A Guide to Lifelong Learning

A Guide to Lifelong Learning—With AI

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Thinking Like an AI

“The kind of computing these LLMs are doing, this kind of intelligence, is just different. Is that an existential risk? Well, I come back to the fact that we have no idea how the human brain works.” It’s easier to imagine catastrophic things than see the opportunities, he points out.

Jia added

Rare AI project that helps make creative works (high literature) accessible / reimagine the way books can be enjoyed (from a solitary, passive experience of becoming list in the fictional world of a book → having a reading buddy to collaborate with in digesting and making the books personally relevant)

Rare AI project that helps develop human creativity, on the prerequisite that the reader actively engages with the LLM through “marginilia” the way book lovers scribble books with notes.

From Notetaking to Neuralink

Contrary Research research.contrary.com

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These systems can be used to give LLMs a form of memory, searching past interactions or previously uploaded documents for useful information. This memory will work much like our own, in that thinking about one idea might unlock random connections to other ideas we thought we had forgotten. As context windows expand and more of our lives get digitiz... See more

Michael Taylor • When Guessing Isn’t Good Enough

Trevor Newberry added

The implication here is kind of what Sublime is attempting to build and operationalize for individuals.

In the work with LLMs lies a deeper question, that I have not been able to answer for myself properly so far. I can use an LLM to extract information, summarise, distill, but it feels like I am fooling myself. The work that I have done is not done by me. I am have not made any connections in my brain. It feels like we should not use them for intell

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What a modern LLM does during training is, essentially, very very quickly skim the textbook, the words just flying by , not spending much brain power on it.

Rather, when you or I read that math textbook, we read a couple pages slowly; then have an internal monologue about the material in our heads and talk about it with a few study-buddies; read an

SITUATIONAL AWARENESS - The Decade Ahead • I. From GPT-4 to AGI: Counting the OOMs

Max Beauroyre added

One of the common themes you see is that LLMs are great when you are operating in the gray zone at the "edge" of your knowledge or confidence. For example, translating a program to C to speed it up. I can code C but I'm really rusty. I can't remember precise function names, what to #include, arguments etc. But LLMs are awesome at that. So it accele... See more

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Nicolay Gerold added

On their own, large language models (LLMs) are, to a significant extent, Babel-like. Their latent space can output every possible combination of words. They are capable of creating genius-level sentences—and also false gibberish. And at this point in the lifecycle of this technology, the quality of the results you’re going to get is far higher when

Dan Shipper • GPT-4: A Copilot for the Mind

sari added