 link below
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will brown
will brown@willccbb·
can confirm these are the vibes if you’re questioning whether staying in academia, quant, big tech, or a big lab is how you want to spend the singularity, hit me up
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Amy Tam@amytam01

x.com/i/article/2023…

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Stämm
Stämm@StammBio·
For decades in the biopharmaceutical industry, bigger was better: massive tanks, sprawling facilities, global blockbusters. But rising biosimilar competition and demand for specialized therapies are forcing a shift. The old batch paradigm is giving way to a new way.
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Eryney
Eryney@eryney_ok·
Never been a better time to pivot into biology. The problems are very far from solved. Engineering skills will be extremely important in continuing to push the frontier forward. It's hard, but wildly worthwhile. Leave B2B SaaS to agents :)
Madison Kanna@Madisonkanna

as a software engineer, i feel a real loss of identity right now. for a long time i defined myself in part by the act of writing code. the pride in a hard-earned solution was part of who i was. now i watch AI accomplish in seconds what took me hours. i find myself caught between relief and mourning, awe and anxiety. the craft that shaped me is suddenly eclipsed by a machine. who am i now?

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Probability and Statistics
Probability and Statistics@probnstat·
Representation learning is the idea that instead of hand-designing features, algorithms should learn the best way to represent data directly from examples. In probability theory, this corresponds to finding latent variables or transformations that make complex joint distributions simpler and closer to independent, which improves inference and prediction. In statistics, representation learning appears in factor models, principal component analysis, and mixture models, where high-dimensional observations are summarized by a few informative hidden components. In machine learning, deep neural networks, embeddings, and autoencoders learn hierarchical representations that capture edges, shapes, words, meanings, and abstract concepts, enabling powerful performance in vision, language, and recommendation systems. In real life, representation learning allows computers to understand faces, voices, medical scans, and user behavior by converting raw signals into meaningful patterns. By discovering the right internal coordinates of data, representation learning makes learning, generalization, and decision-making possible at scale. Image: share.google/9IwlvggLkIyCFo…
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Stämm
Stämm@StammBio·
The video below shows how the full circuit works and what happens inside the cartridge as cells grow in continuous laminar flow. youtube.com/watch?v=222V1N…
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Alec Helbling
Alec Helbling@alec_helbling·
A visualization of a flow model that transforms a 1D standard Gaussian into a Gaussian mixture. The heatmap shows the changing density and the white lines reflect the trajectories of individual samples over time.
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Paras Chopra
Paras Chopra@paraschopra·
Learned something very interesting today! Random projections of a non-linearly separable data onto high dimensional spaces is enough to make it linearly separable. Consider a dataset like XOR that you can't linearly separate. Now, if you project each 2D point onto a D (=50) dimensional space using *randomly* initialised basis vectors, each direction creates a tiny difference between the classes (e.g. gives 51-52% accuracy) because expectation of two classes differs slightly when randomly projected. So each randomly projected feature becomes a tiny discriminator and when you aggregate it over 20-50 such discriminators, a linear classifier is able to separate them perfectly by simply learning how much to weigh each feature. One intriguing possibility of this is that we're able to train deep networks because random projections make most of the data already separable, making the job of gradient descent easy.
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Simone Conradi
Simone Conradi@S_Conradi·
Take two large random matrices and linearly interpolate between them at several hundred steps. Compute the eigenvalues for each interpolated matrix, then plot them in the complex plane. The result is shown here. Made with #python #numpy #matplotlib
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Josie Zayner
Josie Zayner@josiezayner·
Biology as an industry is massively underappreciated for its scale, value and success because when people say biotech they mean pharma and not using technology and genetics for agriculture The pig industry alone is equal to the global smartphone market
Max Unfried@MaxUnfried

Biotech is the industry with the lowest success rate unfortunately, but at the same time trying to solve the problems that are worth solving the most.

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Denghui Zhang
Denghui Zhang@denghui_zhang·
Quite interesting. If reverse engineering from latent embedding to input tokens is feasible, then sharing prompt embedding or vectorDB under RAG setting is not safe (or private) anymore.
GLADIA Research Lab@GladiaLab

LLMs are injective and invertible. In our new paper, we show that different prompts always map to different embeddings, and this property can be used to recover input tokens from individual embeddings in latent space. (1/6)

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Villa Diamante
Villa Diamante@Villadiamante·
Durante muchos años cuando volvía de pasar música a la madrugada con alto grado de estrés auditivo, llegaba a casa y ponía Music For Airports de Brian Eno, era mi hisopo musical. Hacemos un hilo con discos de ambient o música muy tranquila para bajar tres cambios?
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Alec Helbling
Alec Helbling@alec_helbling·
Data often lie on a low-dimensional manifold embedded in a high-dimensional space. But these manifolds are often highly non-linear, making linear dimensionality reduction methods like PCA insufficient. This has motivated the development of non-linear dimensionality reduction.
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martin palazzo
martin palazzo@boardsofdata·
@comfortcske Dulces Otito is a jam factory in San Salvador de Jujuy (Argentina) founded by my grandparent Amado Galli in the 70s. 'Otito' is the name of my uncle. The picture shows a can of 'Dulce de Batata', a marvelous desert.
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Jacob Boysen
Jacob Boysen@jboysen0·
AI ppl: we basically solved bio with the thinking sands Bio ppl: after decades of lab toil, we solved how to stably express GFP in stem cells
Mike Gallagher@GeneticsMike7

1/ Happy to share important work done with my co-author Andrew Khalil in the labs of Rudolf Jaenisch @WhiteheadInst @MITBiology @MIT and Dave Mooney @Harvard @wyssinstitute trying to assess and fix the major problem of transgene silencing in human ESC/iPSC based work

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martin palazzo
martin palazzo@boardsofdata·
@miangoar I would add - Pytorch (deep learning) - AnnData (annotated table mgmt) - Poetry (env mgmt)
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GAMA Miguel Angel 🐦‍⬛🔑
GAMA Miguel Angel 🐦‍⬛🔑@miangoar·
Is this a good basic toolkit to get started in AI-driven protein design from scratch? 🤔 Which other essential tools would be missing?
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