PhD @ UW.
Visiting @ NYU & MSR
Alum @ Carnegie Mellon
Academic webpage: https://samemon.github.io
What did I find? Issues with the article, questionable behavior by the author, indexing problems, and AI's potential for streamlining predatory publishing.
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Source: r/MyBoyfriendIsAI
www.reddit.com/r/MyBoyfrien...
Source: r/MyBoyfriendIsAI
www.reddit.com/r/MyBoyfrien...
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Great piece by @row1.ca on unbundling research into modular knowledge! >
Shared standards are the missing layer for reusable, trustworthy science.
articles.continuousfoundation.org/articles/how...
Why modular science changes everything.
We unpack it here 👇
articles.continuousfoundation.org/articles/how...
Great piece by @row1.ca on unbundling research into modular knowledge! >
Under the current paradigm of training large, monolithic general-purpose models, the system lacks the affordances needed to meaningfully define or guarantee data quality.
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#Data #GenAI
This is a reminder that media doesn’t just inform us, it “trains” us. And unless we slow down, we can turn assumptions into identity without meaning to.
A great piece by Ahmed Albusaidi about stereotyping and the Middle East.
www.seattletimes.com/opinion/how-...
This is a reminder that media doesn’t just inform us, it “trains” us. And unless we slow down, we can turn assumptions into identity without meaning to.
A great piece by Ahmed Albusaidi about stereotyping and the Middle East.
www.seattletimes.com/opinion/how-...
It’s vibe coding, but for science.”
It’s vibe coding, but for science.”
Writing a paper has never been easier.
Clogging the scientific publishing pipeline has never been easier.
It took me 54 seconds to write up an experiment I did not actually conduct.
prism.openai.com
Writing a paper has never been easier.
Clogging the scientific publishing pipeline has never been easier.
It took me 54 seconds to write up an experiment I did not actually conduct.
prism.openai.com
Maybe mixture of experts? modularity? etc.
Maybe mixture of experts? modularity? etc.
Pretraining/data defines the model's core capabilities; it's conceptual knowledge base, semantics, etc.
Pretraining/data defines the model's core capabilities; it's conceptual knowledge base, semantics, etc.
It's not that data quality does not matter. It is that it is not sufficient. Not only is it not sufficient, but architecture is a bottleneck for data quality interventions to be prolific.
It's not that data quality does not matter. It is that it is not sufficient. Not only is it not sufficient, but architecture is a bottleneck for data quality interventions to be prolific.
So even if some data quality assurance takes place, broadly safety kind of lives in the stack, not the corpus.
So even if some data quality assurance takes place, broadly safety kind of lives in the stack, not the corpus.
And I guess current paradigm works (well?) with tightening the leash.
And I guess current paradigm works (well?) with tightening the leash.