Data Science Weekly - Issue 651, by @DataSciNews open.substack.com/pub/datascienc…
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DataScienceWeekly
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DataScienceWeekly
@DataSciNews
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Katılım Kasım 2013
744 Takip Edilen34.7K Takipçiler
DataScienceWeekly retweetledi
No theorems, no bounds in neural networks in 2009?
Already in the mid 1990s, people like Pascal Koiran, Eduardo Sontag et al. were combining VC-dimension theory with NNs to get both theorems and bounds.
My book "A Theory of Learning and Generalization" was published in 1997.
Arjun Jain | Fast Code AI@Arjunjain
This is what AI looked like when I was doing my PhD in 2008. Tree search. Alpha-beta pruning. Branches and heuristics. I worked on the machine learning side, which was a separate field then. And neural networks, even inside ML, were treated as pseudoscience. No theorems. No bounds. I sat in seminars and smirked when someone presented results on them. I told friends not to waste a PhD on that stuff. The people I smirked at run the labs I cannot get into. One of them told me over coffee, in 2009, that he was switching to neural networks. I told him he was being unserious. I genuinely thought I was helping. He runs one of those labs. I got lucky. I went to NYU after, and the smirk left my face in six months. I am grateful I got in when I did. Two or three years earlier and I would not be writing this post. I think about that coffee a lot. What are you smirking at right now?
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@DataSciNews 8th wonder of the world is understanding how these three things compound into exponential growth: speed, crazy ambition, and continuously executing on them
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DataScienceWeekly retweetledi
this is how i wish i learned GPU fundamentals
not a lengthy textbook. not a static image. every concept is an interactive visualization.
covering the SM architecture, memory coalescing, synchronization, and more.
what concepts do you want to see next?
brrrviz.com
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DataScienceWeekly retweetledi
We @CompoundVC make a bull and bear case for the biggest questions in biotech today. The question we address are if:
- Neolabs in bio will become very large, independent companies
- Next-gen contract research organizations (CROs) that generate data for frontier and neolabs (along with biotechs) will become very big businesses
- China will become the global hub of biotech
- There will be more drug approvals per year in the US
- There will be a collapse of incumbent drug distribution.
- Biopharma becomes hyper-deflationary in mid-term (many assets for every target) and returns diminish drastically due to competitive pricing and higher distribution/marketing costs.
- The asset-centric turn in biotech VC produce a wave of orphaned, single-asset companies.
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DataScienceWeekly retweetledi
@michellearning so much of life is either really understanding this or really not understanding this
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People often forget an exponential slope is flatter than a linear one at the beginning
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DataScienceWeekly retweetledi
Today, I'm excited to share that I've started a company and closed an oversubscribed seed round.
Space has a labor shortage, and a growing surplus of science that we can’t afford not to do. Our astronauts’ time costs $130,000 an hour, and almost all of it goes to keeping them alive. The science happens in the margins. The bottleneck in orbit is not rockets, but labor.
General Astronautics will build and scale orbital manufacturing factories, starting with industrial robotics. This is the unlock to new pharmaceuticals, new materials, and new planets.
More on my thesis for humanity: generalastro.com
If this excites you, we are hiring. Come find us: careers.generalastro.com
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DataScienceWeekly retweetledi
AI and pharma are colliding. And so we did a deep dive on all the deals that have been done between these two camps and what all this means. Tremendous insights here from @joliegans corememory.com/p/pharmas-sput…
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DataScienceWeekly retweetledi
Jon Jacobsen discusses what a lot of us are thinking about: how to preserve (and maybe even amplify?) human intuition -- and the pleasure of understanding -- in an era when AI is reshaping the practice of math. link.springer.com/article/10.100…
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DataScienceWeekly retweetledi
I’m hosting a 2-hour live session on hyperbolic geometry through the Poincaré disk.
The disk looks bounded, but its edge is infinitely far away.
We’ll take that one strange fact and move through it as:
visual intuition -> hand proof -> Lean obligations -> Rust types -> TypeScript simulation
The central question:
Why is infinity hiding at the boundary of a circle?
We’ll build the intuition, prove the key idea, encode disk points in Lean, design safe geometry APIs in Rust, and visualize hyperbolic motion in TypeScript.
Not a beginner math class. Not a TS tutorial.
Just a technical rabbit hole for people who enjoy geometry, proof assistants, PL, simulation, and making mathematical objects executable.
check first comment.
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DataScienceWeekly retweetledi
Today we're publishing our index of unmet needs in human disease: 2443 indications scored and ranked on burden of disease, prevalence, pipeline activity, and treatment burden. We hope this will help drug developers identify overlooked medical problems
convoke.bio/blog/introduci…
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DataScienceWeekly retweetledi
DataScienceWeekly retweetledi
With the rise of AI in mathematics, many of us are rethinking what math is for—and what it even is. These are philosophical questions, and we should discuss them carefully and openheartedly. This classic essay by Reuben Hersh is a great place to start. gwern.net/doc/math/1979-…
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DataScienceWeekly retweetledi
This is a tutorial on diffusion and flow matching, based on my previous postings here. I’ve made available the PDF, a python notebook for people to play with it, and the TEX source so hopefully one of you can translate to your language. github.com/nandodef/love4…
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Our 5th grader’s aimsweb scores make it pretty easy to spot when we started using @MathAcademy.
Math has gone from tears at the dinner table to excitement about getting promoted.
Huge thanks to @exojason, @justinskycak, @ninja_maths, and the rest of the crew.
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@Matt_Bader @mathacademy @exojason @justinskycak @ninja_maths Congratulations to your kid for putting in the hard work!
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DataScienceWeekly retweetledi
If you've been struggling learning category theory, you might want to check out Paolo Perrone's 'Notes on Category theory: with examples from basic mathematics' available publicly on arXiv.
These notes were produced during a class given to a diverse set of scientists (including chemists and physicists), with knowledge in linear algebra being the only subject assumed to be known!
🔗👇
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Great talk by @OrgadHadas at @MIT_CSAIL today! Thank you for travelling all the way from @KempnerInst 😆
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