panabee

6 ms·

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Ask HN: When human experts disagree, how should machines determine truth?
1 points by panabee 8d ago|1 comments
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by panabee 13d ago
I think a lot about fixing broken VC-founder dynamics, and this post by Marc Pincus ( https://x.com/markpinc/status/2089572143344599079 ) crystallized one plank of the platform. The principle is simple. VCs are socc
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Creative Thinking by Claude Shannon(www1.ece.neu.edu)
3 points by panabee 27d ago|0 comments
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by panabee 1mo ago
The overall dataset was very large (8,972,221 deaths), but only 1,348 ambulance drivers were included, and just 10 had Alzheimer’s disease as the underlying cause of death. Such a small number of outcomes matter because statistical precisio
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by panabee 5mo ago
How to notify you once v0 is ready -- just comment here?
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by panabee 5mo ago
How to notify you once v0 is ready -- just comment here?
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by panabee 5mo ago
Will aim to ground the framework -- Cancer Mini-101 for Engineers -- in personal use cases. I hope it will be helpful for you.
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by panabee 5mo ago
Great question. The bar for proof in biomedicine is naturally high. I only shared facts because so much is unknown. If you can find a lab exploring the question, maybe you can support them by helping to raise money for experiments. As a fun
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by panabee 5mo ago
I will aim to put together a Cancer 101 for engineers, not sure how to share. Maybe I'll post here or will post to our biomedical GitHub so it can evolve over time?
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by panabee 5mo ago
For people questioning why to involve GPT and AI assistants: GPT and AI assistants cannot be fully trusted, but they can personalize learning. The chief challenge for the framework/handbook will be resolving how to personalize guidance
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by panabee 5mo ago
On second thought, I will publish something regardless of interest. It will be an "Cancer for Engineers" framework, delivered via free, open-source Custom GPTs and Claude Skills. (Gemini gems are less reliable in our experience.)
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by panabee 5mo ago
Here are more fascinating facts about caffeine and cancer. Caffeine affects the immune system via at least two opposing mechanisms. Mechanism 1: A2A receptor antagonism (immunostimulatory) Tumors and damaged tissues release adenosine, which
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by panabee 5mo ago
If you're a wealthy person lacking a neurobiology background, how do you decide which research efforts are the most promising? Which labs do you back? Generally, you rely on experts. Who typically became experts by adhering to the conv
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by panabee 5mo ago
TLDR: gatekeepers stifled exploration and innovation. When a topic only has a limited number of experts, those experts become gatekeepers. Those gatekeepers directly or indirectly control research funding. Gatekeepers necessarily harbor bia
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by panabee 5mo ago
VCs are soccer stars, but founders play basketball. It’s easy to dunk on VCs, but the herd effect is rational after considering the typical VC’s background, the intense competition for good deals, and the job requirements — to prudently dep
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by panabee 1y ago
The association between pathogens and cancer is under-appreciated, mostly due to limitations in detection methods. For instance, it is not uncommon for cancer studies to design assays around non-oncogenic strains, or for assays to use prime
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by panabee 1y ago
A more accurate title: "Are Cornell Students Meritocratic and Efficiency-Seeking? Evidence from 271 MBA students and 67 Undergraduate Business Students." This topic is important and the study interesting, but the methods exhibit t
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by panabee 1y ago
Thanks. This is helpful. Looking forward to more of your thoughts. Some nuance: What happens when the methods are outdated/biased? We highlight a potential case in breast cancer in one of our papers. Worse, who decides? To reiterate, t
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by panabee 1y ago
Valid critique, but one addressing a problem above the ML layer at the human layer. :) That said, your comment has an implication: in which fields can we trust data if incentives are poor? For instance, many Alzheimer's papers were und
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by panabee 1y ago
If you agree that ML starts with philosophy, not statistics, this is but one example highlighting how biomedicine helps model development, LLMs included. Every fact is born an opinion. This challenge exists in most, if not all, spheres of l
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by panabee 1y ago
100% agreed. I also advise you not to read many cancer papers, particularly ones investigating viruses and cancer. You would be horrified. (To clarify: this is not the fault of scientists. This is a byproduct of a severely broken system wit
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by panabee 1y ago
To elaborate, errors go beyond data and reach into model design. Two simple examples: 1. Nucleotides are a form of tokenization and encode bias. They're not as raw as people assume. For example, classic FASTA treats modified and canoni
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by panabee 1y ago
This is long overdue for biomedicine. Even Google DeepMind's relabeled MedQA dataset, created for MedGemini in 2024, has flaws. Many healthcare datasets/benchmarks contain dirty data because accuracy incentives are absent and few
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by panabee 1y ago
The author is a respected voice in tech and a good proxy of investor mindset, but the LLM claims are wrong. They are not only unsupported by recent research trends and general patterns in ML and computing, but also by emerging developments
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by panabee 1y ago
More like alarming anecdote. :) Google did a wonderful job relabeling MedQA, a core benchmark, but even they missed some (e.g., question 448 in the test set remains wrong according to Stanford doctors). For ML, start with MedGemma. It'
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by panabee 1y ago
Thanks, but no one truly understands biomedicine, let alone biomedical ML. Feynman's quote -- "A scientist is never certain" -- is apt for biomedical ML. Context: imagine the human body as the most devilish operating system e
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by panabee 1y ago
Agreed. There is deep potential for ML in healthcare. We need more contributors advancing research in this space. One opportunity as people look around: many priors merit reconsideration. For instance, genomic data that may seem identical m
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by panabee 1y ago
Thank you both for an illuminating thread. Comments were concise, curious, and dense with information. Most notably, there was respectful disagreement and a levelheaded exchange of perspective.
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by panabee 1y ago
To provide more color on cancers caused by viruses, the World Health Organization (WHO) estimates that 9.9% of all cancers are attributable to viruses [1]. Cancers with established viral etiology or strong association with viruses include:
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mRNA-LM: full-length integrated SLM for mRNA analysis(academic.oup.com)
4 points by panabee 1y ago|0 comments

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