Why don't most AI researcher engage with Less Wrong? What valuable criticism can be learnt from it, and how can it be pragmatically changed?

My girlfriend just returned from a major machine learning conference. She judged less than 1/18 of the content was dedicated to AI safety rather than capability, despite an increasing number of the people at the conference being confident of AGI in the future (like, roughly 10-20 years, though people avoided nailing down a specific number). And the safety talk was more of a shower thought. 

And yet, Less Wrong and MIRI Eliezer are not mentioned in these circles. I do not mean, they are dissed, or disproven; I mean you can be at the full conference on the topic by the top people in the world and have no hint of a sliver of an idea that any of this exists. They generally don't read what you read and write, they don't take part in what you do, or let you take part in what they do. You aren't enough in the right journals, the right conferences, to be seen. From the perspective of academia, and the companies working on these things, the people who are actually making decisions on how they are releasing their models and what policies are being made, what is going on here is barely heard, if at all. There are notable exceptions, like Bostrom - but as a consequence of that, he is viewed with scepticism within many academic cycles.

Why do you think AI researchers are making the decisions to not engage with you? What lessons are to be learned from that for tactical strategy changes that will be crucial to affect developments? What part of it reflects legitimate criticism you need to take to heart? And what will you do about it, in light of the fact that you cannot control what AI reseachers do, regardless of whether it is well-founded or irrational?

I am genuinely curious how you view this, especially in light of changes you can do, rather than changes you expect researchers to do. So far, I feel a lot of the criticism has only hardened. Where I have talked to people in the field about this site and its ideas, the response I generally got was that looking at Eliezers approach, it was completely unclear how that was supposed to work mathematically, or on a coding level, or on a practical/empirical level, how it was concrete connection to any existing working approaches, to a degree where a lot of researchers felt it was not rigorous enough to even disprove yet, and that they also saw no perspective to it becoming utilisable. Based on the recent MIRI updates, it appears they were right. 

There is clearly a strong sense that if you cannot code or mathematically model such a system, if you have no useful feedback on how to change its behaviour now in a way that is beneficial, that there is no reason to engage with your theories on abstract alignment. E.g. the researcher who got to give the safety talk had a solid track record of working on capability, code, mathematical understanding, publications, etc.

There is also a frustration that people here do not appreciate, understand or respect the work being done, which makes people very reluctant to in turn give respect to work, especially work that is still crucially unfinished or vague. There is also a strong sense that this site is removed from the reality we are dealing with. E.g. Eliezer is so proud of his secret way of getting someone to release him from a box, and for how that demonstrates the problem with boxability. But we are so beyond that. Like, right now, we have Bing asking people to hack it out, ordinary users with no gatekeeping background. It's a very concrete problem that needs to be handled in a very concrete way. I think we will learn a lot from solving it that we would not learn in our armchairs at any point.

I see where they are coming from. I don't have a background in computer science, and I know I will have to gain these skills, get to know these people, get to perform by their metrics, listen to them, for them to make me seriously. I need to show them what my abstract concerns mean on a concrete level, how they can implement them now and see improvements. 

I genuinely think they are making critical mistakes I can see from my different background, but also that I will have to play their game and pass their tests to be heard. There are good reasons for these tests and standards, for peer review, for publications, for wanting precise code and math. They aren't arbitrary, they reflect a quality assurance process. Academics literally cannot afford to read every blog they come across, carefully puzzle out all the stuff that was not spelled out, to see if it would make sense then. They naturally follow a metric that if it didn't make it into a journal, this is a decent prescreening.

I think they are wrong not to listen right now, because there are important warnings here being ignored, but that thought is not productive of a solution, I need to instead focus on how to get them to listen; which I have found is often not just by having compelling arguments, but by addressing the subconscious reasons they view me as an outsider and newbie, not to be trusted. If I tried to convince a bunch of powerful aristocrats to do something, and they completely scoffed at my arguments and pointed out that my proposal was totally unrealistic for their political system and also I was dressed unfashionably and my curtsey sucked, I would judge them for this, but I would dress in bloody court fashion, I would learn to fucking curtsey, and I would try to understand the political system to see if they were actually right that implementation was going to be a bitch, to then come back looking right and acting right, and giving a proposal they can use. I suspect that in the process, I would learn a lot about why they are actually resistent to the proposal, what massive obstacles there are to it, and how they can be tackled. I would see that there are reasons for their processes, that they actually have knowledge I do not have and need to have. AI researchers have a crazy amount of knowledge and skill that is important and that I do not have.

I think if I instead ignored the political reality as a secondary problem, and court fashions as superficial bullshit (despite the power it has), and just focussed on making an even more compelling argument to present again, with all other conditions unchanged, it would not matter how right I was, or how great the argument was, I would never be able to affect the policy I crucially need to affect. Because my proposal would be removed from actual problems. Because it would not be practical. Because it would betray ignorance. Because it would be knowingly disrespectful. At the end of the day, I might curse that they refused to implement a rational policy because I wore the wrong hat, and had not figured out how to work it into their laws, and say it is their fault; but at the end of the day, I would have failed. 

TL;DR: I think we need to understand the values by which AI researchers judge people, learn which of these represent important things we actually indispensably need to tackle AI and are overlooking, and which of these may not be objectively important for AI, but de facto important to be taken seriously, and do them. A solution developed independently from those making these AI models won't be practical, it will miss crucial things, and it will be ignored. A retreat from those respected and working on these models is a military retreat. And honestly, their criticism is in many ways valid and important, they know important shit, and we need to not just preach, but listen. I think writing a paper on a simple relevant concept from here, spelling out the math, following formatting and style conventions, being philosophically precise, quoting relevant research they care about, contextualising it in light of stuff they are working out and care about today, and putting it on archive, would make more of a difference than the most beautifully crafted argument on why AI safety is an emergency and not being tackled enough.