kevinalexbrown
Joined 15 years ago
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410
Member since
Aug 28
Recent submissions
Comment · 3 months ago
It’s ok to mourn the loss of a frontier and also support the thing driving that loss. The way it looks, the generation of mathematical results will become less and less of a human endeavor. There’s a way that’s sad even if on the whole it is a huge leap for society. This was a great article, and I applaud the author for acknowledging this head on.
Comment · 5 months ago
"tell me about biology" -> "Switched to Opus 4.8"
Comment · 11 months ago
Building the fndamental human patient representation: https://standardmodelbio.substack.com/p/the-patient-is-not-a...
The patient is not a document - multimodal foundation models for biomedicine. JEPA's working well.
Comment · 1 year ago
Standard Model Bio | various | Full-Time | ONSITE SF or Philadelphia | Hybrid
we're building the standard model for bio. We're doing for biology what mathematics did for physics.
papers this month: genomics - https://arxiv.org/abs/2509.25573 protein-language: https://arxiv.org/abs/2509.22853 longitudinal ehr: https://arxiv.org/abs/2509.25591
hello world: https://standardmodelbio.substack.com/p/introducing-standard...
we're humble and ambitious - we want to be the quiet backbone of biomedical ai but we want none of the glory of the final applications.
[email protected]
Comment · 1 year ago
building the standard model for bio: https://standardmodelbio.substack.com/p/introducing-standard...
We're pretty jazzed.
Comment · 4 years ago
Yes and this response has also become a trope. It’s generically universal to nearly any early result.
Comment · 5 years ago
The QZ article is narrowly correct but widely misleading. It almost willfully ignores the momentum and direction.
In reality, radiologists will not be summarily replaced one day. They will get more and more productive as tools extend their reach. This can occur even as the number of radiologists increases.
Here's a recent example where Hinton was right in concept: recent AI work for lung cancer detection made radiologists perform better in an FDA 510k clearance.
20 readers reviewed all of 232 cases using both a second-reader as well as a concurrent first reader workflows. Following the read according to both workflows, five expert radiologists reviewed all consolidated marks. The reference standard was based on reader majority (three out of five) followed by expert adjudication, as needed. As a result of the study’s truthing process, 143 cases were identified as including at least one true nodule and 89 with no true nodules. All endpoints of the analyses were satisfactorily met. These analyses demonstrated that all readers showed a significant improvement for the detection of pulmonary nodules (solid, part-solid and ground glass) with both reading workflows.
https://www.accessdata.fda.gov/cdrh_docs/pdf20/K203258.pdf
(I am proud to have worked with others on versions of the above, but do not speak for them or the approval, etc)
The AI revolution in medicine is here. That is not in dispute by most clinicians in training now, nor, from all signs, by the FDA. Not everyone is making use of it yet, and not all of it is perfect (as with radiologists - just try to get a clean training set). But the idea that machine learning/ai is overpromising is like criticizing Steve Jobs in 2008 for overpromising the iphone by saying it hasn't totally changed your life yet. Ok.
Comment · 6 years ago
Siemens Healthineers Imaging Intelligence | https://www.siemens-healthineers.com | Malvern, PA (Greater Philadelphia) | INTERNS | Onsite Our R&D group delivers medical image/text tools (e.g. deep learning, NLP, etc) for medical data analysis. We are well recognized for delivering cutting-edge intelligent solutions to Siemens 3D workstations and medical imaging scanners. Our group also has strong publication record in top tier journals and conferences, and several Siemens "inventor of the year" award recipients.
We offer well-paid internships lasting >= 3 months, with independent moonshot projects.
Responsibilities: · Contribute to research projects to develop intelligent solutions for medical imaging and text analytics · Conduct fast prototyping, feasibility studies for exploratory clinical research · Support the productization of research prototypes
We look for: · Strong research capability in computer vision, machine learning, text analytics and medical image analysis, proven by publications in journals/conferences. · Research experience in image/text analytics using large scale, weakly supervised / unsupervised learning algorithms · Research experience in medical image/text analysis of different modalities (CT, MRI, PET, medical reports etc.)
Email: [email protected]
Comment · 6 years ago
Siemens Healthineers Imaging Intelligence | https://www.siemens-healthineers.com | Malvern, PA (Greater Philadelphia) | INTERNS | Onsite
Our R&D group delivers medical image/text tools (e.g. deep learning, NLP, etc) for medical data analysis. We are well recognized for delivering cutting-edge intelligent solutions to Siemens 3D workstations and medical imaging scanners. Our group also has strong publication record in top tier journals and conferences, and several Siemens "inventor of the year" award recipients.
We offer well-paid internships lasting >= 3 months, with independent moonshot projects.
Responsibilities: · Contribute to research projects to develop intelligent solutions for medical imaging and text analytics · Conduct fast prototyping, feasibility studies for exploratory clinical research · Support the productization of research prototypes
We look for: · Strong research capability in computer vision, machine learning, text analytics and medical image analysis, proven by publications in journals/conferences. · Research experience in image/text analytics using large scale, weakly supervised / unsupervised learning algorithms · Research experience in medical image/text analysis of different modalities (CT, MRI, PET, medical reports etc.)
Email: [email protected]
Yes, if