> You have to remember EVERYTHING. Only then you can perform the cognitive tasks necessary to perform meaningful knowledge work. You don't have to remember everything. You have to remember enough entry points and the shape of what follows, trained through experience and going through the process of thinking and writing, to reason your way through meaningful knowledge work.
A bit like the memory palace. One memory leads to another. Not random-access.
You only need the initial seed to restore the full state, provided you can reason your way from there. If you haven't applied yourself to problem solving, then perhaps you might need to memorize the full state.
A great deal of anti-AI posts as of late seem like milquetoast pearl clutching to me. They don’t want to outright say they feel threatened/devalued but the arguments they put forward are not only unconvincing, but in this case among many others actively work against them. nb. I tried really hard to not point out the smugness of Zettelkasten which I suspect emboldens this feeling of superiority, because I’d rather sit…
I am sympathetic to memory-focused tools like Anki and Zettelkasten (haven't used the latter myself, though) but I think this post is a bit oversimplified. I think there are at least two models of work that require knowledge: 1. Work when you need to be able to refer to everything instantly. I don't know if this is actually necessary for most scenarios other than live debates, or some form of hyper-productivity in wh…
The AI can also give you pretty good examples of "kind" that you can then evaluate. I've had it find companies that "do X" and then used those companies to understand enough about what I am or am not looking for to research it myself using a search engine. The last time I did this I didn't end up surfacing any of what the AI provided. It's more like talking to the guy in the next cubicle, hearing some suggestions from them, and using those suggestions to form my own opinion about what's important and digging in on that. You do still have to do the work of forming an opinion. The ML model is just much better at recognizing relationships between different words and between features of a category of statements, and in my case they were statements that companies in a particular field tended to make on their websites.
The US is, however, learning exactly what happens when rationality is not part of the equation. This is all a dance around what is a "fact" and how to string facts into a reasoning model that lets you predict or confirm other potential facts, etc... It's simply different people we're talking about. Certain personalities are always going to gravitate to the "search for reason" model in life rather than "reason about f…
At least in the field of sports science and exercise physiology, we have very little in the way of facts. Much of what we once thought were facts have been disproven or at least called into question by later research. So we need to be humble, and very circumspect in what we label as a "fact".
Do you have any go-to examples of “facts” that were disproven in this field?
At my first software dev internship my manager asked me to code in languages I was not trained in. I told him I would need some time to study up on these. He scoffed and said just look up what you need on Google. Initially I resisted, I felt like it was too shallow. That it was akin to copying answers I didn't really comprehend. However it didn't take long to pick up the habit. Learning is like going to the gym now, it's self enforced discipline
I was talking with somebody about their migration recently [0], and we got to speculating about AI and how it might have helped. There were basically 2 paths: - Use the AI and ask for answers. It'll generate something! It'll also be pleasant, because it'll replace the thinking you were planning on doing. - Use the AI to automate away the dumb stuff, like writing a bespoke test suite or new infra to run those tests. I…
I’ve tried the second path at work and it’s grueling.
“Almost certainly succeed” requires that you mostly plan out the implementation for it, and then monitor the LLM to ensure that it doesn’t get off track and do something awful. It’s hard to get much other work done in the meantime.
I feel like I’m unlocking, like, 10% or 20% productivity gains. Maybe.
The US is, however, learning exactly what happens when rationality is not part of the equation. This is all a dance around what is a "fact" and how to string facts into a reasoning model that lets you predict or confirm other potential facts, etc... It's simply different people we're talking about. Certain personalities are always going to gravitate to the "search for reason" model in life rather than "reason about f…
At least in the field of sports science and exercise physiology, we have very little in the way of facts. Much of what we once thought were facts have been disproven or at least called into question by later research. So we need to be humble, and very circumspect in what we label as a "fact".
Like what? There are some basics that have been studied and represent useful approximations. The general statement that your body "makes specific adaptations to imposed demands" seems to hold no matter what you do. There seems to always be some debate about what demands to impose to get a specific adaptation. For example, people have a very diverse array of opinions about how and when to stretch to achieve certain kinds of flexibility and if you do some reading you will find that these opinions follow from a body of work in a specific activity and that they don't always translate well to other activities.
> You have to remember EVERYTHING. Only then you can perform the cognitive tasks necessary to perform meaningful knowledge work. You don't have to remember everything. You have to remember enough entry points and the shape of what follows, trained through experience and going through the process of thinking and writing, to reason your way through meaningful knowledge work.
Actually this is how LLMs (with reasoning) work as well. There is the pre-training which is analogous to the human brain getting trained by as much information as possible. There is a "yet unknown" threshold of what is enough pre-training and then the models can start reasoning and use tools and the feedback from it to do something that resembles to human thinking and reasoning. So if we don't pre-train our brains with enough information, we will have a weak base model. Again this is of course more of an analogy as we yet don't know how our brains really work but more and more it is looking remarkably aligned with this hypothesis.
TLDR: firing and forgetting the first result off of a single google search isn't the best long term approach. But this guy had a bri..coaching to sell you that apparently makes you a better human being
> But this guy had a bri..coaching to sell you that apparently makes you a better human being
To the credit of the author and this individual post, you would have to go out of your way to come to that conclusion. That is, there isn’t anything present in the actual article to suggest that what you’re alleging is valid.