LessWrong 2.0 Reader

Thank you!

The Planecrash audiobook is great, and I would not have read it if it were not for the audio version.

I consumed edible cannabis for the first time a few months ago, and it felt very similar to the experience you're describing. I felt regularly surprised at where I was, and had lots of trouble remembering more than the last 30 seconds of the conversation. 

The most troubling experience was listening to someone telling me something, me replying, and while saying the reply, forgetting where I was, what I was replying to and what I already said. The weirdest part is that at this point I would finish the reply in a sort of disconnected state, not knowing where the words were coming from, and at the end I would have a feeling of "I said what I wanted to say", even though I could not remember a word of it.

I don't think our chances of survival will increase if LessWrong becomes substantially more risk-averse about publishing research and musings about AI. I think they will decrease.

Are you absolutely sure about that? The large-scale part, sure, but I suspect it's possible to do good skincare science on a budget. The thing that makes me pause is that Chemist Confessions (I know I keep referencing them, but they’re just so cool) did a clinical study of an eye cream that they formulated, which contains encapsulated retinol (usually considered to be too harsh to apply around the eye area) and their experimental design is *chef’s kiss*. And they’re super small and indie!

I actually love that they published their results in the form of an approachable blog post. It reminds me that I don’t have to sound “academic” or write a full-on paper in order to publish my own research. :D

They also run a testing program for experiments that people can do remotely, and it looks like it’s focused on stuff where qualitative feedback is appropriate (e.g., opinions on texture).

Very helpful, thank you for the extra detail!

I think I have two disagreements with your assessment. 

First, the probability of a random independent AI researcher or hobbyist discovering a neat hack to make AI training cheaper and taking over. GPT4 took 100M$ to train and is not enough to go FOOM. To train the same thing within the budget of the median hobbyist would require algorithmic advantages of three or four orders of magnitude. 

Historically, significant progress has been made by hobbyists and early pioneers, but mostly in areas which were not under intense scrutiny by established academia. Often,  the main achievement of a pioneer is discovering a new field, them picking all the low-hanging fruits is more of a bonus. If you had paid a thousand mathematicians to think about signal transmission on a telegraph wire or semaphore tower, they probably would have discovered Shannon entropy. Shannon's genius was to some degree looking into things nobody else was looking into which later blew up into a big field. 

It is common knowledge that machine learning is a booming field. Experts from every field of mathematics have probably thought if there is a way to apply their insights to ML. While there are certainly still discoveries to be made, all the low-hanging fruits have been picked. If a hobbyist manages to build the first ASI, that would likely be because they discover a completely new paradigm -- perhaps beyond NNs. The risk that a hobbyist discovers a concept which lets them use their gaming GPU to train an AGI does not seem that much higher than in 2018 -- either would be completely out of the left field. 

My second disagreement is the probability of an ASI being roughly aligned with human values, or to be more precise, the difference of that probability conditional on who discovers it. The median independent AI enthusiast is not a total asshole [citation needed], so if alignment is easy and they discover ASI, chances are that they will be satisfied with becoming the eternal god emperor of our light cone and not bother to tell their ASI to turn any any huge number of humans to fine red mist. This outcome will not be so different than if Facebook develops an aligned ASI first. If alignment is hard -- which we have some reason to believe it is -- then the hobbyist who builds ASI by accident will doom the world, but I am also rather cynical about the odds of big tech having much better odds. 

Going full steam ahead is useful if (a) the odds of a hobbyist building ASI if big tech stops capability research are significant and (b) alignment is very likely for big tech and unlikely for the hobbyist. I do not think either one is true. 

To be honest, I downplayed much of my situation in my post. After all, it is my first post. I have contributed nothing to this community so far. I feel it's irresponsible, unfair, and even sort of entitled to hose people down with the full weight of my very negative and emotionally draining circumstances as an introductory post. It's not charming. I kept thinking, while writing this post, "do I really want this to be the first thing I say to these people?" I respect this community more than any other one and understand that it can only absorb so many posts that essentially "take" (or seek to take) more than they offer before the quality standards that set LW apart begin to suffer. I feel a sense of duty to be an interesting conversationalist and add value here.

