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Now that clip is incredibly unfair and completely misreads the relationship between Rogan and Lex. Lex does ask extraordinarily predictable questions, and is just incredibly naive at times, but I don't believe for a second that he isn't genuine. He's a very un-cynical and charitable guy. To my eyes Rogan gave him his favorite watch as a gesture of love to a friend he likes, it seems like Glennwald and Beattie are too old and cynical to even recognize a mental state of friendly-love. They're completely stumped by the gesture and have to interpret it through their bizzare worldview where everything is a political machination.
edit: okay after looking a bit more into his "MIT credentials", it does seem like Lex was some wannabe social climber dude. My opinion of him is dropped significantly
edit2: out of curiosity I went to read his phd thesis from Drexel University, since it's kind of related to my field. To be frank, his thesis is shit. He basically applied standard ML methods to the problem of identifying internet users from their click patterns and other info. His approach is basically what you'd immediately come up with once the problem of "use ML to identify users from browser data" presented itself, there isn't a non-trivial idea in there that I can see. I'm honestly somewhat baffled that you can get a PhD with a thesis like that...
I find that very hard to believe.
There are people who are as genuinely naive and full of wonder as he pretends to be.
They even may be smart.
But the way he's presenting himself makes it clear he's socially rather savvy, so that doesn't make any sense at all.
You can't have naive people who are networking as aggressively as he is. Unless, of course, the joke about Fridman not being an MIT AI researcher, but actually the MIT AI research is true.
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On trivial PhD.
Old professors and academics are often unaware of how easy it is to spin up a ML model using sk-learn or a Neural Network using pytorch or keras. They think the students are programming backpropagation from scratch. So a lot of ML-related research is a lot more trivial than it looks to non-programmers, this includes theoretical CS academics who don't program much. Some of my GitHub repos could be turned into conference papers.
I predict a lot a lot of careers will be built on kaggle level Jupyter notebooks before the system catches up to it.
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