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Twishmay
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Twishmay
@twishmay
Founder @PsyTech_AI | MBA @Wharton | Engineer @iitbombay | Music, magic, & physics
🌏 Katılım Kasım 2019
5.2K Takip Edilen1.5K Takipçiler
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There's a broadly held misconception in AI that methods that scale well are simple methods -- even, that simple methods usually scale. This is completely wrong.
Pretty much none of the truly simple methods in ML scale well. SVM, kNN, random forests are some of the simplest methods out there, and they don't scale at all. Meanwhile "train a transformer via backprop and gradient descent" is a very high-entropy method, easily 10x more complex than random forest fitting. But it scales very well.
Further, given a simple method that doesn't scale, it is usually the case that you alter it to make it scale by adding a lot of complication. For instance, take a simple a simple combinatorial search-based method (not scalable at all) -- you can make it scale by adding deep learning guidance (which blows up complexity). Scalability usually belongs to high-entropy, complex systems.
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Running a company:
2020: can you survive a pandemic?
2021: still here? we’re going to give all of your competitors $100m series A rounds.
2022: wow, you made it? okay, all engineers cost $600,000/year now.
2023: nice job! okay, SVB failed and we’re going to take away your bank account.
2024: a survivor I see. but can you pivot from ai to crypto to defense tech back to ai-enabled defense tech in a 12 month period to stay relevant?
2025: unfortunately all of your competitors have raised $2b series B rounds. oh and only 500 engineers are relevant and they cost $100m/yr each.
2026: well, well, well. you’re still in business? let’s deploy the thunderclap of godlike LLMs from the heavens so all of your customers can rebuild your app in 2 hours. can you survive?
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The reason symmetry is so important in physics is because symmetry is a highly effective compression operator. If a system is invariant under some symmetry, you only need to explain one axis of it. Scientific models represent the systematic exploitation of the universe's internal redundancies through symbolic logic.
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The appearance of stone tools in the archaeological record is often treated as the moment “we” arrived.
They offer a clear boundary between “clever humans” and the animals around us. But what if that assumption is wrong?
A recent archaeological find at Lomekwi in Kenya suggests that the first stone tools weren’t made by humans at all, but by small-brained, long-extinct hominins who lived more than three million years ago.
Who exactly were these mysterious toolmakers, and how does the discovery of their creations change the story of our evolution?
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[:2026]
"Survival hinges on how you respond. It’s no longer whether you can build—it’s whether you know what to build, and why, before the window closes."
blog.southparkcommons.com/p/avoiding-the…
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