I posted anyway for a couple reasons. For one thing, I have read LW for years. It nourishes something in me that I don't get anywhere else in my life - the inquisitive, slow-thinking, human-oriented, analytical, diplomatic part of me that I am constantly forced by my life circumstances to suppress in view of the fact that these qualities are considered unfortunate in the world I inhabit.

So, in other words, I feel very strongly that I "belong" here and can therefore justify asking the community to afford me the luxury of venting and soliciting its assistance without tweaking people's noses too much. I feel like I can signal in-group membership and long-standing familiarity with LW strongly enough to justify doing this, because it really is my respect for this community's collective excellence that sets it apart for me as the ideal place to seek advice and insight.

I also figure this could be useful to someone else at some point, so it does have value in that sense.

But yes, you're right. There's more to my circumstances. Of course there is. They're much worse than I can justify disclosing.

Reaching out to startups on sites like ycombinator is a great idea. It never occurred to me to market to them. Thank you for suggesting it. Your consideration means a lot to me.

If you can get a salesforce cert, you can get any of the other baseline IT certs.  Being a female and being native is actually massive for hiring at companies that care about that stuff.

Apply for government IT jobs, help desk type stuff, a lot of it is hybrid or remote, if it's a hybrid position, ask to be remote for the first month (two paychecks) to manage moving.  

Six months in, open a business, ask your company to switch you to 1099, route the job through your business, work it for another year, this creates a performance history. 

Now you are a poor, native american woman owned small business, and you can apply for 8A set aside contracts as the prime.  This allows you to take 10% or so off the top when teaming with a large company that will actually staff the thing.  Grow to around 40 employees, sell for 5-10mil in 15 years.

It's not honest work, but lots of people have done this.  If you think I'm full of it, I literally worked on a contract where one of the companies on the winning team was called "Native American Woman".  

Good luck.

Please feel free to elaborate on the specific qualms you have with what's written! Super happy to retract anything necessary and learn!

Haskell is a beautiful language, but in my admittedly limited experience it's been quite hard to reason about memory usage in deployed software (which is important because programs run on physical hardware. No matter how beautiful your abstract machine, you will run into issues where the assumptions that abstraction makes don't match reality).

That's not to say more robust programming languages aren't possible. IMO rust is quite nice, and easily interoperable with a lot of existing code, which is probably a major factor in why it's seeing much higher adoption.

But to echo and build off what @ustice said earlier [LW(p) · GW(p)]:

The hard part of programming isn't writing a program that transforms simple inputs with fully known properties into simple outputs that are meet some known requirement. The hard parts are finding or creating a mostly-non-leaky abstraction that maps well onto your inputs, and determining what precise machine-interpretable rules produce outputs that look like the ones you want.

Most bugs I've seen come at the boundaries of the system, where it turns out that one of your assumptions about your inputs was wrong, or that one of your assumptions about how your outputs will be used was wrong.

I almost never see bugs like this

• My sort(list, comparison_fn) function fails to correctly sort the list"
• My graph traversal algorithm skips nodes it should have hit
• My pick_winning_poker_hand() function doesn't always recognize straights

Instead, I usually see stuff like

• My program assumes that when the server receives an order_received webhook, and then hits the server to fetch the order details from the vendor's API for the order identified in the webhook payload, the vendor's API will return the order details and not a 404 not found"
• My server returns nothing at all when fetching the user's bill for this month, because while the logic is correct (determine the amount due for each order and sum), this particular user had 350,000 individual orders this month so the endpoint takes >30 seconds, times out, and returns nothing.
• The program takes satellite images along with Metadata that includes the exact timesamp, which satellite took the picture, and how the satellite was angles. It identifies locations which match a specific feature, and spits out a latitude, longitude, label, and confidence score. However, when viewing the locations on a map, they appear to be 100-700 meters off, but only for points within the borders of China (because the programmer didn't know about GCJ-02)

Programming languages that help you write code that is "correct" mostly help prevent the first type of bug, not the second.